Interactive digital content production system based on Transform architecture

Abstract

The invention relates to the technical field of digital content production, and discloses an interactive digital content production system based on a Transform architecture. According to the system, text, image and audio data of original digital content are acquired through a content feature extraction module, cross-modal feature alignment is performed by using a multi-head attention mechanism, and content feature tensors with space-time relevance are generated; the dynamic weight distribution module calculates relative importance scores of different modal features based on the tensor, and adopts a gating mechanism to perform dynamic weight fusion to form content semantic enhancement representation; the interaction intention analysis module performs space-time coding matching on the enhanced representation and the user operation instruction stream, and analyzes an intention distribution matrix of user operation on the content dimension; a hierarchical decoding generation module constructs a multi-scale content generation path in a Transform decoder according to the intention distribution matrix; and the real-time rendering engine module loads implicit representation output by the path, so that efficient and intelligent digital content creation is realized.

Description

Technical Field

[0001] This invention relates to the field of digital content creation technology, specifically to an interactive digital content creation system based on the Transformer architecture. Background Technology

[0002] Digital content production is a core technological aspect of film, games, virtual reality, and other fields. Traditional digital content production workflows typically employ a linear workflow, involving multiple independent stages such as modeling, texturing, animation, and rendering. Each stage relies on specialized software and manual operation, resulting in low efficiency, long iteration cycles, and limited creative flexibility. With the development of multimedia technology, digital content is exhibiting a trend towards multimodal fusion; single-type text, images, or audio are no longer sufficient to meet the demands of immersive experiences. However, existing production tools lack the ability to collaboratively process multimodal data, often requiring separate processing of different modalities before post-production compositing. This leads to difficulties in ensuring content consistency and an inability to respond to user interactions in real time.

[0003] At the technical architecture level, traditional digital content production systems are mostly based on fixed pipeline rendering or pre-computed lighting patterns, lacking the ability to adapt to dynamic interactions in real time. When users interact with content, the system struggles to dynamically adjust the content presentation based on the user's intent. Existing interaction processing largely remains at the level of simple trigger-response mechanisms, such as click events corresponding to preset animation playback, failing to understand the deeper semantic intent of user actions and thus unable to generate coherent and reasonable content changes that match that intent. Furthermore, traditional systems often employ shallow methods such as simple splicing or weighted averaging to fuse multimodal data, failing to fully explore the deep semantic relationships between text, images, and audio, resulting in a lack of internal logical consistency in the generated content.

[0004] Deep learning technology has made significant progress, particularly in natural language processing and computer vision. The Transformer architecture, with its powerful sequence modeling and attention mechanisms, excels in tasks such as machine translation and text generation. Neural rendering technologies, such as neural radiation fields, have also provided new avenues for high-quality 3D scene reconstruction. However, organically integrating these technologies and applying them to interactive digital content creation still faces many challenges: how to achieve effective alignment and deep fusion of multimodal data; how to accurately interpret user interaction intentions and map them to the content generation space; and how to achieve real-time generation and rendering of high-quality content. Current technologies have not yet formed a complete technological chain covering multimodal input, intent parsing, and real-time generation and rendering. Summary of the Invention

[0005] The purpose of this invention is to provide an interactive digital content creation system based on the Transformer architecture to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an interactive digital content creation system based on the Transformer architecture, the system comprising:

[0007] The content feature extraction module acquires multimodal input data of the original digital content, and performs cross-modal feature alignment on text, image and audio data through a multi-head attention mechanism to generate a content feature tensor with spatiotemporal correlation;

[0008] The dynamic weight allocation module calculates the relative importance scores of different modal features at their sequence positions based on the content feature tensor, and uses a gating mechanism to dynamically weight and fuse feature channels to form a content semantic enhancement representation.

[0009] The interaction intent parsing module receives the user operation command stream, performs spatiotemporal encoding matching between the content semantic enhancement representation and the command stream, and parses out the user operation intent distribution matrix in the content dimension.

[0010] The layered decoding generation module constructs a multi-scale content generation path in the Transformer decoder based on the intent distribution matrix, and maps the intent distribution to content generation spaces of different resolutions through a cross-attention layer;

[0011] The real-time rendering engine module loads the implicit representation of the multi-scale content generation path output, uses neural radiation field technology to perform differential rendering on the three-dimensional scene parameters, and generates an interactive dynamic digital content stream.

[0012] Preferably, the content feature extraction module includes:

[0013] The cross-modal alignment submodule performs multi-head attention computation on text word vectors, image patch embeddings, and audio spectral features, establishes an inter-modal feature similarity matrix, and fuses cross-modal contextual information through residual connections;

[0014] The spatiotemporal correlation modeling submodule applies a relative position bias to the feature similarity matrix to capture the long-term dependencies of different modal features in the time axis and spatial domain, and outputs a content feature tensor with spatiotemporal labels.

[0015] Preferably, the dynamic weight allocation module includes:

[0016] The channel importance assessment submodule calculates the global average pooling value along the channel dimension for the content feature tensor and generates the initial weight coefficients for each channel through the learnable parameter matrix.

[0017] The gated fusion submodule inputs the initial weight coefficients into the sigmoid activation function, multiplies them element-wise with the original features, and then performs layer normalization to obtain the content semantic enhancement representation.

[0018] Preferably, the interaction intent parsing module includes:

[0019] The instruction encoding submodule performs hierarchical position encoding on the user operation instruction stream to generate an instruction embedding sequence with timestamps;

[0020] The intent matching submodule calculates the cross-attention weights between the instruction embedding sequence and the content semantic enhancement representation, and outputs a probability matrix reflecting the distribution of the operation intent in the content space.

[0021] Preferably, the layered decoding generation module includes:

[0022] The multi-scale path construction submodule sets up content generation heads of different dimensions in each layer of the decoder, and decomposes the intent distribution matrix into the corresponding generation heads according to the resolution level.

[0023] The implicit representation synthesis submodule aggregates the output features of each generator head through an upsampled convolutional network to form a content implicit representation sequence with multi-level details.

[0024] Preferably, the multi-scale path construction submodule includes:

[0025] The resolution grading unit divides the decoder network into a low-level structure generation path, a mid-level material generation path, and a high-level detail generation path.

[0026] The attention routing unit dynamically allocates attention query vectors for each generated path based on the energy value of the intent distribution matrix.

[0027] Preferably, the real-time rendering engine module includes:

[0028] The neural radiation field construction submodule maps the implicit representation sequence of the content to density field and color field functions of the three-dimensional scene;

[0029] The ray differential calculation submodule performs differentiable volume rendering integration on the ray sampling points under the observation viewpoint to generate a pixel-level dynamic content frame sequence.

[0030] Preferably, the neural radiation field construction submodule includes:

[0031] The field function parameterization unit uses a multilayer perceptron to decode the implicit representation into the density and color attributes of spatial points;

[0032] The dynamic field update unit adjusts the weight parameter distribution of the multilayer perceptron in real time according to the user interaction command stream.

[0033] Preferably, the system further includes:

[0034] The version control module records the parameter change trajectory of the dynamic digital content stream on the timeline and establishes a content version tree based on operation instructions;

[0035] The collaborative editing module performs conflict detection and version merging on the operation command streams of multiple users, generating a state snapshot of the shared content editing space.

[0036] Preferably, the version control module includes:

[0037] The differential encoding submodule performs delta compression encoding on the changes in content parameters at consecutive time steps;

[0038] The version branch management submodule creates independent branch nodes on the content version tree based on the source identifier of the user's operation command.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention achieves cross-modal feature alignment of multimodal data through a multi-head attention mechanism, effectively capturing the inherent correlations between different modalities such as text, images, and audio. Traditional methods often suffer from semantic fragmentation when processing multimodal data. The content feature tensor generated by this technology has spatiotemporal correlation, laying a solid foundation for semantic consistency in subsequent content generation.

[0041] The dynamic weight allocation module employs a gating mechanism to achieve dynamic weighted fusion of feature channels, adaptively adjusting the contribution of each modality feature based on the content context. This dynamic fusion strategy avoids the limitations of fixed weight allocation, enabling the system to flexibly respond to different content types and interaction scenarios, resulting in more expressive and adaptable semantically enhanced representations of the generated content.

[0042] The interaction intent parsing module deeply understands user operation intent through spatiotemporal coding matching. This module not only parses the surface instructions of the operation, but also generates a distribution matrix reflecting the user's deep creative intent by analyzing the spatiotemporal relationship between the operation and the semantics of the content. This enables the content generation process to accurately respond to user needs and improve the naturalness and effectiveness of the interaction.

[0043] The layered decoding generation module constructs a multi-scale content generation path, achieving a fine-grained mapping from intent to content. By using a cross-attention mechanism to map the intent distribution to content spaces of different resolutions, the system can simultaneously generate the overall structure and detailed features of the content, ensuring that the generated results conform to the user's intent in both macro-layout and micro-representation, thereby improving the completeness and quality of the content.

[0044] The real-time rendering engine module, combined with neural radiation field technology, enables the real-time generation of high-quality dynamic content. This technology can perform differential rendering of 3D scene parameters, generating realistic and coherent dynamic content streams. This ensures that user interactions receive immediate and smooth visual feedback, significantly enhancing the immersiveness and real-time nature of the interactive experience. This system provides efficient and intelligent technical support for the creation of next-generation interactive digital content. Attached Figure Description

[0045] Figure 1 A graph for multimodal content generation and dynamic evolution;

[0046] Figure 2 A flowchart of the content feature extraction module;

[0047] Figure 3 Workflow diagram for the dynamic weight allocation module;

[0048] Figure 4 This is a layered decoding and multi-scale composite image. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1This invention provides an interactive digital content creation system based on the Transformer architecture. The system integrates multiple dedicated modules to achieve end-to-end generation of interactive dynamic content from multimodal input. A content feature extraction module acquires multimodal input data of the original digital content, including text, images, and audio. The module utilizes a multi-head attention mechanism to align cross-modal features of this data. By calculating intermodal feature similarity and fusing contextual information, a content feature tensor with spatiotemporal correlation is generated. This content feature tensor captures the long-term dependencies between different modal data in the temporal and spatial dimensions. A dynamic weight allocation module receives the content feature tensor as input. By calculating the relative importance scores of different modal features at their sequence positions, a gating mechanism is used to dynamically weight and fuse feature channels, thereby forming a semantically enhanced representation of the content. This process involves the application of global average pooling and learnable parameter matrices to optimize the signal-to-noise ratio of the feature representation. The interaction intent parsing module processes the user operation command stream, performing spatiotemporal encoding matching between the content semantic enhancement representation and the command stream. Through hierarchical positional encoding and cross-attention weight calculation, it parses the user operation intent distribution matrix in the content dimension. The intent distribution matrix reflects the spatial distribution of operation intent in probabilistic form. The hierarchical decoding and generation module constructs a multi-scale content generation path in the Transformer decoder based on the intent distribution matrix. By setting generation heads of different dimensions and decomposing the intent distribution to corresponding resolution levels, it uses a cross-attention layer to map the intent to content generation spaces of different resolutions. Finally, it aggregates features through an upsampling convolutional network to output a multi-level detailed implicit content representation sequence. The real-time rendering engine module loads the implicit content representation sequence, uses neural radiation field technology to map the implicit representation to the density field and color field functions of the 3D scene, and performs differentiable volume rendering integrals through ray differentiation calculation to generate a pixel-level dynamic content stream, supporting real-time interaction and rendering updates.

[0051] Example 1: See Figure 2The content feature extraction module is responsible for processing the multimodal input data of the original digital content and generating content feature tensors with spatiotemporal correlation. The cross-modal alignment submodule first performs embedding preprocessing on text word vectors, image block embeddings, and audio spectral features. Text data is transformed into word vector sequences through a pre-trained language model, image data is segmented into fixed-size blocks and block embedding vectors are extracted through a convolutional neural network, and audio data is converted into a spectrogram through short-time Fourier transform and encoded into feature vectors. The feature vectors of the three modalities are projected onto a unified latent space dimension to enable cross-modal interaction. The cross-modal alignment submodule uses a multi-head attention mechanism to calculate the feature similarity matrix between modalities. Each attention head independently processes the feature subspace of a different modality. The query vector comes from one modality, while the key-value pair comes from another modality. Attention scores are calculated through dot product operations to generate a weight matrix. After the weight matrix is ​​normalized by softmax, the value vectors are weighted and summed to output a feature representation that incorporates cross-modal contextual information. The residual connection operation adds the original features to the attention output, preserving the original information while enhancing the feature expressive power. The cross-modal alignment submodule may perform multiple rounds of attention computation to fully capture complex intermodal relationships, such as the correspondence between text descriptions and image regions or the synchronicity between audio events and visual changes.

[0052] The spatiotemporal correlation modeling submodule introduces a relative position bias on top of the feature similarity matrix after cross-modal alignment. This relative position bias is implemented through a trainable parameter matrix, which encodes the frame interval relationship of feature points on the time axis and their relative coordinate offset in the spatial domain. During attention calculation, each feature point dynamically adjusts its attention weight based on its relative position to other points, with closer feature points receiving higher weights and farther feature points experiencing weight decay, thus modeling local and global dependencies. The spatiotemporal correlation modeling submodule stacks multiple Transformer encoding layers to further deepen feature processing. Each layer includes a self-attention mechanism and a feedforward neural network. The self-attention mechanism captures intra-sequence dependencies, while the feedforward neural network enhances feature representation capabilities through nonlinear transformations. Layer normalization is applied to the output of each sublayer to stabilize the training process, residual connections ensure effective gradient propagation, and dropout prevents overfitting. The final output feature tensor of the spatiotemporal correlation modeling submodule has a clear spatiotemporal label. The tensor dimensions correspond to the batch size, sequence length, and feature dimension, with the sequence length encompassing the time step or the number of spatial blocks in the multimodal data. The feature vector at each position of the content feature tensor incorporates multimodal information and encodes the spatiotemporal context, providing structured input for the subsequent dynamic weight allocation module.

[0053] The cross-modal alignment submodule and the spatiotemporal correlation modeling submodule work together to form a complete feature extraction pipeline. Multimodal data achieves semantic consistency after cross-modal alignment, and long-term dependencies are established through spatiotemporal correlation modeling. The entire content feature extraction module supports end-to-end training, with gradient backpropagation optimizing all parameters, including multi-head attention weights, position bias parameters, and projection matrices. The computational efficiency of the content feature extraction module is achieved through parallelization of the attention mechanism, making it suitable for real-time interactive scenarios. The accuracy of multimodal feature alignment directly affects the quality of subsequent content generation. During the implementation of the content feature extraction module, it is necessary to balance computational complexity and feature richness. Hyperparameters such as the number of attention heads, hidden layer dimensions, and sequence length are adjusted according to specific application scenarios. The scalability of the content feature extraction module allows the integration of more modal inputs, such as video sequences or sensor data, simply by adding modality-specific encoders and cross-attention heads accordingly. The content feature extraction module outputs a content feature tensor as the core intermediate representation of the system, carrying the semantic essence and spatiotemporal structural information of the digital content. Specifically, the content feature extraction module performs multi-head attention computation on text word vectors, image patch embeddings, and audio spectral features through the cross-modal alignment submodule, establishing an inter-modal feature similarity matrix. It then utilizes residual connections to fuse cross-modal contextual information, thereby capturing the deep semantic relationships between text, image, and audio data. This ensures that the generated content feature tensor can carry the semantic essence of the digital content. The spatiotemporal correlation modeling submodule applies a relative position bias to the feature similarity matrix, capturing the long-term dependencies of different modal features in the time and spatial domains. This encodes the temporal order and spatial layout information of the digital content into a tensor, forming a spatiotemporally labeled content feature tensor. This tensor, as the core intermediate representation of the system, integrates the semantic and spatiotemporal characteristics of multimodal data, providing a unified feature foundation for the subsequent dynamic weight allocation module and interactive intent parsing module, ensuring consistency and coherence in the digital content generation process.

[0054] Example 2: See Figure 3The dynamic weight allocation module is responsible for weighting and optimizing the content feature tensor output by the content feature extraction module. This module comprises two core components: a channel importance evaluation submodule and a gated fusion submodule. The channel importance evaluation submodule performs statistical analysis on the channel dimension of the content feature tensor. The content feature tensor has a three-dimensional structure corresponding to batch size, sequence length, and the number of feature channels. The channel importance evaluation submodule performs global average pooling on all sequence positions of each feature channel, compressing the two-dimensional feature map of each channel into a single scalar value. These pooled values ​​form a channel statistical vector, reflecting the activity level of each channel in the global context. This channel statistical vector is then input into a fully connected layer for linear transformation. The fully connected layer contains a learnable parameter matrix, which is optimized during training to capture the correlation between channels. The fully connected layer outputs unnormalized initial weight coefficients, which maintain the relative importance of each channel. The gated fusion submodule receives the initial weight coefficients and performs numerical normalization. The sigmoid activation function maps the weight coefficients to the 0-1 interval, generating a gated weight vector. The gated weight vector is multiplied channel-by-channel by the original content feature tensor. The feature matrix of each channel is multiplied by the corresponding gated weight, thus implementing a feature selection mechanism. The gated fusion submodule applies layer normalization to the weighted feature tensor, calculating the mean and variance of each feature channel and scaling and shifting them to stabilize the numerical distribution. The layer-normalized feature tensor is output as a content semantic enhancement representation. This representation retains key information while suppressing noisy channels, improving the robustness of the feature representation.

[0055] The interaction intent parsing module handles the matching problem between user operation command streams and content semantic enhancement representations. This module consists of an instruction encoding submodule and an intent matching submodule. The instruction encoding submodule preprocesses and vectorizes the user operation command stream, which includes interactive events with consecutive timestamps, such as clicks, drags, and voice commands. It extracts feature vectors for each operation event, including attributes such as operation type, coordinate position, and timestamp. These feature vectors are mapped to a unified-dimensional instruction embedding representation through an embedding layer. A hierarchical positional encoding mechanism adds positional information to the instruction embedding representation. This mechanism includes absolute positional encoding and relative positional encoding. Absolute positional encoding marks the order of instructions in the sequence, while relative positional encoding captures the time interval relationship between instructions. The intent matching submodule calculates the correlation between the instruction embedding sequence and the content semantic enhancement representation. It uses a cross-attention mechanism to establish a bidirectional mapping. In the cross-attention calculation, the instruction embedding sequence serves as the query vector, and the content semantic enhancement representation serves as the key and value vectors. Attention weight distribution is calculated using dot product attention. The attention weight matrix, normalized using the softmax function, represents the correlation strength between each operation instruction and content features. The weighted summation output constitutes the intent distribution matrix. The intent distribution matrix is ​​a two-dimensional probability distribution. The row dimension of the matrix corresponds to the position of the operation instruction sequence, and the column dimension corresponds to the position of the content feature sequence. The matrix element values ​​represent the degree of intent focus of a specific operation on a specific content region.

[0056] The dynamic weight allocation module and the interactive intent parsing module have a tightly integrated data flow, with the semantically enhanced representation of the output from the dynamic weight allocation module being directly input into the interactive intent parsing module. The parameters of the two modules are jointly optimized; during training, gradient backpropagation simultaneously updates the fully connected layer parameters of the channel importance evaluation submodule and the attention layer parameters of the intent matching submodule. The feature selection performance of the dynamic weight allocation module directly affects the matching accuracy of the interactive intent parsing module; enhancing important features improves the clarity of the intent distribution. At the physical implementation level, the channel importance evaluation submodule of the dynamic weight allocation module adopts a parallel computing architecture, simultaneously handling pooling operations for all feature channels. The channel-wise multiplication operation of the gated fusion submodule is implemented through a broadcast mechanism, forming a complete branch of the computation graph with layer normalization. The instruction encoding submodule of the interactive intent parsing module is implemented as a pipelined process, receiving user operation instruction streams in real time and outputting embedded sequences. The cross-attention calculation of the intent matching submodule uses a multi-head attention mechanism, with multiple attention heads computing the matching relationships in different subspaces in parallel, and finally concatenating the outputs of each head to form the intent distribution matrix.

[0057] The hyperparameters of the dynamic weight allocation module include the window size for global average pooling and the number of hidden units in the fully connected layer. These parameters are dynamically adjusted based on the dimension of the content feature tensor. The hyperparameters of the interaction intent parsing module include the dimension of instruction embedding, the number of attention heads, and the frequency of positional encoding. These parameters are matched to the complexity of the user operation instruction flow. The real-time requirements of both modules necessitate the use of efficient algorithms to avoid complex calculations impacting the interactive experience. The error handling mechanism of the dynamic weight allocation module includes the detection and correction of abnormal weight coefficients, and the initiation of a smoothing process when channel weights reach extreme values. The interaction intent parsing module filters invalid operation instructions, identifying and discarding illegal operation requests that exceed the content scope. The integration interface of the two modules is clearly defined, and the input and output data formats are standardized to ensure seamless integration with other modules in the system.

[0058] The dynamic weight allocation module's scalability supports adaptive adjustment of multimodal feature weights. When new modal features are added to the content feature tensor, the channel importance evaluation submodule automatically expands the fully connected layer dimension. The interaction intent parsing module can adapt to the operation commands of new interactive devices by updating the embedding dictionary of the command encoding submodule to be compatible with new operation types. The maintainability of both modules is ensured through modular design, with each submodule developed and tested independently before integration. Performance optimization of the dynamic weight allocation module and the interaction intent parsing module involves computation graph optimization and memory management. The global average pooling of the dynamic weight allocation module is merged with the fully connected layer into a single computation node to reduce memory copying. The attention calculation of the interaction intent parsing module adopts a block processing strategy, with large attention matrices divided into multiple blocks for parallel computation. The numerical stability of both modules is ensured through gradient pruning and normalization layers to prevent numerical overflow during training. The channel importance evaluation submodule of the dynamic weight allocation module can be extended to multi-granularity importance evaluation, introducing multi-scale statistical features combining local window pooling and global pooling. The intent matching submodule of the interaction intent parsing module can integrate a temporal attention mechanism to capture long-term dependencies in the operation command sequence. The collaborative work of the two modules forms a complete content-intent interaction understanding pipeline, providing high-quality input conditions for the layered decoding generation module.

[0059] The implementation of the dynamic weight allocation module and the interaction intent parsing module takes hardware acceleration requirements into account. The pooling operations of the channel importance assessment submodule and the element-wise operations of the gating fusion submodule are mapped to GPU parallel computing units. The attention calculation of the interaction intent parsing module utilizes tensor cores to accelerate matrix operations, and hierarchical positional encoding achieves efficient lookup through lookup tables. The deployment of both modules on embedded devices employs quantization techniques, converting floating-point parameters to fixed-point numbers to reduce computational resource consumption. Testing and verification of the dynamic weight allocation module and the interaction intent parsing module include unit testing and integration testing. Unit tests verify the functional correctness of each submodule, while integration tests check the consistency of data flow between modules. Test cases cover normal operation and boundary conditions, such as empty instruction flows and extreme feature values. The documentation for both modules records detailed design decisions and interface specifications, facilitating subsequent maintenance and upgrades.

[0060] Example 3: Implementation of the Layered Decoding Generation Module. The layered decoding generation module constructs multi-scale content generation paths based on the intent distribution matrix. It includes a multi-scale path construction submodule and an implicit representation synthesis submodule. The multi-scale path construction submodule sets content generation heads of different dimensions in each layer of the Transformer decoder. These content generation heads are specially designed neural network structures used to output feature maps at specific resolution levels. The resolution grading unit divides the decoder network into low-level structure generation paths, mid-level material generation paths, and high-level detail generation paths. The low-level structure generation path processes low-resolution features and focuses on the global shape and contour of the digital content. The mid-level material generation path processes mid-resolution features and adds surface texture and material information. The high-level detail generation path processes high-resolution features and refines edges and fine details. The attention routing unit dynamically allocates attention query vectors for each generation path based on the energy value of the intent distribution matrix. The energy value of the intent distribution matrix is ​​obtained by calculating the sum or maximum value of the probability values ​​at each position in the matrix, reflecting the intensity distribution of the user's operational intent.

[0061] The attention routing unit maps the intent distribution matrix to the query vector space using a learnable parameter matrix. These query vectors are then used for cross-attention computation in the decoder. The operation of the attention routing unit can be represented by the following formula:

[0062]

[0063] in: Indicates the generation path Attention query vector, Is with path The corresponding learnable weight matrix, It is the energy value vector of the intention distribution matrix. It is a path The mapping matrix is ​​used, and the softmax function is used to normalize the energy value distribution. The characters in the formula have the following meanings: It is a path The query vector is used to retrieve relevant features; It is a weight matrix, optimized through training; It is an energy value vector that encodes the intensity of intent; It is a mapping matrix that adapts energy values ​​to the path space. The attention routing unit outputs multiple query vectors, each corresponding to a different generation path, ensuring that intent information is effectively routed to the appropriate resolution level.

[0064] The multi-scale path construction submodule integrates multiple content generation heads at each layer of the decoder. These content generation heads typically consist of fully connected layers or convolutional layers. Fully connected layers project the hidden state onto the feature space, while convolutional layers process the spatial feature maps. The low-level structure generation path uses larger convolutional kernels and strides to extract coarse-grained features, the mid-level material generation path uses medium-sized convolutional kernels to capture local patterns, and the high-level detail generation path uses small convolutional kernels to retain high-frequency information. The intent distribution matrix is ​​decomposed at the resolution level, and a linear transformation maps the rows or columns of the matrix to the input dimensions of each generation head. This decomposition process maintains the consistency of the probability distribution and avoids information loss. The multi-scale path construction submodule improves efficiency through parallel computation, with each generation path processing features independently and then integrating them through a routing mechanism. The implicit representation synthesis submodule receives feature maps output by the multi-scale path construction submodule, and these feature maps have different spatial dimensions and number of channels. The implicit representation synthesis submodule aggregates features through an upsampling convolutional network. The upsampling operation uses transposed convolution or bilinear interpolation to increase the feature map resolution. Transposed convolution amplifies features by learning parameters, while bilinear interpolation smoothly amplifies features through interpolation calculations. The output of the low-level structure generation path is upsampled and then fused with the features of the mid-level material generation path. The fusion operation uses element-wise addition or channel concatenation. Element-wise addition preserves feature consistency, while channel concatenation increases the amount of information. The fused features are then combined with the output of the high-level detail generation path and refined through multiple convolutional layers to finally generate an implicit content representation sequence.

[0065] The implicit representation sequence encodes multi-level details of digital content, with each element corresponding to a time step or spatial location. The implicit representation synthesis submodule uses skip connections to directly connect low-level and high-level features. These skip connections are implemented through identity mapping or 1x1 convolutions to avoid the vanishing gradient problem. The parameters of the upsampling convolutional network are learned through training. The loss function combines reconstruction loss and adversarial loss. The reconstruction loss measures the difference between the generated content and the real content, while the adversarial loss improves realism through a discriminative network. The hierarchical decoding generation module, as part of the Transformer decoder, receives the semantically enhanced representation of the content from the encoder and the intent distribution matrix from the interaction intent parsing module. It generates the final output through multiple rounds of attention computation and a feedforward network. The training process of the hierarchical decoding generation module uses end-to-end optimization, with gradient backpropagation updating all parameters, including the content generation head weights, attention routing unit matrix, and upsampling convolutional kernels. During the inference phase, the hierarchical decoding generation module supports real-time generation by caching attention weights and feature maps to reduce computational latency. The dynamic routing mechanism of the multi-scale path construction submodule allows for adaptive resource allocation based on user intent, allocating more computational resources to regions with strong intent, thus improving generation efficiency. Implicit representation of multi-level fusion of composite submodules ensures richness and consistency of content details, avoiding ambiguity or artificial traces.

[0066] The implementation of the hierarchical decoding generation module considers a balance between computational complexity and generation quality. The resolution grading unit sets the number of paths based on hardware resources; for example, only low- and mid-level paths are used in resource-constrained environments, while all paths are enabled in high-end systems. The energy calculation of the attention routing unit can be extended to multimodal fusion, combining historical data or contextual information from user operations. The output of the implicit representation synthesis submodule can be directly input into the real-time rendering engine module or further post-processed to enhance visual effects. The design of the hierarchical decoding generation module supports flexible expansion, such as adding additional generation paths to handle specific modalities or resolutions without changing the core architecture. The parameters of the multi-scale path construction submodule and the implicit representation synthesis submodule are optimized through training on a large-scale dataset containing diverse digital content and user interaction records. Data augmentation techniques such as rotation, scaling, and color dithering are used during training to improve generalization ability. The inference speed of the hierarchical decoding generation module is optimized through model quantization and knowledge distillation to ensure real-time performance. The deployment of the entire module considers memory footprint and power consumption constraints, making it suitable for embedding into interactive applications or cloud platforms.

[0067] The error handling mechanism of the hierarchical decoding generation module includes anomaly detection and recovery, such as falling back to the default path when the energy value of the intent distribution matrix is ​​abnormal. The module's robustness is improved through adversarial training to resist input noise or malicious manipulation. The interface between the hierarchical decoding generation module and other modules in the system is clearly defined, and the data flow uses a standard tensor format to ensure compatibility. The continuous learning capability of the hierarchical decoding generation module allows for online parameter updates to adapt to new content types or user preferences. Implementation details include logging and performance monitoring for easy debugging and optimization. The innovation of the hierarchical decoding generation module lies in the combination of multi-scale generation paths and dynamic attention routing, achieving efficient and high-quality content generation. The structure of the resolution grading unit is inspired by the human visual system, processing information from global to local. The formulaic expression of the attention routing unit ensures mathematical interpretability, facilitating theoretical analysis and improvement. The implicit representation synthesis submodule's multi-level upsampling strategy balances computational efficiency and detail preservation. The hierarchical decoding generation module demonstrates flexibility in practical applications, supporting various generation tasks from text to images, audio to video. The module's evaluation uses objective metrics such as PSNR and SSIM, as well as subjective user research, but the patent text does not contain specific data. Further optimization directions for the layered decoding generation module include introducing variants of attention mechanisms, such as sparse attention, to reduce computational cost, or using generative adversarial networks to enhance realism. The module's interpretability is improved by visualizing attention weights, aiding in understanding the intent mapping process. The implementation of the layered decoding generation module follows software engineering best practices, with comprehensive code comments and documentation. Patent protection for the layered decoding generation module covers its architecture design, routing algorithms, and synthesis methods, ensuring intellectual property security.

[0068] See Figure 4 The upper sub-image presents the feature distribution of the multi-scale content generation path, showing the feature evolution process from the underlying structure to high-level details through variations in grayscale levels. Regions of varying shades represent the feature response intensity at different resolution levels, demonstrating the dynamic allocation effect of the attention routing mechanism at different scales. The lower sub-image focuses on the implicit representation synthesis stage, showcasing the final output after aggregating multi-scale features through an upsampled convolutional network. The texture details and structural integrity in the image reflect the module's optimization results in balancing computational complexity and generation quality, providing high-quality underlying support for real-time interactive content generation.

[0069] Example 4: Implementation of the Real-Time Rendering Engine Module. Based on the implicit representation sequence output by the layered decoding generation module, an interactive dynamic digital content stream is generated. The neural radiation field construction submodule is responsible for converting the implicit representation into a geometric and visual representation of the 3D scene. The neural radiation field construction submodule uses a field function parameterization unit to model the density field and color field functions. The field function parameterization unit adopts a multilayer perceptron structure. The input of the multilayer perceptron includes the coordinate vector of a 3D spatial point and the direction vector of the viewing angle. The coordinate vector defines the position of the point in the scene, and the direction vector defines the camera's viewing angle. The multilayer perceptron consists of multiple fully connected layers. The fully connected layers use activation functions such as ReLU or sine functions to introduce nonlinearity. The output layer generates a density scalar and a color vector for the point. The density scalar represents the opacity of the spatial point, and the color vector represents the RGB color value. The dynamic field update unit adjusts the weight parameters of the multilayer perceptron in real time according to the user interaction command stream. The user interaction command stream includes operation types such as rotation, scaling, or movement. The command stream is mapped to the bias terms or scaling factors of the multilayer perceptron weights through a control network to ensure that the scene responds to interactive changes. For example, when rendering a virtual cube scene, the content implicit representation sequence encodes the shape and texture of the cube. The neural radiation field construction submodule decodes the implicit representation into the initial weights of the multilayer perceptron. When the user issues a rotation command, the dynamic field update unit calculates the rotation matrix and adjusts the weights so that the cube is correctly rendered from the new perspective.

[0070] The neural radiation field construction submodule's processing flow involves sampling spatial points and querying field functions. The scene space is discretized into a grid or a hierarchical structure is used to accelerate the query. Each sampling point's attributes are calculated using a multilayer perceptron. The implementation of the dynamic field update unit relies on a parameterized conditional network. The conditional network encodes user commands into a context vector, which is concatenated with the spatial point coordinates and input into the multilayer perceptron to achieve dynamic weight adjustment. The output of the neural radiation field construction submodule is a continuous field function, defining the density and color distribution of the entire 3D space, supporting rendering at any resolution. The ray differentiation calculation submodule performs differentiable rendering based on the field function output by the neural radiation field construction submodule. The ray differentiation calculation submodule emits ray beams from a virtual camera, with each ray corresponding to an image pixel. The ray direction is determined by camera parameters. The ray differentiation calculation submodule samples multiple points along the ray, using either uniform sampling or importance sampling strategies. Importance sampling adjusts the sampling interval based on the density field, increasing sampling points in high-density areas. Each sampling point queries the density field and color field, calculates the pixel color through volumetric rendering integration, and accumulates the color contribution of the sampling points, considering transmittance and absorption effects. The integration process achieves differentiability, and automatic differentiation technology is used to calculate the gradient. This gradient is used for end-to-end training to optimize the neural radiation field parameters. The output of the ray differentiation calculation submodule is a pixel-level dynamic content frame sequence, which is output as a video stream, supporting real-time display and interaction. Refer to Table 1 to show the sampling point parameters of a ray in the ray differentiation calculation submodule. The ray is emitted from the camera origin, with its direction towards the scene center, and the sampling points are distributed along the ray.

[0071] Table 1: Parameter Table of Light Sampling Points

[0072] Sampling point index Spatial coordinates (x, y, z) density value Color values ​​(R, G, B) transmittance 1 (0.1,0.2,0.3) 0.05 (0.8,0.6,0.4) 0.95 2 (0.2,0.3,0.4) 0.12 (0.7,0.5,0.3) 0.84 3 (0.3,0.4,0.5) 0.08 (0.9,0.7,0.5) 0.77 4 (0.4,0.5,0.6) 0.15 (0.6,0.4,0.2) 0.65

[0073] The parameters in the table are used for volumetric rendering calculations. Transmittance is obtained by accumulating density values, and color values ​​are weighted and summed to generate the final pixel color. The ray differentiation calculation submodule processes all rays in parallel, utilizing GPU acceleration for efficient rendering.

[0074] The real-time rendering engine module's overall pipeline ensures low-latency interaction. The neural radiation field construction submodule and the ray differentiation calculation submodule work collaboratively: the neural radiation field construction submodule updates the field function, and the ray differentiation calculation submodule generates frame sequences. The implementation of the real-time rendering engine module considers computational efficiency, employing optimization techniques such as early termination of ray calculations via air jumps and skipping sampling in low-density regions to reduce computational load. The real-time rendering engine module also integrates a caching mechanism to cache rendering results from frequently used viewpoints, accelerating repeated access. In a specific example, assuming the rendering of a dynamic digital character, the implicit representation sequence output by the layered decoding generation module contains the character's shape and motion information. The neural radiation field construction submodule maps this implicit representation to the character's density and color fields. When the user changes the character's pose through operation commands, the dynamic field update unit adjusts the multilayer perceptron weights, updating the field function to reflect the new pose. The ray differentiation calculation submodule renders the character from the current viewpoint, generating a smooth animation stream. The output of the real-time rendering engine module directly drives the display device, providing an immersive interactive experience.

[0075] The real-time rendering engine module's error handling includes anomaly detection, such as falling back to the default field when the field function outputs an invalid value, ensuring system robustness. Deployment of the real-time rendering engine module supports multiple platforms, from high-end workstations to mobile devices, balancing quality and performance by adjusting sampling rate and resolution. Training of the real-time rendering engine module uses a combination of synthetic and real-world datasets; the loss function includes the difference between rendered and real images, and regularization terms stabilize training. Continuous optimization of the real-time rendering engine module uses a user feedback loop, recording interaction data to fine-tune field function parameters. The innovation of the real-time rendering engine module lies in the combination of differentiable rendering and dynamic field updates, achieving high-quality real-time graphics generation. The field function parameterization unit of the neural radiation field construction submodule utilizes the universality of multilayer perceptrons to represent complex scenes, while the volume rendering integral of the ray differential computation submodule maintains physical accuracy. The real-time rendering engine module's applications cover virtual reality, games, and simulation training, providing realistic and interactive content. The implementation of the real-time rendering engine module follows best practices in computer graphics, and its modular code facilitates maintenance and expansion.

[0076] Further development directions for the real-time rendering engine module include integrating more efficient sampling algorithms and hardware acceleration technologies to improve rendering speed. The scalability of the real-time rendering engine module allows for the addition of post-processing effects such as global illumination or shadows to enhance visual effects. Patent protection for the real-time rendering engine module covers dynamic field update methods and differentiable rendering pipelines, ensuring technological uniqueness. Actual testing of the real-time rendering engine module uses standard benchmark scenes to evaluate performance, but the patent text does not contain specific data. Implementation details of the real-time rendering engine module include memory management optimization, reducing field function storage overhead, and using compression techniques such as quantization storage density and color values. The interactive response time of the real-time rendering engine module is minimized through pipelined parallelization, with the neural radiation field construction submodule and the ray differentiation calculation submodule executed concurrently. The user interface of the real-time rendering engine module supports multiple input devices, such as gamepads, gestures, or voice, with command streams encoded in a unified format. The reliability of the real-time rendering engine module is ensured through redundant computation and error correction, and backup and recovery of critical scene data. Integration of the real-time rendering engine module with other system modules is achieved through clearly defined API interfaces, with data exchange using standard tensor formats. The real-time rendering engine module's logging system records rendering parameters and performance metrics, facilitating debugging and optimization. The implementation of the real-time rendering engine module prioritizes security and privacy protection, with encrypted processing of user interaction data. The module's documentation provides detailed descriptions of configuration and usage methods, supporting developer integration. The module's open standards compatibility ensures interoperability with third-party tools, promoting ecosystem development.

[0077] Example 5: The version control module is responsible for recording the parameter change trajectory of the dynamic digital content stream on the timeline and establishing a content version tree based on operation instructions. The differential encoding submodule compresses and encodes the changes in content parameters at consecutive time steps. Content parameters include the neural radiation field weights in the real-time rendering engine module, the implicit representation sequence output by the layered decoding generation module, and metadata of user interaction instructions. The differential encoding submodule uses the delta encoding algorithm to compare the parameter sets of adjacent time steps t and t+1, storing only the differences in parameters that have changed, rather than the complete data. For example, during 3D scene editing, when the user translates the cube model from coordinates (0,0,0) to (1,0,0), the differential encoding submodule records the displacement vector (1,0,0) and timestamp information, but does not store the unchanged material parameters. The version branch management submodule creates branch nodes of the content version tree based on the source identifier of the user operation instructions. The source identifier includes the user ID, device fingerprint, and session identifier. The content version tree adopts a directed acyclic graph data structure, and each node stores complete metadata of the content state, including timestamp, differential encoded data, and parent node pointer. The root node corresponds to the initial state of the content. When multiple users edit the same content at the same time, the version branch management submodule creates an independent branch node for each user. The branch node is associated with the base version through a pointer.

[0078] The collaborative editing module implements conflict detection and version merging for multi-user operation command flows. The conflict detection algorithm analyzes the spatiotemporal overlap of operation commands. Temporal overlap detection compares whether command timestamp intervals intersect, while spatial overlap detection calculates the intersection of the 3D bounding boxes of the operation objects. When two users' translation commands act simultaneously on the same model and their spatial ranges overlap, the collaborative editing module marks it as a hard conflict; when the operation objects are different or their spatial areas do not intersect, it is marked as a soft conflict. Version merging uses operation transformation technology to reorder concurrent commands according to logical timing. For example, user A's rotation command and user B's scaling command are merged without conflict through transformation matrix synthesis. The collaborative editing module periodically generates state snapshots of the shared content editing space. The snapshot frequency is dynamically adjusted according to the operation complexity; high-frequency snapshots are used for simple geometric editing, while low-frequency snapshots are used for complex material editing. A specific implementation process uses a multi-user collaborative design of a car model as an example: user A modifies the wheel size while user B adjusts the car body color. The version control module's recording process begins at the initial model state V0. User A's operation command OA1 generates version branch A1, and user B's command OB1 creates branch B1. The difference encoding submodule performs delta encoding on the wheel parameter changes in version A1, storing the size adjustment amount; it encodes the color parameter changes in version B1 separately. When user A submits modifications, the version branch management submodule detects the existence of a new version in branch B, triggering the conflict detection process of the collaborative editing module. Since the wheel size and body color modifications do not overlap in time and space, the collaborative editing module determines it as a soft conflict and automatically merges them to generate a new version V1. During the merging process, the version branch management submodule creates a new trunk node, integrates the difference encoding data from A1 and B1, and generates a complete parameter set containing the wheel size and body color changes.

[0079] The version control module employs a hierarchical data storage structure. The bottom layer stores raw difference data, the middle layer constructs a version tree index, and the top layer provides a version query interface. The difference encoding submodule selects an encoding strategy based on the parameter type: differential pulse code modulation is used for continuous numerical parameters, while dictionary encoding is used for discrete enumerated parameters. For large parameters such as the neural radiation field weight matrix, the difference encoding submodule implements block differential encoding, dividing the matrix into several sub-blocks and calculating the differences separately. The version branch management submodule manages the lifecycle of branch nodes, automatically archiving idle branches and keeping active branches resident in memory for fast access. The content version tree supports version rollback operations, reconstructing the historical state by traversing the parent node pointer chain. The rollback process retains all branch records for auditing purposes.

[0080] The collaborative editing module's conflict resolution strategy is configured with a multi-level processing mechanism. Hard conflicts are prioritized for automatic merging via operation transformation. If merging fails, a user negotiation interface is triggered. The user negotiation interface displays a visual comparison of conflicting operations and provides manual resolution tools. The collaborative editing module's state snapshot generation uses copy-on-write technology, sharing storage space for basic states and saving only differing data to reduce storage overhead. Snapshot metadata includes user permission information and operation logs, supporting role-based access control. State synchronization in the shared content editing space adopts an event-driven architecture. When any user submits a modification, the collaborative editing module broadcasts a state update event, and other user clients receive incremental update data. In an in-depth example of a car model design scenario, when user C attempts to modify the wheel model that user A is editing, the system triggers real-time conflict detection. The collaborative editing module compares the spatiotemporal overlap between user A's currently unsubmitted operations and user C's new operation, detecting that the objects being operated on are the same wheel geometry. The version branch management submodule creates a branch node C1 on the content version tree and marks it as conflicting with branch A1. The collaborative editing module returns an operation rejection notification to user C and provides a branch preview function to display potential merging results. After user A submits changes, the version branch management submodule merges branch A into the main branch, and the collaborative editing module automatically replays user C's pending operations to generate a new branch C2. This implementation ensures data consistency while reducing editing conflicts.

[0081] Performance optimizations in the version control module include incremental indexing of differentially encoded data and a lazy loading mechanism for the version tree. The differential encoding submodule performs incremental compression on frequently updated parameters, merging consecutive small changes into batch update packages. The version branch management submodule optimizes the deep version tree by pruning, merging linear historical nodes to reduce tree depth. The collaborative editing module's distributed processing supports multi-server deployment; operation command streams are partitioned using a consistent hashing algorithm, and state snapshots are stored with multiple replicas to ensure reliability. The integration of the version control and collaborative editing modules achieves asynchronous communication through message queues; version commit operations are processed sequentially in the message queue, avoiding concurrent write conflicts. Implementation details include choosing Protocol Buffers binary protocol for version metadata serialization to ensure cross-platform compatibility. The differential encoding submodule's compressed dictionary establishes a dynamic update mechanism to adapt to the emergence of new parameter types during editing. The version branch management submodule's branch cleanup strategy is based on recent usage frequency, automatically archiving long-unupdated branch nodes. The collaborative editing module's conflict detection algorithm introduces a semantic understanding layer, identifying logical conflicts rather than just spatiotemporal overlaps by analyzing operation intent. For example, simultaneously modifying the load-bearing structure and decorative elements of a model may be identified as a potential semantic conflict, requiring confirmation from the designer.

[0082] The version control module's extended functionality includes version tag management and a difference visualization tool. Users can add semantic tags to important versions, and the system supports quick version node location by tag. The difference visualization tool renders a 3D comparison view of adjacent versions, highlighting areas of parameter change. The collaborative editing module integrates a chat communication component, allowing users to communicate via text during conflict resolution. The system records a complete editing history graph, supporting the tracing of the evolution of each design decision. Data security is paramount during implementation; version transmission employs end-to-end encryption, and access control is implemented for stored data. The interface between the version control module and other system modules is defined as a RESTful API, and version queries support time range filtering and user filtering. The collaborative editing module provides a WebSocket real-time communication interface to ensure low latency for state synchronization. System deployment considers horizontal scaling requirements, and the version storage backend supports a distributed database cluster. Performance monitoring metrics include version commit latency, conflict detection accuracy, and storage compression ratio; these metrics are used for system optimization but do not include specific data. The combined implementation of the version control and collaborative editing modules provides a complete version management solution for large-scale collaborative design projects, balancing operational freedom and data consistency requirements.

[0083] Example 6: Focusing on lesson preparation scenarios for information technology majors, the core is a vertical tool for creating digital textbooks on programming fundamentals. This tool helps teachers integrate multimodal teaching resources to generate interactive, standardized digital textbooks. The original digital content includes programming language specification texts such as Python syntax standards, code example libraries, teaching videos such as algorithm demonstrations, audio explanations such as debugging step instructions, code screenshots or flowchart scans, and exercise case studies such as programming exercises.

[0084] After acquiring this multimodal data, the content feature extraction module converts the programming specification text into word vectors labeled with key terms, segments code examples into embedding vectors according to functional modules, deconstructs teaching videos into key operation frames and extracts image patch features, encodes audio explanations into feature vectors through spectral analysis, and extracts key features such as grammatical structure and comments from code screenshots through image recognition. A multi-head attention mechanism is used to calculate intermodal feature similarity, establishing a correlation between "function definition rules" in the text and function implementations in code examples and debugging operations in videos. Simultaneously, a relative positional bias is applied to the feature similarity matrix to capture the teaching logic of "explaining syntax first, then demonstrating examples" on the timeline, as well as the correspondence between code modules and document comments in the spatial domain. This generates a content feature tensor with spatiotemporal markers, clearly presenting the association between each teaching knowledge point and its corresponding multimodal resource. Specifically, function definition rules refer to the specific grammatical rules in the programming language specification text, such as the part about function definitions in the Python syntax standard, including keywords for function declarations, parameter list formats, and return value conventions. These rules are encoded in the form of text word vectors as part of the multimodal input. Through the cross-modal alignment submodule of the content feature extraction module, the system uses a multi-head attention mechanism to calculate intermodal feature similarity: First, text word vectors (function definition rules), image patch embeddings (function implementations in code examples), and audio spectral features (debugging operations in videos) are projected onto a unified latent space dimension. Then, each attention head independently processes the feature subspace of different modalities, using text features as query vectors and code or video features as key-value pairs, and calculates attention scores through dot product operations to generate a feature similarity matrix. This matrix quantifies the semantic association strength between text rules and code implementations, debugging operations, such as the degree of matching between function definition keywords and corresponding function structures in code blocks, and debugging steps demonstrated in videos. After establishing the association, the spatiotemporal association modeling submodule applies a relative position bias to the feature similarity matrix, encoding the relative distance relationships of feature points on the time axis (e.g., explaining rules before demonstrating operations in teaching sequence) and spatial domain (e.g., the correspondence between function definitions and comments in code layout) through a trainable parameter matrix, thereby capturing long-term dependencies and ensuring that the association conforms to the teaching logic, such as the temporal consistency between rule explanation and practical demonstration.

[0085] The dynamic weight allocation module calculates the global average pooling value along the channel dimension of the content feature tensor and generates the initial weight coefficients for each modality through the learnable parameter matrix. Specifically, the global average pooling value is a key indicator obtained by the channel importance evaluation submodule of the dynamic weight allocation module through statistical analysis of the content feature tensor. The content feature tensor has a three-dimensional structure, corresponding to batch size, sequence length, and number of feature channels, where the number of feature channels represents the number of different modalities or feature dimensions. The calculation process of the global average pooling value is as follows: an average pooling operation is performed on all sequence positions (i.e., batch and sequence length dimensions) of each feature channel, compressing the two-dimensional feature map of each channel into a scalar value. For example, for a certain feature channel, the system iterates through the feature values ​​of all positions in that channel, sums them, and divides by the total number of positions to obtain the global average pooling value of that channel. These pooling values ​​form a channel statistical vector, reflecting the average activation level or importance of each channel in the global context. Subsequently, the channel statistical vector is linearly transformed through the learnable parameter matrix in the fully connected layer to generate the initial weight coefficients for each channel, which are used for subsequent gating fusion. When teachers edit theoretical chapters, the weights of the text modality (programming standards) and the image modality (code screenshots) are dynamically increased to highlight the textual descriptions of grammatical rules and standard code examples. When creating practical teaching chapters, the weights of the video modality (operation demonstrations) and the audio modality (step-by-step explanations) are increased to ensure a clear presentation of the programming process. The initial weight coefficients are processed by the sigmoid activation function, multiplied element-wise with the original features, and then normalized layer by layer to form a semantically enhanced representation, making the core teaching resources of the current lesson preparation stage more prominent.

[0086] The interactive intent parsing module receives the teacher's operation command stream, including dragging and dropping code blocks to adjust the structure, clicking to add annotation examples, and voice input to "supplement knowledge points on exception handling." The instruction encoding submodule performs hierarchical positional encoding on these operations, adds a timestamp to each instruction, and generates an instruction embedding sequence. The intent matching submodule calculates the cross-attention weight between this sequence and the content semantic enhancement representation. If the teacher repeatedly selects a certain type of algorithm code and queries optimization methods, the system will parse out their intent to "improve the teaching content of this type of algorithm," output the corresponding intent distribution matrix, and clarify the knowledge points focused on by the operation and the types of resources that need to be supplemented.

[0087] The hierarchical decoding generation module constructs multi-scale content generation paths in the Transformer decoder based on the intent distribution matrix. The bottom-level structure generation path presents the textbook's chapter framework and knowledge structure, such as from basic syntax to advanced applications. The mid-level material generation path displays the code's structural layout and comment styles. The high-level detail generation path highlights key syntax points, error handling mechanisms, and other details in the code. The attention routing unit, based on the energy values ​​of the intent distribution matrix, prioritizes the allocation of "supplementary knowledge points" intents to the corresponding chapter's detail generation paths. Through an upsampled convolutional network, it aggregates the output features of each path, forming an implicit content representation sequence that includes chapter structure, multimodal teaching content, and interactive exercises. This clearly presents the complete teaching logic from theoretical explanation to practical demonstrations and exercise reinforcement.

[0088] The real-time rendering engine module loads this implicit representation sequence, and the neural radiation field construction submodule maps it to density and color field functions of a 3D teaching scene, such as visualizing the code execution flow. The dynamic field update unit adjusts parameters in real time according to the teacher's operations; for example, when the teacher adjusts the code logic, the system synchronously updates the code highlighting and execution path visualization. Specifically, the field function parameterization unit adopts a multilayer perceptron structure. Its input includes the coordinate vectors of 3D spatial points and the direction vector of the viewing perspective. These vectors originate from the implicit representation sequence output by the hierarchical decoding generation module, which encodes the geometric and appearance information of the teaching content. The multilayer perceptron consists of multiple fully connected layers, which process the input through nonlinear activation functions. The output layer generates a density scalar (representing the opacity or geometric existence of the point) and a color vector (representing the RGB color value) for each spatial point. For example, in a programming teaching scenario, the implicit representation may contain the spatial layout of the code structure, which the multilayer perceptron decodes into a density field (such as the 3D shape of a code block) and a color field (such as syntax highlighting colors). The dynamic field update unit adjusts the weight parameters of the multilayer perceptron in real time based on user interaction command flow (such as adjusting the teaching perspective or modifying code). The commands are encoded into context vectors through a conditional network, concatenated with point coordinates, and input into the network to achieve dynamic updates of the field function. The ray differential calculation submodule performs volume rendering integration on ray sampling points under the observation perspective, generating a pixel-level dynamic content frame sequence. Teachers can intuitively view the layout of the textbook page, the interactive demonstration status of the code, and the association and jump effects between exercises and knowledge points.

[0089] The version control module records the parameter changes during the teacher's creation process, while the difference coding submodule compresses and encodes the changes from operations such as chapter structure adjustments, resource additions, and code modifications. When teachers try different arrangements of teaching content, the version branch management submodule creates independent branch nodes based on the source identifier of the operation, forming a content version tree. Teachers can then review different versions of the textbook draft and compare the rationality of the teaching logic.

[0090] The collaborative editing module supports multiple teachers in jointly writing teaching materials and performs conflict detection on multi-user operation command flows. If two teachers simultaneously modify the code examples in the same chapter, the system will automatically identify and prompt them to coordinate the editing order. The module periodically generates a status snapshot of the shared editing space, integrating the editing results of all teachers. Course leaders can view the progress of the teaching material production through the snapshots and provide real-time modification suggestions. After the teaching material is completed, the system packages all resources into a standardized digital teaching material file, including interactive code demonstrations, synchronized audio and video teaching content, and a programming exercise module that supports automatic assessment, adapted to teaching platforms commonly used in information technology majors.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An interactive digital content production system based on a Transformer architecture, characterized in that, The system comprises: The content feature extraction module obtains multi-modal input data of original digital content, performs cross-modal feature alignment on text, image and audio data through a multi-head attention mechanism, and generates a content feature tensor with spatio-temporal correlation; The dynamic weight distribution module calculates the relative importance scores of different modal features at sequence positions based on the content feature tensor, dynamically weights and fuses the feature channels through a gating mechanism, and forms a content semantic enhanced representation; The interactive intent analysis module receives a user operation instruction stream, performs spatio-temporal coding matching on the content semantic enhanced representation and the instruction stream, and analyzes an intent distribution matrix of the user operation in the content dimension; The hierarchical decoding generation module constructs a multi-scale content generation path in the Transformer decoder according to the intent distribution matrix, and maps the intent distribution to different resolution content generation spaces through a cross-attention layer; The real-time rendering engine module loads the implicit representation output by the multi-scale content generation path, differentiates and renders three-dimensional scene parameters using the neural radiance field technology, and generates an interactive dynamic digital content stream.

2. The interactive digital content production system based on a Transformer architecture according to claim 1, wherein, The content feature extraction module comprises: The cross-modal alignment submodule performs multi-head attention calculation on text word vectors, image block embeddings and audio spectrum features, establishes a modal feature similarity matrix, and fuses cross-modal context information through a residual connection; The spatio-temporal correlation modeling submodule applies a relative position bias on the feature similarity matrix to capture the long-term dependence of different modal features in the time axis and the spatial domain, and outputs a content feature tensor with spatio-temporal labels.

3. The interactive digital content production system based on a Transformer architecture according to claim 1, wherein, The dynamic weight distribution module comprises: The channel importance evaluation submodule calculates the global average pooling value of the content feature tensor along the channel dimension, and generates initial weight coefficients for each channel through a learnable parameter matrix; The gating fusion submodule inputs the initial weight coefficients into a sigmoid activation function, element-wise multiplies the original features, and then adopts layer normalization processing to obtain a content semantic enhanced representation.

4. The interactive digital content production system based on a Transformer architecture according to claim 1, wherein, The interactive intent analysis module comprises: The instruction encoding submodule performs hierarchical position encoding on the user operation instruction stream to generate an instruction embedding sequence with timestamp labels; The intent matching submodule calculates the cross-attention weight between the instruction embedding sequence and the content semantic enhanced representation, and outputs a probability matrix reflecting the distribution of operation intent in the content space.

5. The Transformer architecture based interactive digital content production system of claim 1, wherein, The hierarchical decoding generation module comprises: The multi-scale path construction submodule sets different dimensional content generation heads at each layer of the decoder, and decomposes the intent distribution matrix to the corresponding generation heads according to the resolution level; The implicit representation synthesis submodule aggregates the output features of each generation head through an up-sampling convolutional network to form a content implicit representation sequence with multi-level details.

6. The interactive digital content production system based on a Transformer architecture according to claim 5, wherein, The multi-scale path construction submodule comprises: The resolution grading unit divides the decoder network into a bottom structure generation path, a middle material generation path and a high-level detail generation path; The attention routing unit dynamically allocates attention query vectors for each generation path according to the energy values of the intent distribution matrix.

7. The Transformer architecture based interactive digital content production system of claim 1, wherein, The real-time rendering engine module comprises: The neural radiance field construction submodule maps the content implicit representation sequence into a density field and a color field function of the three-dimensional scene; The light differential calculation submodule performs differentiable volume rendering integration on the light sampling points under the observation view angle to generate a pixel-level dynamic content frame sequence.

8. The interactive digital content production system based on a Transformer architecture according to claim 7, wherein, The neural radiance field construction submodule comprises: The field function parameterization unit decodes the implicit representation into density and color attributes of spatial points by using a multi-layer perception machine; The dynamic field update unit adjusts the weight parameter distribution of the multi-layer perception machine in real time according to the user interaction instruction stream.

9. The Transformer architecture based interactive digital content production system of claim 1, wherein, Further comprising: The version control module records the parameter change trajectory of the dynamic digital content stream on the time axis, and establishes a content version tree based on the operation instruction; The collaborative editing module performs conflict detection and version merging on the operation instruction streams of multiple users to generate a state snapshot of a shared content editing space.

10. The interactive digital content production system based on a Transformer architecture according to claim 9, wherein, The version control module comprises: The difference encoding submodule performs delta compression encoding on the content parameter change amount of consecutive time steps; The version branch management submodule creates independent branch nodes on the content version tree according to the source identification of the user operation instruction.

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