Industrial grade 4d video generation system and method based on spatio-temporal anchoring and hybrid expert parallelism

Abstract

The application discloses a kind of industrial grade 4D video generation system and method based on space-time anchoring and mixed expert parallel, belong to artificial intelligence video generation technical field.System includes eight big modules of global configuration, rigid space-time anchoring, 4D space-time coding and decoding, timing synchronization mixed expert, mass data flow loading, multistage distributed training, real-time inference scheduling, post-processing enhancement;Method is through building multidimensional rigid space-time anchor point tensor to eradicate iterative cumulative error, uses non-recursive 4D convolution to realize video global coding and decoding, uses multi-path mixed expert network to improve model capacity, combined with multistage distributed parallel to break through memory bottleneck, and realizes model hardware resident and multi-path real-time generation at inference end.The application completely solves the problem of timing drift and structure tearing of traditional autoregressive architecture, has the characteristics of high stability, high efficiency and low delay, and is suitable for film-level industrial video intelligent production scene.

Description

Technical Field

[0001] This invention belongs to the fields of artificial intelligence video generation, deep learning architecture and distributed industrial computing technology. Specifically, it relates to a highly stable, low-latency industrial-grade 4D video generation system and method, which is particularly suitable for the industrial production of long-time, high-resolution cinematic videos and can eradicate iterative accumulation errors, temporal drift and structural tearing problems from the bottom up. Background Technology

[0002] Existing video generation technologies mostly adopt an autoregressive frame-by-frame generation architecture, which has many technical shortcomings in industrial-scale production scenarios: First, inter-frame errors accumulate layer by layer, and during long-term generation, iterative disasters such as object shape collapse, temporal drift, and structural tearing are prone to occur, which cannot meet the stability requirements of industrial-grade content; Second, the contradiction between large model capacity and single-card memory bottleneck is prominent, and traditional distributed parallel strategies are imperfect, making it difficult to adapt to large-scale industrial cluster training and resulting in low training efficiency; Third, the use of traditional batch reading methods for massive video datasets leads to significant IO processing bottlenecks, which restrict the overall training speed; Fourth, repeated model loading at the inference end is time-consuming and has high latency, making it impossible to achieve real-time text-to-video generation and difficult to adapt to industrial production line processes.

[0003] Current technologies only repair generation defects through post-processing, without eradicating the error accumulation mechanism from the underlying architecture. The model's stability, scalability, and real-time performance cannot meet the requirements of industrial-grade deployment. Therefore, a new 4D video generation architecture is urgently needed to solve these problems. Summary of the Invention

[0004] 3.1 Purpose of the Invention This invention addresses the shortcomings of existing technologies by providing an industrial-grade 4D video generation system and method based on spatiotemporal anchoring and hybrid expert parallelism. It aims to completely eliminate iterative accumulation errors from the underlying architecture, achieve efficient distributed training of ultra-large models, bottleneck-free loading of massive data, and low-latency end-to-end real-time inference, and solve the core problems of existing technologies such as time drift, memory bottlenecks, IO latency, and low inference efficiency. It is suitable for the entire process of film-level industrial video production.

[0005] The system of this invention comprises eight core modules, which work together to complete the entire process from data processing and model training to real-time inference, as detailed below: Global configuration module: Unifies the configuration of model architecture, distributed parallel strategy, training accuracy mode, data loading rules and inference scheduling parameters, adapting to different industrial deployment scenarios; Rigid spatiotemporal anchoring module: Constructs a multi-dimensional fusion global unified spatiotemporal anchor tensor, forcing the model to share the same structural benchmark across all time-series segments, thus blocking the error accumulation path from the root. 4D Spatiotemporal Encoding and Decoding Module: Adopts a non-recursive 4D convolutional architecture to complete the encoding, compression, decoding, and restoration of the entire video spatiotemporal volume in one go, eliminating inter-frame recursive dependencies and completely eliminating the iteration curse; The time-synchronous hybrid expert module builds a multi-parallel expert network, combined with routing units and spatiotemporal constraint units, which greatly improves the model capacity under the premise of controllable computation, while ensuring the temporal consistency of the generated results between frames. Massive data streaming loading module: It adopts block reading, background asynchronous prefetching, and cache queue scheduling mechanism to eliminate the bottleneck of massive video data IO processing and is suitable for training large-scale industrial-grade datasets; Multi-level distributed training module: Integrates multiple parallel strategies and combines distributed optimization technology to realize the sharding of model weights, gradients and optimizer states, break through the bottleneck of single card memory, and adapt to large-scale cluster training. Real-time inference scheduling module: Enables model hardware memory to be resident and multi-channel parallel sampling scheduling to complete low-latency end-to-end generation of text instructions to video content; Post-processing enhancement module: Performs image quality optimization, timing consistency correction, and lip-sync processing on the generated video to output video content that meets industrial-grade standards.

[0006] An industrial-grade 4D video generation method based on spatiotemporal anchoring and hybrid expert parallelism includes the following steps: System initialization: The global configuration module completes the unified configuration of model architecture, distributed strategy, data processing and inference rules, and builds the system operating environment; Rigid spatiotemporal anchor point construction: Integrating four core features—object feature embedding, temporal constraints, spatial grid, and physical rules—and generating a globally unified spatiotemporal anchor point tensor after normalization, this tensor is then connected to the computational units of each layer of the main model. Massive data preprocessing: Through the massive data streaming loading module, industrial-grade video data is segmented, asynchronously prefetched, and cached for scheduling, achieving data transmission without IO bottlenecks; Multi-level distributed training: The multi-level distributed training module is launched, integrating multi-dimensional parallel strategies and adopting a multi-stage progressive training process to complete the training of the 4D spatiotemporal encoding and decoding model and the temporal synchronous hybrid expert model. During the training process, optimizer sharding and gradient accumulation are used to improve efficiency. Real-time inference generation: The trained model is kept resident in hardware memory in singleton mode, receives text creation instructions and completes encoding, and schedules multiple parallel sampling units to generate multiple video streams simultaneously; Post-processing output: Multiple video streams are aggregated and spliced, and after optimization by the post-processing enhancement module, industrial-grade standard video content is exported.

[0007] Completely eliminate iteration disasters: Abandon the traditional autoregressive architecture and adopt a global one-time generation mode. Rigid spatiotemporal anchoring eliminates temporal drift, structural tearing and cumulative errors from the root, and the stability of generated content meets the requirements of industrial-grade production. Balancing Model Capacity and Efficiency: Multi-path hybrid expert networks significantly improve model representation capabilities with controllable computational load, while multi-level distributed parallel strategies overcome memory bottlenecks, adapting to large-scale industrial clusters and significantly improving training efficiency. High-efficiency processing of massive data: Streaming asynchronous prefetching technology eliminates IO bottlenecks, supports training of ultra-large-scale industrial-grade video datasets, and greatly optimizes data processing efficiency; Industrial-grade real-time generation: The model is always resident on hardware and uses multi-path parallel inference to achieve low-latency response from text to video. It can be directly connected to industrial content production processes to improve the efficiency of large-scale production. High adaptability and scalability: The system adopts a modular design, which can flexibly adapt to different hardware clusters, video generation needs of different resolutions and durations, and has strong industrial deployment and expansion capabilities. Example 1. In the system initialization phase, the model architecture, multi-level parallel strategy, data streaming loading rules and real-time inference parameters are set through the global configuration module to complete the loading of each module and the adaptation to the hardware environment; the rigid spatiotemporal anchoring module initializes object feature embedding, temporal constraints, spatial grid and physical rule units, and merges them to generate a global spatiotemporal anchor tensor, which is then connected to the subsequent calculation module. Example 2. In the model training phase, the massive data streaming loading module reads the industrial-grade video dataset in blocks and performs asynchronous prefetching in the background, transmitting it to the training module without bottlenecks. The multi-level distributed training module starts a collaborative strategy of data parallelism, tensor parallelism, and pipeline parallelism, and completes the training in a three-stage progressive process. First, the encoding and decoding model is verified and converged, and then the main model is trained in full. The spatiotemporal structure is constrained by the rigid spatiotemporal anchoring module throughout the process to ensure training stability. After training is completed, the model weights are fixed.

[0008] Example 3. In the real-time inference stage, the trained model is kept resident in the hardware memory, receives text creation instructions and completes encoding; the real-time inference scheduling module starts the multi-path parallel sampling unit and generates multiple sets of video streams with different shots at the same time; after the video streams are aggregated and spliced, they enter the post-processing enhancement module to complete image quality optimization and timing correction, and finally output industrial-grade high-definition video content. Attached Figure Description

[0009] Figure Labels 101 Global Configuration Module 102 Rigid Spatiotemporal Anchoring Module 103 4D Spatiotemporal Encoding / Decoding Module 104 Timing Synchronization Hybrid Expert Module 105 Massive Data Streaming Loading Module 106 multi-level distributed training modules 107 Real-time Inference Scheduling Module 108 Post-processing Enhancement Module Figure 1 Overall structural block diagram of industrial-grade 4D video generation system It mainly includes a global configuration module, a rigid spatiotemporal anchoring module, a 4D spatiotemporal encoding and decoding module, a time-series synchronous hybrid expert module, a massive data streaming loading module, a multi-level distributed training module, a real-time inference scheduling module, and a post-processing enhancement module. These modules are connected in sequence to form a complete end-to-end video generation system, realizing the entire process from text command input to industrial-grade video output.

[0010] Figure 2 Schematic diagram of rigid spatiotemporal anchoring module This module consists of an object feature embedding unit, a temporal constraint vector unit, a spatial grid vector unit, a physical rule anchor point unit, and a fusion normalization unit. It generates a globally unified spatiotemporal anchor point tensor through multi-dimensional feature fusion, providing the model with full temporal structural constraints.

[0011] Figure 3 Schematic diagram of the timing-synchronous hybrid expert module structure It includes a routing weight calculation unit, a spatiotemporal constraint matrix unit, a multi-path expert network array, and an output fusion unit. The routing unit filters and activates experts, and the spatiotemporal constraint unit ensures inter-frame consistency, thereby achieving high-capacity, time-synchronized feature transformation.

[0012] Figure 4 Flowchart of multi-level distributed training method The process includes system initialization and configuration, streaming data preprocessing, multi-level parallel training scheduling, three-stage progressive training, optimizer sharding and gradient accumulation, and model weight saving, enabling efficient model training in large-scale industrial clusters.

[0013] Figure 5 Flowchart of Industrial-Grade Real-Time Inference Method It includes model loading and persistent memory initialization, text command reception and encoding, multi-channel parallel video stream sampling, video stream aggregation and splicing, post-processing enhancement and industrial-grade video export, to achieve low-latency end-to-end video generation.

Claims

1. An industrial grade 4D video generation system based on spatio-temporal anchoring and hybrid expert parallelism, characterized in that, include: The module includes a global configuration module, a rigid spatiotemporal anchoring module, a 4D spatiotemporal encoding and decoding module, a time-series synchronous hybrid expert module, a massive data streaming loading module, a multi-level distributed training module, a real-time inference scheduling module, and a post-processing enhancement module. The rigid spatiotemporal anchoring module is used to generate a multi-dimensional fusion global unified spatiotemporal anchor tensor. The temporal synchronous hybrid expert module includes a multi-path expert network, a routing weight calculation unit, and a spatiotemporal constraint matrix unit. The 4D spatiotemporal encoding and decoding module adopts a non-recursive 4D convolutional architecture to complete the encoding and decoding of the complete video spatiotemporal volume in one go.

2. The industrial grade 4D video generation system of claim 1, wherein, The multi-level distributed training module integrates data parallelism, tensor parallelism, and pipeline parallelism strategies, and combines distributed optimization technology to realize the piecewise processing of model weights, gradients, and optimizer states.

3. The industrial-grade 4D video generation system according to claim 1, characterized in that, The massive data streaming loading module adopts a block reading, background asynchronous prefetching, and cache queue scheduling mechanism to achieve IO-free processing of massive video data.

4. The industrial-grade 4D video generation system according to claim 1, characterized in that, The real-time inference scheduling module enables the model hardware memory to be resident and multi-channel parallel sampling scheduling to complete the low-latency end-to-end generation of text instructions into video content.

5. An industrial-grade 4D video generation method based on spatiotemporal anchoring and hybrid expert parallelism, characterized in that, Includes the following steps: 1) The system model architecture, distributed strategy, data processing, and inference rules are configured uniformly through the global configuration module; 2) Construct a rigid spatiotemporal anchor tensor that integrates object features, temporal constraints, spatial grids, and physical rules, as a unified structural benchmark for the full temporal computation of the model; 3) The massive data streaming loading module enables the segmented prefetching and bottleneck-free transmission of industrial-grade video data; 4) Start the multi-level distributed training module and adopt a multi-stage progressive process to complete the training of the 4D spatiotemporal encoding and decoding model and the time-series synchronous hybrid expert model; 5) The trained model is kept resident in the hardware memory, receives text commands, and schedules multiple parallel sampling units to generate multiple video streams; 6) The generated video is optimized through the post-processing enhancement module to output industrial-grade standard video content.

6. The industrial-grade 4D video generation method according to claim 5, characterized in that, Step 4) describes a multi-stage progressive training process, which is divided into three stages: verification of the encoding / decoding model, full convergence of the encoding / decoding model, and full training of the main model.

7. The industrial-grade 4D video generation method according to claim 5, characterized in that, Step 4) The timing-synchronous hybrid expert module has a routing weight calculation unit that selects and activates some expert networks based on input features, and a spatiotemporal constraint matrix unit that ensures the consistency of expert call results between frames.

8. The industrial-grade 4D video generation method according to claim 5, characterized in that, Step 5) The multi-channel parallel sampling unit simultaneously generates multiple video streams from different shots, which are then aggregated and spliced ​​to form complete video content.

9. The industrial-grade 4D video generation method according to claim 5, characterized in that, The 4D spatiotemporal encoding and decoding module has no frame-by-frame recursion throughout the process, and the latent space features are constrained by distribution to avoid spatial distortion.

10. The industrial-grade 4D video generation method according to claim 5, characterized in that, The training process employs hybrid precision and quantization techniques, combined with gradient accumulation and dynamic loss scaling to improve training throughput efficiency.

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