A neural network-based 3D head reconstruction method and system

Abstract

This invention discloses a 3D head reconstruction method and system based on neural networks, comprising the following steps: 1) Modeling multi-source 3D head data using a VAE-based unified latent representation modeling mechanism to construct a 3D head VAE model; 2) Employing a diffusion model-based conditional complete head structure generation mechanism to achieve progressive recovery from local facial latent representations to complete head latent vectors. This invention can automatically extract head structural features from 3D face and 3D head models, and then generate a complete and unified 3D head model based on local 3D faces, providing 3D model support for subsequent applications such as animation, virtual reality, and medical imaging. This invention achieves unified representation of 3D structures through targeted training based on variational autoencoders and diffusion models, while balancing generation accuracy and computational efficiency, and can be used in scenarios such as medical analysis, rehabilitation-assisted design, and personalized digital modeling.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and 3D reconstruction technology, specifically to a 3D head reconstruction method and system based on neural networks. Background Technology

[0002] In recent years, with the rapid development of 3D digital modeling technology, computer vision, and intelligent sensing systems, the demand for 3D head structure modeling in fields such as virtual reality and augmented reality, human-computer interaction, digital human construction, film and animation production, industrial design, and personalized product development has continued to grow. High-precision 3D head models are not only a crucial foundation for achieving spatial alignment, morphological analysis, and personalized adaptation, but also provide key data support for digital content generation and intelligent modeling. However, in practical applications, complete and high-quality head structure data is often difficult to acquire stably. Significant differences exist between different acquisition devices, resolutions, and data formats, and data noise and pose variations also affect modeling accuracy. These factors, to some extent, limit the generalization ability and engineering implementation efficiency of 3D head structure modeling methods. Currently, 3D head reconstruction mainly relies on the following technical paths: The first category comprises direct modeling methods based on medical imaging equipment (such as MRI or CT). These methods can acquire complete cranial structures with high accuracy, but suffer from high equipment costs, complex acquisition processes, radiation risks (CT), and unsuitability for frequent scanning. The second category consists of 3D facial scanning methods based on structured light or RGB-D equipment. These methods are portable, low-cost, and suitable for rapid acquisition. However, their acquisition range typically only covers the facial area, making it difficult to acquire complete posterior head and cranial top structures, resulting in significant model gaps and failing to meet requirements. The third category comprises 3D completion methods based on statistical shape models or deep learning models. These methods attempt to learn morphological distribution patterns from existing head databases to generate complete structures under partial input conditions. However, existing solutions generally rely on strictly paired data (i.e., complete head data and local facial data of the same object), which is often difficult to obtain in actual data acquisition. Furthermore, differences in resolution, coordinate systems, and statistical distribution shifts between different data sources lead to insufficient model generalization ability.

[0003] In practical scenarios, the following technical challenges exist: significant differences in the distribution of multi-source heterogeneous data; obvious differences in acquisition methods, resolution, noise distribution, and coordinate representation between medical imaging data and facial scan data, making direct fusion difficult; difficulty in acquiring paired data; limited number of samples with both high-quality MRI and facial 3D scan data for the same patient, making it difficult to train supervised learning models; and non-linear morphological relationships between facial geometry and the overall skull structure, making it difficult to guarantee structural consistency using simple interpolation or symmetric completion methods.

[0004] In recent years, deep generative models have made progress in the field of 3D shape modeling, but general generative frameworks often focus on visual realism rather than structural consistency. Furthermore, most models are not specifically designed for cross-modal data adaptation, resulting in a significant decrease in reconstruction quality when the source of input data changes.

[0005] For example, when only facial point cloud data acquired by structured light is input, existing models may generate a visually smooth posterior brain structure, but its actual geometric proportions are inconsistent with statistical regularities; when the input resolution changes or local missing data exists, the model stability further decreases. In addition, existing methods often simply splice together or uniformly encode different data sources, without systematically adapting to cross-modal distributions at the level of latent representation space.

[0006] An existing method for constructing a complete 3D morphable model of the human head integrates multiple local 3D shape models to establish a unified statistical representation model of the head, which is then used for complete head structure reconstruction. This method primarily relies on 3DMM parametric modeling and regression prediction to complete missing regions. However, its technical approach depends heavily on statistical shape models and parametric regression methods, failing to establish a unified probabilistic latent representation space and neglecting conditional generative inference within that latent space. Consequently, it cannot effectively address the problem of generating a complete head structure given only local facial data.

[0007] Another 3D shape completion method based on hierarchical variational autoencoders (DAE) (Probabilistic Shape Completion by Estimating Canonical Factors with Hierarchical VAE) achieves probabilistic completion from partial point clouds to complete shapes by learning the latent distribution of shapes. However, this method is mainly aimed at general 3D shape completion tasks and does not model human head structures, nor does it consider the unified preprocessing and alignment of multi-source 3D head data. Furthermore, this paper does not incorporate a diffusion model for conditional generation, making it impossible to achieve progressive reconstruction of the complete head structure based on local facial information.

[0008] Therefore, in the absence of large-scale, rigorously paired samples, how to achieve: unified morphological representation of data from different sources; effective alignment of cross-modal feature distributions; and complete head structure generation under local input conditions has become a key problem that current 3D head reconstruction technology urgently needs to solve.

[0009] Based on the above background, it is necessary to propose a 3D head reconstruction method and system oriented towards application scenarios, which can construct a unified latent representation space under limited data conditions and realize conditional structure generation, thereby improving reconstruction accuracy and system deployability. Summary of the Invention

[0010] The core of this invention lies in constructing a neural network-based 3D head reconstruction system. This system can automatically extract head structural features from 3D face and 3D head models, and then generate a complete and unified 3D head model using local 3D face data as a condition. This provides 3D model support for subsequent applications such as animation, virtual reality, and medical imaging. This method achieves a unified representation of the 3D structure through targeted training based on a variational autoencoder (VAE) and a diffusion model, while balancing generation accuracy and computational efficiency. It can be applied to scenarios such as medical analysis, rehabilitation-assisted design, and personalized digital modeling.

[0011] The system uses a generative model as its core, combining latent spatial encoding and conditional generation strategies to form a complete closed-loop process: "Image input—Latent feature encoding—3D structure generation." After targeted training on a 3D head dataset, the model can accurately understand the morphological features and detailed information in the input model and generate 3D head data that can be directly used for subsequent modeling, rendering, or analysis, providing stable and interpretable foundational support for 3D reconstruction applications.

[0012] The present invention is achieved by at least one of the following technical solutions.

[0013] A 3D head reconstruction method based on neural networks includes the following steps: (1) A unified latent representation modeling mechanism for three-dimensional heads based on VAE is used to model multi-source three-dimensional head data and construct a three-dimensional head VAE model; (2) A conditional complete head structure generation mechanism based on a diffusion model is adopted to achieve progressive recovery from local facial latent representation to complete head latent vector.

[0014] Furthermore, before proceeding to modeling, step (1) involves uniform preprocessing of the multi-source 3D head data, including the following steps: 11. Coordinate normalization: Convert 3D meshes or point clouds from different acquisition sources to a standard coordinate system and perform scale normalization. 12. Topology unification: Resampling the 3D mesh to ensure that the number of vertices matches the topology; 13. Pose alignment: Rigid registration based on key anatomical landmarks eliminates the interference of pose differences on morphological modeling; 14. Unified data format: Convert point cloud or grid data into a unified tensor representation.

[0015] Furthermore, the 3D head VAE model includes an encoder network and a decoder network; the encoder network is used to compress high-dimensional 3D head structure data into low-dimensional continuous latent representations, and the decoder network is used to decode the latent vectors back into the complete 3D head structure.

[0016] Furthermore, the encoder network performs multi-layer feature extraction and aggregation on the input 3D point cloud or mesh data, gradually transitioning from local geometric features to overall morphological features. Through feature fusion and global representation mapping, it finally outputs the distribution parameters of latent variables, which are used to characterize the probability distribution of the 3D head structure in the latent space.

[0017] Furthermore, the decoder network first performs feature expansion on the latent vectors, and then gradually recovers the three-dimensional spatial coordinate information through a multi-layer structure reconstruction module, outputting a point cloud or mesh representation of the complete head.

[0018] Furthermore, the latent vectors of local faces are used as conditional constraints input into the conditional diffusion model. Through a conditional guidance mechanism, the generation process infers the overall head structure while maintaining facial consistency.

[0019] Furthermore, the loss function of the conditional diffusion model :

[0020] in, Represents the original latent vector In the The result after adding noise to the step time. This represents the latent vectors of a portion of the faces encoded by VAE, which serve as conditional information. This represents the neural network inside the diffusion. The expected value represents the average over all cases. This represents the actual Gaussian noise added.

[0021] A system for implementing the aforementioned neural network-based 3D head reconstruction method includes: The data construction and preprocessing module is used to standardize multi-source 3D head data, including coordinate normalization, scale unification, topology consistency, pose alignment and data format unification, in order to construct standard input data that meets the requirements of network training. The VAE-based unified latent representation learning module for 3D heads is used to compress high-dimensional 3D head structure data into low-dimensional continuous latent representations and learn the unified probability latent distribution of the complete head structure. The complete head structure generation module based on the conditional diffusion model is used to perform probabilistic generation inference in a unified latent space with local facial latent representation as a condition, gradually recover the complete head latent representation, and decode and generate the complete 3D head structure.

[0022] A computer device according to the present invention includes a memory and a processor, the memory being electrically connected to the processor, the memory storing a computer program, which, when executed by the processor, causes the processor to implement the method described herein.

[0023] The present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor implements the method described herein.

[0024] Compared with existing technologies, the beneficial effects of the present invention are as follows: The beneficial effects of this invention are reflected in three main aspects: improved accuracy of 3D reconstruction, optimized computational and engineering efficiency, and application adaptability.

[0025] Firstly, regarding 3D representation capabilities, this invention utilizes VAE latent space modeling and a conditional generation mechanism based on diffusion models to enable the system to deeply understand the head morphology and detailed features in the input 2D image, achieving high-precision mapping from 2D projection to a complete 3D head structure. The model can not only reconstruct explicit head structures, such as facial contours and the position of facial features, but also generate semantic-level detailed information, such as skull proportions, surface texture, and hairstyle distribution. This refined representation capability significantly outperforms traditional 3D reconstruction methods based on keypoints or template matching, generating highly consistent 3D models suitable for subsequent rendering or analysis.

[0026] Secondly, in terms of computational and engineering efficiency, this invention significantly reduces the computational load and storage requirements of the model through latent spatial encoding and conditional diffusion generation. Experiments show that, while maintaining 3D reconstruction accuracy, model training time is shortened by approximately 50%, memory usage is reduced by more than 60%, and real-time inference for single images is supported. This means that developers and research institutions can deploy high-precision 3D reconstruction systems without high-cost computing resources, enabling "lightweight and practical" deep generative model applications. Compared with traditional full-parameter optimization or large-scale multi-view reconstruction methods, this invention not only reduces training and deployment costs but also has the advantages of rapid iteration and customized reconstruction, allowing for local retraining or fine-tuning based on new data or specific style requirements.

[0027] Furthermore, regarding application adaptability, this invention utilizes conditional generation and a multi-dimensional quality assessment mechanism to enable the generated results to be directly adaptable to various application scenarios. When the input image is occluded, has facial expression changes, or is subject to lighting interference, the system can adaptively infer missing regions and generate complete 3D structures that conform to head anatomy and visual principles. The system also provides structured evaluation metrics, such as 3D error distribution, keypoint consistency, and texture similarity, which can provide interpretable model performance feedback for developers or researchers, addressing the "black box" problem of deep generative models.

[0028] Overall, this invention breaks through the limitations of traditional 3D reconstruction methods in terms of accuracy, efficiency, and robustness, while taking into account algorithm innovation, computing power optimization, and multi-scenario adaptability. It provides a high-precision and practical implementation path for 3D head modeling and has significant academic research and industrial application value. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall architecture of a neural network-based 3D head reconstruction method as an example.

[0030] Figure 2 This is a schematic diagram of the diffusion process in an example.

[0031] Figure 3 This is a schematic diagram of system reasoning for an example. Detailed Implementation

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] This embodiment of a neural network-based 3D head reconstruction system includes: The data construction and preprocessing module is used to standardize multi-source 3D head data, including coordinate normalization, scale unification, topology consistency, pose alignment and data format unification, in order to construct standard input data that meets the requirements of network training. The VAE-based unified latent representation learning module for 3D heads is used to compress high-dimensional 3D head structure data into low-dimensional continuous latent representations and learn the unified probability latent distribution of the complete head structure. The complete head structure generation module based on the conditional diffusion model is used to perform probabilistic generation inference in a unified latent space with local facial latent representation as a condition, gradually recover the complete head latent representation, and decode and generate the complete 3D head structure.

[0034] This embodiment presents a 3D head reconstruction method based on a neural network, such as... Figure 1 As shown, this method, centered on 3D representation understanding, achieves a continuous mapping from a 3D facial model to a 3D head model through 3D facial input, latent space encoding, and conditional structure generation, ensuring the integrity and accuracy of the reconstruction results in terms of morphology and detail. Specifically, it includes the following steps: (1) A unified latent representation modeling mechanism for three-dimensional head based on VAE is adopted to realize the compressed expression and stable reconstruction of three-dimensional head structure.

[0035] In 3D head reconstruction, significant differences exist between different data sources. For example, head models reconstructed from CT images have high structural accuracy but are costly; 3D facial scan data focuses on surface geometric details but often lacks the posterior brain region; different acquisition devices also exhibit inconsistencies in resolution, scale units, and coordinate system definitions. These differences result in the original 3D data exhibiting a highly discrete state in the distribution space, making it difficult to directly perform unified modeling and generation inference. Therefore, it is first necessary to construct a continuous, controllable, and statistically consistent 3D morphological latent representation space to support the subsequent structure generation and completion process. This invention proposes a unified latent representation modeling mechanism for 3D heads based on a Variational Autoencoder (VAE) model. By learning the distribution of complete head structures through a probabilistic generation framework, it achieves latent space alignment and morphological normalization of multi-source data, including the following steps: (1.1) Standardization and unified representation of three-dimensional head data.

[0036] Before proceeding with VAE modeling, this invention first performs unified preprocessing on the multi-source 3D header data, including the following steps: 11. Coordinate normalization: Convert 3D meshes or point clouds from different acquisition sources to a standard coordinate system and perform scale normalization. 12. Topology unification: Resampling the 3D mesh to ensure that the number of vertices matches the topology; 13. Pose alignment: Rigid registration based on key anatomical landmarks eliminates the interference of pose differences on morphological modeling; 14. Unified data format: Convert point cloud or grid data into a unified tensor representation to support neural network input.

[0037] Through the above steps, the original 3D head model is transformed into a data representation with consistent structure and uniform scale, providing a stable input foundation for subsequent latent space learning.

[0038] (1.2). Constructing a 3D head VAE model based on probabilistic latent space modeling of VAE.

[0039] After data standardization, this invention constructs a 3D head VAE model. In one embodiment, the 3D head VAE model comprises an encoder network and a decoder network. The encoder network compresses high-dimensional 3D head structure data into a low-dimensional continuous latent representation. Its input is 3D point cloud or mesh data in a uniform format. The network first performs multi-layer feature extraction and aggregation on the input data, gradually transitioning from local geometric features to overall morphological features. Through feature fusion and global representation mapping, it finally outputs the distribution parameters of latent variables, including mean vector and variance vector, used to characterize the probability distribution of the 3D head structure in the latent space. To ensure the stability and scalability of the model in engineering implementation, the encoder structure adopts a modular design, supporting different types of 3D data input, and improves training stability through feature normalization and residual connection mechanisms, making the latent space representation continuous and interpolable. The decoder network decodes the latent vectors back into a complete 3D head structure. The decoder first performs feature expansion on the latent vectors, and then gradually recovers the 3D spatial coordinate information through a multi-layer structure reconstruction module, outputting a point cloud or mesh representation of the complete head. This process ensures the consistency of the generated structure in terms of overall proportion and local geometric details.

[0040] The loss function for the VAE model is as follows:

[0041] in, Represents actual 3D head data. This represents the 3D head generated by the model.

[0042] Through the above-mentioned encoding and decoding structure design, this invention realizes the compressed expression and stable reconstruction of the three-dimensional head structure, enabling high-dimensional geometric data to form a continuous distribution in the low-dimensional latent space, providing a reliable engineering foundation for the subsequent diffusion model to generate conditions in the latent space.

[0043] (2) A conditional complete head structure generation mechanism based on a diffusion model is adopted to achieve progressive recovery from local facial latent representation to complete head latent vector.

[0044] After completing the latent space modeling of the 3D head morphology, the key challenge in 3D completion tasks is how to infer the complete head structure from only local facial data. Traditional generation methods typically employ one-time regression prediction or autoencoder completion, which are prone to problems such as structural collapse, proportional imbalance, or blurred details, especially when inferring from unobserved regions (such as the top of the skull and the back of the head). Therefore, it is necessary to construct a progressive, distributed constraint generation mechanism that enables the model to gradually recover the complete structure in the latent space.

[0045] To address this, this invention proposes a conditional complete head structure generation mechanism based on a diffusion model. This mechanism performs probabilistic generation inference within a unified latent space constructed by a VAE (Visual Envelope Architecture), achieving a progressive recovery from local facial latent representations to complete head latent vectors. Figure 2 As shown.

[0046] In practical applications, the system input is usually local facial 3D data. First, the local facial 3D data is mapped to the latent space using a pre-trained VAE encoder to obtain the facial latent representation vector.

[0047] Since the complete head latent vector contains information about unobserved regions, this invention uses local facial latent representation vectors as conditional constraints input to the conditional diffusion model. Through a conditional guidance mechanism, the generation process infers the overall head structure while maintaining facial consistency. In one embodiment, local facial latent vectors (generated by a VAE encoder) are used as conditional inputs to the diffusion model, and the latent space representation is initialized with random noise. During the training phase, the mapping relationship from noise to the complete head latent representation is learned through a progressive noise addition and denoising process. The training data includes multi-angle, multi-pose 3D head scan data, which undergoes normalization and alignment processing to ensure spatial consistency between the input conditions and the complete head.

[0048] During the optimization phase, the conditional diffusion model uses an optimizer to update parameters. To improve training stability and generation performance, this invention introduces a learning rate preheating and linear decay strategy, and evaluates the model using a validation set after each training round. Automatic early stopping occurs when the loss shows no improvement for three consecutive rounds, ensuring model convergence and stability.

[0049] To simultaneously improve generation accuracy and conditional consistency, this invention designs the following training loss function for the conditional diffusion model. :

[0050] in, Represents the original latent vector In the The result after adding noise to the step time. This represents the latent vectors of a portion of the faces encoded by VAE, which serve as conditional information. This represents the neural network inside the diffusion. The expected value represents the average over all cases. This represents the actual Gaussian noise added.

[0051] In the reasoning stage, such as Figure 3As shown, the system starts with a noise-initialized latent representation, combines it with local facial conditional vectors, and generates a complete head latent representation through multiple denoising steps. Subsequently, the latent representation is used by a VAE decoder to generate a 3D head model, including head shape, key bone locations, and facial surface topology, ensuring anatomical rationality and structural integrity.

[0052] Evaluation results show that the complete head generated based on the conditional diffusion model outperforms traditional template fitting methods in terms of structural integrity, geometric accuracy, and facial feature fidelity. It can effectively infer the reasonable shape of unobserved areas and supports real-time 3D reconstruction and downstream applications (such as virtual try-on and digital human head modeling). In addition, the module adopts a lightweight design and can complete inference on a single GPU, significantly reducing the deployment threshold.

[0053] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A neural network-based 3D head reconstruction method, characterized by, Includes the following steps: (1) A unified latent representation modeling mechanism for three-dimensional heads based on VAE is used to model multi-source three-dimensional head data and construct a three-dimensional head VAE model; (2) A conditional complete head structure generation mechanism based on a diffusion model is adopted to achieve progressive recovery from local facial latent representation to complete head latent vector.

2. The neural network-based 3D head reconstruction method of claim 1, wherein, Step (1) Before proceeding with modeling, the multi-source 3D head data undergoes unified preprocessing, including the following steps: Coordinate normalization: Convert 3D meshes or point clouds from different acquisition sources to a standard coordinate system and perform scale normalization. Topology unification: Resampling the 3D mesh to keep the number of vertices consistent with the topology; Pose alignment: Rigid registration based on key anatomical landmarks eliminates the interference of pose differences on morphological modeling; Unified data format: Convert point cloud or grid data into a unified tensor representation.

3. The neural network-based 3D head reconstruction method of claim 1, wherein, The 3D head VAE model includes an encoder network and a decoder network; the encoder network is used to compress high-dimensional 3D head structure data into low-dimensional continuous latent representations, and the decoder network is used to decode the latent vectors back into the complete 3D head structure.

4. The 3D head reconstruction method based on neural networks according to claim 3, characterized in that, The encoder network performs multi-layer feature extraction and aggregation on the input 3D point cloud or mesh data, gradually transitioning from local geometric features to overall morphological features. Through feature fusion and global representation mapping, it finally outputs the distribution parameters of latent variables, which are used to characterize the probability distribution of the 3D head structure in the latent space.

5. The 3D head reconstruction method based on neural networks according to claim 3, characterized in that, The decoder network first expands the latent vectors with features, and then gradually recovers the three-dimensional spatial coordinate information through a multi-layer structure reconstruction module, outputting a point cloud or mesh representation of the complete head.

6. The 3D head reconstruction method based on neural networks according to claim 3, characterized in that, In step (2), the latent vector of the local face is used as a conditional constraint input to the conditional diffusion model. Through the conditional guidance mechanism, the generation process infers the overall head structure while maintaining facial consistency.

7. The 3D head reconstruction method based on neural networks according to claim 6, characterized in that, Loss function of conditional diffusion model : in, Represents the original latent vector In the The result after adding noise to the step time. This represents the latent vectors of a portion of the faces encoded by VAE, which serve as conditional information. This represents the neural network inside the diffusion. The expected value represents the average over all cases. This represents the actual Gaussian noise added.

8. A system for implementing the neural network-based 3D head reconstruction method of claim 1, characterized in that, include: The data construction and preprocessing module is used to standardize multi-source 3D head data, including coordinate normalization, scale unification, topology consistency, pose alignment and data format unification, in order to construct standard input data that meets the requirements of network training. The VAE-based unified latent representation learning module for 3D heads is used to compress high-dimensional 3D head structure data into low-dimensional continuous latent representations and learn the unified probability latent distribution of the complete head structure. The complete head structure generation module based on the conditional diffusion model is used to perform probabilistic generation inference in a unified latent space with local facial latent representation as a condition, gradually recover the complete head latent representation, and decode and generate the complete 3D head structure.

9. A computer device comprising a memory and a processor, the memory being electrically connected to the processor, the memory storing a computer program, characterized in that: When the computer program is executed by the processor, it causes the processor to implement the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor implements the method as described in any one of claims 1 to 8.

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