Hybrid Neural Rendering for Real-Time 3D Avatar Hair Animation
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Solution Overview
Problem
Existing methods for 3D avatar creation and animation struggle with accurately capturing fine details like hair, require substantial computational resources, and result in low frame rates on devices with limited processing power, leading to unrealistic and inefficient animations.
Innovation Solution
A hybrid neural rendering model is used to generate 3D facial animations from 2D images, incorporating a 3D morphable model for the face and neck, a prism lattice structure for hair, and neural radiance fields, trained to render realistic and expressive avatars in real-time on resource-constrained devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If detailed 3D avatars with fine details like hair are created, then manufacturing precision and realism are improved, but device complexity and computational resource requirements increase
Solution Approach 1:
The patent segments the 3D avatar model into distinct components: a base mesh for the overall structure and separate neural radiance fields for fine details like hair. This segmentation allows each component to be optimized independently, achieving high detail accuracy where needed while keeping the overall model complexity manageable through modular processing.
Solution Approach 2:
The patent applies local quality by using neural radiance fields specifically for regions requiring fine detail representation (such as hair and facial features) while using simpler mesh representations for other areas. This selective application of complexity ensures high manufacturing precision for critical details without uniformly increasing the entire model's complexity.
2Manufacturing precision
If detailed 3D avatars are created, then manufacturing precision is improved, but productivity decreases due to longer processing times
Solution Approach 1:
The patent implements preliminary action by pre-training the neural radiance fields and computing their parameters in advance. During runtime, the system only needs to query pre-computed data structures and perform lightweight rendering operations, significantly improving processing speed while maintaining high detail accuracy in the avatar representation.
Solution Approach 2:
The patent uses copying by creating simplified representations or proxies of the detailed 3D avatar that can be quickly rendered. The neural radiance fields store pre-computed appearance and geometric information that can be efficiently sampled and copied during rendering, avoiding the need for complex real-time calculations while preserving detail accuracy.
3Manufacturing precision
If detailed 3D avatars are created, then manufacturing precision is improved, but loss of time increases due to longer download and storage times
Solution Approach 1:
The patent extracts only the essential parameters and features needed for high-quality avatar rendering, storing them in compact data structures. By taking out only the necessary information (neural radiance field parameters, key mesh vertices, and associated metadata) rather than storing complete high-resolution geometry, the system achieves high manufacturing precision while minimizing data size and download time.
Solution Approach 2:
The patent applies parameter changes by representing detailed 3D avatars not through traditional high-polygon meshes but through neural radiance field parameters. This parameterization approach captures fine details like hair with far fewer data elements, reducing storage requirements and download time while maintaining or improving manufacturing precision through the continuous, differentiable nature of neural field representations.
4Manufacturing precision
If complex models with detailed hair and facial features are used, then manufacturing precision is improved, but use of energy increases due to higher computational requirements
Solution Approach 1:
The patent replaces the traditional mechanical approach of using high-polygon meshes and complex geometric data structures with a neural network-based system. The neural radiance fields use learned representations and gradient-based rendering that are computationally more efficient, reducing energy consumption while achieving superior detail accuracy compared to conventional mesh-based approaches.
Data Source
AI summary
A method for controlling a device can include receiving an input two-dimensional (2D) image, receiving a hybrid three-dimensional (3D) model including a first set of triangles forming a triangular mesh, and a second set of triangles with associated alpha map and neural feature maps, the vertices of both of the first and second sets of triangles including rigging information, and deforming the first and second sets of triangles of the hybrid 3D model based on 3D animation parameters and the rigging information, to generate deformed triangles. The method can further include rendering the deformed triangles based on rendering the first set of triangles using a texture mapping technique and rendering the second set of triangles using deferred neural rendering based on the neural feature maps and the alpha map to generate rendered triangles, and displaying an animated 3D object based on the rendered triangles and the input 2D image.