Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture
(2406.00440)Abstract
4D head capture aims to generate dynamic topological meshes and corresponding texture maps from videos, which is widely utilized in movies and games for its ability to simulate facial muscle movements and recover dynamic textures in pore-squeezing. The industry often adopts the method involving multi-view stereo and non-rigid alignment. However, this approach is prone to errors and heavily reliant on time-consuming manual processing by artists. To simplify this process, we propose Topo4D, a novel framework for automatic geometry and texture generation, which optimizes densely aligned 4D heads and 8K texture maps directly from calibrated multi-view time-series images. Specifically, we first represent the time-series faces as a set of dynamic 3D Gaussians with fixed topology in which the Gaussian centers are bound to the mesh vertices. Afterward, we perform alternative geometry and texture optimization frame-by-frame for high-quality geometry and texture learning while maintaining temporal topology stability. Finally, we can extract dynamic facial meshes in regular wiring arrangement and high-fidelity textures with pore-level details from the learned Gaussians. Extensive experiments show that our method achieves superior results than the current SOTA face reconstruction methods both in the quality of meshes and textures. Project page: https://xuanchenli.github.io/Topo4D/.
Overview
-
Topo4D introduces a novel framework for automatic 4D head capture, aiming to generate high-fidelity dynamic facial assets with minimal manual intervention.
-
The framework utilizes a dynamic 3D Gaussian Mesh initialized with multi-view stereo and iterative closest point methods, along with a two-step optimization process for geometry and texture refinement.
-
Topo4D's experimental results demonstrate superior mesh and texture quality compared to state-of-the-art methods, with applications in gaming, movies, AR/VR, and more.
Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture
"Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture" by Li et al. presents a novel framework for automatic 4D head capture designed to simplify and enhance the process of dynamic facial asset generation. The paper introduces several innovative techniques to overcome the limitations of existing methods which rely heavily on manual intervention and are prone to errors.
Summary and Methodology
The proposed framework, Topo4D, aims to automatically generate dynamic topological meshes and corresponding 8K texture maps directly from calibrated multi-view time-series images. The significant contributions include the introduction of a Gaussian Mesh and a strategy for alternative geometry and texture optimization.
Gaussian Mesh Initialization
The authors initialize the head model by representing the set of time-series faces as dynamic 3D Gaussians bound to mesh vertices. This is referred to as the Gaussian Mesh. Gaussians are initialized using the results from multi-view stereo (MVS) and iterative closest point (ICP) methods on the first frame. The initialization stage involves optimizing the attributes of these Gaussians for high-fidelity rendering while maintaining the predefined topological structure.
Geometry and Texture Optimization
For each time frame, alternative geometry and texture optimization is performed:
- Geometry Optimization: Geometry-related attributes of Gaussians are optimized to maintain temporal topological stability, employing an optimization process that includes physical and topological priors to ensure consistent mesh structures across frames.
- Texture Optimization: Following geometry refinement, dense texture learning is performed using high-resolution images. The innovative UV Space Densification technique allows for the generation of fine-grained textures while maintaining the topological coherence established during geometry optimization.
The final output results in highly detailed dynamic facial meshes and 8K texture maps.
Experimental Results
The efficacy of Topo4D is validated through extensive experimental comparisons. The results demonstrate superior performance in both mesh and texture quality. For instance, Topo4D achieves significantly lower mesh-to-scan errors compared to state-of-the-art methods such as DECA, 3DDFA, HRN, and DFNRMVS. Furthermore, Topo4D's textures boast pore-level details, with a texture resolution surpassing that of competitive up-sampling and texture generation methods like UnsupTex and HRN.
Implications and Future Work
The implications of Topo4D span both practical and theoretical domains:
- Practical Applications: The ability to automate and expedite the process of 4D facial capture with high fidelity makes Topo4D suitable for industries such as gaming, movies, AR/VR, and any domain requiring realistic and dynamic facial animations.
- Theoretical Contributions: The integration of Gaussian-based representation with topological constraints offers a novel approach to dynamic scene representation, potentially influencing future research in 4D reconstruction and neural scene representations.
Looking forward, there are several promising directions for future developments:
- High-Fidelity Full-Head Capture: Extending the principles of Topo4D to capture entire head models, including hair and detailed ear structures, could immensely benefit virtual avatar creation.
- Incorporation of PBR Textures: Introducing physically-based rendering (PBR) textures that respond to lighting variations could further enhance the realism of the captured models.
- Real-Time Processing: Optimizing the framework for real-time applications could open new vistas in interactive entertainment and virtual reality.
Overall, Topo4D constitutes a significant advancement in 4D facial capture technology, providing a robust and efficient framework for generating high-fidelity dynamic facial assets with high temporal coherence and unparalleled texture detail. The framework's emphasis on topology preservation and temporal stability marks an essential step forward in the pursuit of automated and high-quality 4D facial modeling.
Create an account to read this summary for free: