Topo4D: 面向高保真4D头部采集的拓扑保持高斯溅射方法
Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture
AI总结：
针对现有4D头部采集方法误差大、依赖人工的问题，提出Topo4D框架，通过绑定网格顶点的固定拓扑动态3D高斯及逐帧交替优化，从多视图时序图像生成高保真4D头部网格与纹理，效果优于现有SOTA方法。
摘要：
4D头部采集旨在从视频生成动态拓扑网格及对应纹理图，因其能够模拟面部肌肉运动、还原挤毛孔场景下的动态纹理，被广泛应用于影视与游戏领域。业界通常采用包含多视图立体匹配与非刚性配准的方法，但该方法易产生误差，且严重依赖艺术家耗时的人工处理。为简化这一流程，我们提出Topo4D，一种新型的自动几何与纹理生成框架，可直接从经标定的多视图时序图像中优化得到稠密配准的4D头部与8K纹理图。具体而言，我们首先将时序人脸表示为一组具有固定拓扑的动态3D高斯，其中高斯中心绑定到网格顶点。随后，我们逐帧进行几何与纹理的交替优化，在保持时序拓扑稳定性的同时，实现高质量的几何与纹理学习。最终，我们可从学习得到的高斯中提取出具有规则布线排列的动态人脸网格，以及具备毛孔级细节的高保真纹理。大量实验表明，我们的方法在网格质量与纹理质量上均优于当前SOTA人脸重建方法。项目主页：https://xuanchenli.github.io/Topo4D/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
