发表机构
School of Software and BNRist, Tsinghua University（清华大学软件学院及清华-伯克利深圳学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数字头像创建等需求，UVFaceFusion提出用UV空间神经融合取代启发式拓扑优化，通过VGGT和Pixel3DMM获取数据，经神经融合网络预测点图，直接采样出固定拓扑网格，在多基准测试和野外数据上有很好表现，重建精度高且速度快。
AI 中文摘要
重建具有指定拓扑结构的高保真面部几何形状对于数字头像创建和动画制作至关重要，但现有的自动化方法常常在几何保真度和野外泛化能力之间进行权衡。我们提出了UVFaceFusion，这是一个用于从日常图像进行多视图、固定拓扑面部重建的前馈框架。我们的关键思想是在规范的UV空间中用可学习的神经融合取代启发式拓扑优化。给定多视图图像，我们首先分别使用VGGT和Pixel3DMM获得每个视图的密集点图和面部UV对应关系。然后，将特定视图的点图提升到规范的UV域，并与一个新颖且具有掩码感知能力的神经融合网络进行融合。该网络预测一个完整的UV空间点图，从中直接采样出固定拓扑的网格。尽管仅在Ava - 256上进行训练，但UVFaceFusion由于其规范的UV空间几何到几何的融合减少了对特定数据集外观和捕获条件的依赖，因此能很好地推广到多个公共基准测试和野外捕获数据。在各种基准测试上进行的实验表明，UVFaceFusion在单个RTX 4090上从16个输入视图重建网格时，不到3秒就能达到最先进的重建精度。代码可在该https URL获取。
英文摘要
Reconstructing high-fidelity facial geometry with an assigned topology is essential for digital avatar creation and animation, yet existing automated methods often trade off geometric fidelity and in-the-wild generalization. We present UVFaceFusion, a feed-forward framework for multi-view, fixed-topology face reconstruction from daily images. Our key idea is to replace heuristic topological optimization with learnable neural fusion in a canonical UV space. Given multi-view images, we first obtain dense point maps and facial UV correspondences of each view using VGGT and Pixel3DMM, respectively. Then, the view-specific point maps are lifted into the canonical UV domain and fused with a novel mask-aware neural fusion network. The network predicts a complete UV-space point map, from which a fixed-topology mesh is directly sampled. Although trained only on Ava-256, UVFaceFusion generalizes well to multiple public benchmarks and in-the-wild captures, benefiting from its canonical UV-space geometry-to-geometry fusion that reduces dependence on dataset-specific appearance and capture conditions. Experiments on various benchmarks show that UVFaceFusion achieves state-of-the-art reconstruction accuracy while reconstructing a mesh from 16 input views in less than 3 seconds on a single RTX 4090. Code is available at https://github.com/grignarder/UVFaceFusion.
