使用四面体约束高斯泼溅创建可编辑的3D真实感虚拟化身
Creating Your Editable 3D Photorealistic Avatar with Tetrahedron-constrained Gaussian Splatting
AI总结：
提出基于四面体约束高斯泼溅（TetGS）的框架，通过解耦局部空间适应与真实外观学习，从单目视频生成具有精确区域定位和真实渲染的可编辑3D虚拟化身。
摘要：
个性化3D虚拟化身编辑因其用户友好性以及在AR/VR和虚拟试衣等应用中的可用性而具有广阔前景。以往的研究探索了3D编辑的可行性，但往往难以生成视觉上令人愉悦的结果，这可能是由于在复杂重建场景下几何与纹理混合优化中表征学习的不稳定所致。在本文中，我们旨在为普通用户提供一种易于使用的解决方案，以创建具有精确区域定位、几何适应性和真实感渲染的可编辑3D虚拟化身。为应对这一挑战，我们引入了一个精心设计的框架，利用混合的四面体约束高斯泼溅（TetGS）作为底层表示，将编辑过程解耦为局部空间适应和真实外观学习。TetGS结合了四面体网格的可控显式结构与3D Gaussian Splatting的高精度渲染能力，并以包含三个阶段的渐进方式进行优化：从真实世界单目视频进行3D虚拟化身实例化，为TetGS初始化提供准确的先验；利用显式划分的四面体进行局部空间适应，以指导高斯核的重新分布；以及使用从粗到细的激活策略进行基于几何的外观生成。定性和定量实验均证明了我们的方法在生成真实感3D可编辑虚拟化身方面的有效性和优越性。
英文摘要：
Personalized 3D avatar editing holds significant promise due to its user-friendliness and availability to applications such as AR/VR and virtual try-ons. Previous studies have explored the feasibility of 3D editing, but often struggle to generate visually pleasing results, possibly due to the unstable representation learning under mixed optimization of geometry and texture in complicated reconstructed scenarios. In this paper, we aim to provide an accessible solution for ordinary users to create their editable 3D avatars with precise region localization, geometric adaptability, and photorealistic renderings. To tackle this challenge, we introduce a meticulously designed framework that decouples the editing process into local spatial adaptation and realistic appearance learning, utilizing a hybrid Tetrahedron-constrained Gaussian Splatting (TetGS) as the underlying representation. TetGS combines the controllable explicit structure of tetrahedral grids with the high-precision rendering capabilities of 3D Gaussian Splatting and is optimized in a progressive manner comprising three stages: 3D avatar instantiation from real-world monocular videos to provide accurate priors for TetGS initialization; localized spatial adaptation with explicitly partitioned tetrahedrons to guide the redistribution of Gaussian kernels; and geometry-based appearance generation with a coarse-to-fine activation strategy. Both qualitative and quantitative experiments demonstrate the effectiveness and superiority of our approach in generating photorealistic 3D editable avatars.
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。
