UVA：面向视图合成、姿态渲染、几何与纹理编辑的统一体积化身
UVA: Towards Unified Volumetric Avatar for View Synthesis, Pose rendering, Geometry and Texture Editing
AI总结：
本文提出统一体积化身UVA，通过分离的神经场与锚节点潜在编码实现化身几何和纹理的局部独立编辑，同时保持新视图合成与新姿态渲染的竞争性能。
摘要：
神经辐射场（NeRF）凭借其高质量的渲染能力，例如在新视图和新姿态方面，已成为人体化身重建中流行的三维表示方法。然而，以往编辑化身几何和外观的方法仅允许通过身体形状参数和二维纹理贴图进行全局编辑。本文提出了一种名为统一体积化身（UVA）的新方法，该方法支持对几何和纹理进行局部且独立的编辑，同时保留渲染新视图和新姿态的能力。UVA 使用蒙皮运动场将每个观测点变换到规范空间，并在分离的神经场中分别表示几何和纹理。每个场由一组结构化潜在编码组成，这些编码附着在规范空间中可变形网格的锚节点上，并通过插值扩散到整个空间，从而实现局部编辑。为解决编码插值中的空间歧义问题，我们使用局部有符号高度指示器。我们还将依赖视图的辐射颜色替换为依赖姿态的着色因子，以更好地表示不同姿态下的表面光照。在多个化身上的实验表明，我们的 UVA 在新视图合成和新姿态渲染方面取得了具有竞争力的结果，同时支持对几何和外观进行局部且独立的编辑。源代码将予以发布。
英文摘要：
Neural radiance field (NeRF) has become a popular 3D representation method for human avatar reconstruction due to its high-quality rendering capabilities, e.g., regarding novel views and poses. However, previous methods for editing the geometry and appearance of the avatar only allow for global editing through body shape parameters and 2D texture maps. In this paper, we propose a new approach named \textbf{U}nified \textbf{V}olumetric \textbf{A}vatar (\textbf{UVA}) that enables local and independent editing of both geometry and texture, while retaining the ability to render novel views and poses. UVA transforms each observation point to a canonical space using a skinning motion field and represents geometry and texture in separate neural fields. Each field is composed of a set of structured latent codes that are attached to anchor nodes on a deformable mesh in canonical space and diffused into the entire space via interpolation, allowing for local editing. To address spatial ambiguity in code interpolation, we use a local signed height indicator. We also replace the view-dependent radiance color with a pose-dependent shading factor to better represent surface illumination in different poses. Experiments on multiple human avatars demonstrate that our UVA achieves competitive results in novel view synthesis and novel pose rendering while enabling local and independent editing of geometry and appearance. The source code will be released.
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。
