Abstract
Reconstructing complete and animatable 3D human avatars from monocular videos remains challenging, particularly under severe occlusions. While 3D Gaussian Splatting has enabled photorealistic human rendering, existing methods struggle with incomplete observations, often producing corrupted geometry and temporal inconsistencies. We present InpaintHuman, a novel method for generating high-fidelity, complete, and animatable avatars from occluded monocular videos. Our approach introduces two key innovations: (i) a multi-scale UV-parameterized representation with hierarchical coarse-to-fine feature interpolation, enabling robust reconstruction of occluded regions while preserving geometric details; and (ii) an identity-preserving diffusion inpainting module that integrates textual inversion with semantic-conditioned guidance for subject-specific, temporally coherent completion. Unlike SDS-based methods, our approach employs direct pixel-level supervision to ensure identity fidelity. Experiments on synthetic benchmarks (PeopleSnapshot, ZJU-MoCap) and real-world scenarios (OcMotion) demonstrate competitive performance with consistent improvements in reconstruction quality across diverse poses and viewpoints.
Abstract (translated)
从单目视频中重建完整且可动画化的3D人体化身仍然是一项挑战，尤其是在严重遮挡的情况下。尽管3D高斯点阵技术（Gaussian Splatting）已经实现了逼真的3D人物渲染，现有的方法在处理不完整的观察数据时往往存在问题，经常会生成有缺陷的几何结构和时间上的不一致性。我们提出了InpaintHuman这一创新方法，旨在从被遮挡的单目视频中生成高质量、完整且可动画化的化身。我们的方法引入了两个关键创新： (i) 多尺度UV参数化表示以及分层粗到细特征插值，能够稳健地重建被遮挡区域的同时保留几何细节； (ii) 一个保持身份特性的扩散式修补模块，该模块结合文本逆向工程技术与基于语义引导的方式，用于实现针对特定对象的、时间上一致化的完成。 与其他基于SDS（Screen Door Shader）的方法不同的是，我们的方法直接采用像素级别的监督机制来确保身份特征的一致性。在合成基准测试（PeopleSnapshot, ZJU-MoCap）和现实世界场景(OcMotion)上的实验表明，我们的方法具有竞争力，并且在各种姿态和视角下都一致地提高了重建质量。
URL
https://arxiv.org/abs/2601.02098
