WildGHand: 从单目真实视频中学习抗扰动高斯手姿态体
WildGHand: Learning Anti-Perturbation Gaussian Hand Avatars from Monocular In-the-Wild Videos
AI总结：
WildGHand通过动态扰动解耦模块和扰动感知优化策略，实现从真实视频中抗扰动的高斯手姿态体生成，取得多项指标上的显著提升。
摘要：
尽管在从单目视频中进行3D手形重建方面取得了最近的进步，但大多数现有方法依赖于在受控环境中捕获的数据，因此在现实世界中受到严重干扰的设置下表现不佳，例如手-物体交互、极端姿势、光照变化和运动模糊。为了解决这些问题，我们引入了WildGHand，一种基于优化的框架，能够在真实视频中实现自适应的3D高斯点撒并在生成高保真手姿态体。WildGHand包含两个关键组件：(i)一个动态扰动解耦模块，该模块在优化过程中明确地将扰动表示为时间变化的偏置在3D高斯属性上；(ii)一个扰动感知的优化策略，该策略生成每帧各向异性加权掩码以指导优化。这些组件共同作用，使该框架能够在空间和时间维度上识别并抑制扰动。我们进一步整理了一个在多种扰动下捕获的单目手视频数据集以基准测试真实手姿态体重建。在该数据集和两个公开数据集上的广泛实验表明，WildGHand在多个指标上实现了最先进的性能，并在多个指标上显著优于其基础模型（例如，PSNR相对增益高达15.8%，LPIPS相对减少23.1%）。我们的实现和数据集可在https://github.com/XuanHuang0/WildGHand上获得。
英文摘要：
Despite recent progress in 3D hand reconstruction from monocular videos, most existing methods rely on data captured in well-controlled environments and therefore degrade in real-world settings with severe perturbations, such as hand-object interactions, extreme poses, illumination changes, and motion blur. To tackle these issues, we introduce WildGHand, an optimization-based framework that enables self-adaptive 3D Gaussian splatting on in-the-wild videos and produces high-fidelity hand avatars. WildGHand incorporates two key components: (i) a dynamic perturbation disentanglement module that explicitly represents perturbations as time-varying biases on 3D Gaussian attributes during optimization, and (ii) a perturbation-aware optimization strategy that generates per-frame anisotropic weighted masks to guide optimization. Together, these components allow the framework to identify and suppress perturbations across both spatial and temporal dimensions. We further curate a dataset of monocular hand videos captured under diverse perturbations to benchmark in-the-wild hand avatar reconstruction. Extensive experiments on this dataset and two public datasets demonstrate that WildGHand achieves state-of-the-art performance and substantially improves over its base model across multiple metrics (e.g., up to a $15.8\%$ relative gain in PSNR and a $23.1\%$ relative reduction in LPIPS). Our implementation and dataset are available at https://github.com/XuanHuang0/WildGHand.
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。
