ARAH: Animatable Volume Rendering of Articulated Human SDFs

Shaofei Wang, Katja Schwarz, Andreas Geiger, Siyu Tang
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引用次数: 55

Abstract

Combining human body models with differentiable rendering has recently enabled animatable avatars of clothed humans from sparse sets of multi-view RGB videos. While state-of-the-art approaches achieve realistic appearance with neural radiance fields (NeRF), the inferred geometry often lacks detail due to missing geometric constraints. Further, animating avatars in out-of-distribution poses is not yet possible because the mapping from observation space to canonical space does not generalize faithfully to unseen poses. In this work, we address these shortcomings and propose a model to create animatable clothed human avatars with detailed geometry that generalize well to out-of-distribution poses. To achieve detailed geometry, we combine an articulated implicit surface representation with volume rendering. For generalization, we propose a novel joint root-finding algorithm for simultaneous ray-surface intersection search and correspondence search. Our algorithm enables efficient point sampling and accurate point canonicalization while generalizing well to unseen poses. We demonstrate that our proposed pipeline can generate clothed avatars with high-quality pose-dependent geometry and appearance from a sparse set of multi-view RGB videos. Our method achieves state-of-the-art performance on geometry and appearance reconstruction while creating animatable avatars that generalize well to out-of-distribution poses beyond the small number of training poses.
ARAH:铰接式人体sdf的可动画体渲染
将人体模型与可微分渲染相结合,最近可以从稀疏的多视图RGB视频集中生成穿着衣服的人的动画化身。虽然最先进的方法可以通过神经辐射场(NeRF)实现逼真的外观,但由于缺少几何约束,推断的几何形状往往缺乏细节。此外,由于从观察空间到规范空间的映射不能忠实地推广到看不见的姿势,因此在非分布姿势中动画化身尚不可能。在这项工作中,我们解决了这些缺点,并提出了一个模型来创建具有详细几何形状的可动画化的穿着的人类化身,该模型可以很好地推广到分布外的姿势。为了获得详细的几何图形,我们将铰接的隐式表面表示与体绘制结合起来。为了推广,我们提出了一种同时进行射线面相交搜索和对应搜索的联合寻根算法。我们的算法能够实现高效的点采样和精确的点规范化,同时很好地推广到看不见的姿势。我们证明了我们提出的管道可以从一组稀疏的多视图RGB视频中生成具有高质量姿态相关几何形状和外观的穿着头像。我们的方法在几何和外观重建方面实现了最先进的性能,同时创建了可动画的化身,这些化身可以很好地推广到超出少量训练姿势的非分布姿势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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