We present ARCH++, an image-based method to reconstruct 3D avatars with
arbitrary clothing styles. Our reconstructed avatars are animation-ready and
highly realistic, in both the visible regions from input views and the unseen
regions. While prior work shows great promise of reconstructing animatable
clothed humans with various topologies, we observe that there exist fundamental
limitations resulting in sub-optimal reconstruction quality. In this paper, we
revisit the major steps of image-based avatar reconstruction and address the
limitations with ARCH++. First, we introduce an end-to-end point based geometry
encoder to better describe the semantics of the underlying 3D human body, in
replacement of previous hand-crafted features. Second, in order to address the
occupancy ambiguity caused by topological changes of clothed humans in the
canonical pose, we propose a co-supervising framework with cross-space
consistency to jointly estimate the occupancy in both the posed and canonical
spaces. Last, we use image-to-image translation networks to further refine
detailed geometry and texture on the reconstructed surface, which improves the
fidelity and consistency across arbitrary viewpoints. In the experiments, we
demonstrate improvements over the state of the art on both public benchmarks
and user studies in reconstruction quality and realism.

本研究提出了 ARCH++，一种使用图像的方法来重建具有任意服装的三维人物形象，通过引入端到端的基于点的几何编码器、共同监督框架和图像到图像转换网络来解决重建质量中存在的局限性，同时在公共基准测试和用户研究中证明了实现了重建质量和真实性的改进。