Reconstructing 3D human body shapes from 3D partial textured scans remains a
fundamental task for many computer vision and graphics applications -- e.g.,
body animation, and virtual dressing. We propose a new neural network
architecture for 3D body shape and high-resolution texture completion --
BCom-Net -- that can reconstruct the full geometry from mid-level to high-level
partial input scans. We decompose the overall reconstruction task into two
stages - first, a joint implicit learning network (SCom-Net and TCom-Net) that
takes a voxelized scan and its occupancy grid as input to reconstruct the full
body shape and predict vertex textures. Second, a high-resolution texture
completion network, that utilizes the predicted coarse vertex textures to
inpaint the missing parts of the partial 'texture atlas'. A thorough
experimental evaluation on 3DBodyTex.V2 dataset shows that our method achieves
competitive results with respect to the state-of-the-art while generalizing to
different types and levels of partial shapes. The proposed method has also
ranked second in the track1 of SHApe Recovery from Partial textured 3D scans
(SHARP [38,1]) 2022 challenge1.

利用 BCom-Net 神经网络构建了一个两阶段的方法来实现从部分输入的 3D 扫描中完整地重建出身体形态和纹理，并在 SHARP 2022 挑战中获得了第二名。

TSCom-Net: 粗到细的三维纹理形状补全网络

TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network

Existing inpainting methods have achieved promising performance in recovering
defected images of specific scenes. However, filling holes involving multiple
semantic categories remains challenging due to the obscure semantic boundaries
and the mixture of different semantic textures. In this paper, we introduce
coherence priors between the semantics and textures which make it possible to
concentrate on completing separate textures in a semantic-wise manner.
Specifically, we adopt a multi-scale joint optimization framework to first
model the coherence priors and then accordingly interleavingly optimize image
inpainting and semantic segmentation in a coarse-to-fine manner. A
Semantic-Wise Attention Propagation (SWAP) module is devised to refine
completed image textures across scales by exploring non-local semantic
coherence, which effectively mitigates mix-up of textures. We also propose two
coherence losses to constrain the consistency between the semantics and the
inpainted image in terms of the overall structure and detailed textures.
Experimental results demonstrate the superiority of our proposed method for
challenging cases with complex holes.

本文提出了一种基于语义和纹理的一致性先验的图片修复方法，采用多尺度联合优化框架，通过 Semantic-Wise Attention Propagation 模块和两个一致性损失函数对图像语义和纹理进行逐层完善，在填补涉及多个语义类别的空洞时展现出优越性。

由语义和纹理的连贯性先验引导下的图像修复

Image Inpainting Guided by Coherence Priors of Semantics and Textures

Prior work to infer 3D texture use either texture atlases, which require
uv-mappings and hence have discontinuities, or colored voxels, which are memory
inefficient and limited in resolution. Recent work, predicts RGB color at every
XYZ coordinate forming a texture field, but focus on completing texture given a
single 2D image. Instead, we focus on 3D texture and geometry completion from
partial and incomplete 3D scans. IF-Nets have recently achieved
state-of-the-art results on 3D geometry completion using a multi-scale deep
feature encoding, but the outputs lack texture. In this work, we generalize
IF-Nets to texture completion from partial textured scans of humans and
arbitrary objects. Our key insight is that 3D texture completion benefits from
incorporating local and global deep features extracted from both the 3D partial
texture and completed geometry. Specifically, given the partial 3D texture and
the 3D geometry completed with IF-Nets, our model successfully in-paints the
missing texture parts in consistence with the completed geometry. Our model won
the SHARP ECCV'20 challenge, achieving highest performance on all challenges.

本研究旨在实现通过 IF-Nets 模型进行基于局部和全局深度特征提取，从部分三维纹理和已完成几何体上完成 3D 纹理的完整。我们的模型成功地用靠近已完成几何体的内插法来处理丢失的纹理部分，并在 SHARP ECCV20 挑战中取得了最高表现。