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。