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

peer reviewedReconstructing 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 highresolution texture completion – TSCom-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 [37 , 2]) 2022 1 challenge1.FREE-3D: Feature-based Reverse Engineering Of 3D Scan