3D Reconstruction; Shape Completion; Texture-Inpainting; Implicit Function; Signed Distance Function
Résumé :
[en] 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 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.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Computer Vision Imaging & Machine Intelligence (CVI²)
Disciplines :
Sciences informatiques
Auteur, co-auteur :
KARADENIZ, Ahmet Serdar ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
ALI, Sk Aziz ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
KACEM, Anis ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
DUPONT, Elona ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > CVI2
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network
Date de publication/diffusion :
2022
Nom de la manifestation :
European Conference on Computer Vision Workshops
Lieu de la manifestation :
Tel-Aviv, Israël
Date de la manifestation :
from 23-10-2022 to 27-10-2022
Titre de l'ouvrage principal :
TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network
SHARP 2021, the 2nd shape recovery from partial textured 3d scans. https://cvi2. uni.lu/sharp2021/. Accessed 23 July 2022
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SHARP 2022, the 3rd shape recovery from partial textured 3d scans. https://cvi2. uni.lu/sharp2022/. Accessed 23 July 2022
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