Perceiving Systems, Computer Vision

High-Fidelity Clothed Avatar Reconstruction from a Single Image

2023

Conference Paper

ps

This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit model to learn the general shape in the canonical space of a person in a learning-based way, and at the second stage, we refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way. A hyper-network is utilized to generate a good initialization so that the convergence of the optimization process is greatly accelerated. Extensive experiments on various datasets show that the proposed CAR successfully produces high-fidelity avatars for arbitrarily clothed humans in real scenes.

Author(s): Tingting Liao and Xiaomei Zhang and Yuliang Xiu and Hongwei Yi and Xudong Liu and Guo-Jun Qi and Yong Zhang and Xuan Wang and Xiangyu Zhu and Zhen Lei
Book Title: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR)
Pages: 8662--8672
Year: 2023
Month: June
Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference
Event Name: CVPR 2023
Event Place: Vancouver
State: Accepted
URL: https://tingtingliao.github.io/CAR/
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BibTex 

@inproceedings{car2023liao,
  title = {High-Fidelity Clothed Avatar Reconstruction from a Single Image},
  author = {Liao, Tingting and Zhang, Xiaomei and Xiu, Yuliang and Yi, Hongwei and Liu, Xudong and Qi, Guo-Jun and Zhang, Yong and Wang, Xuan and Zhu, Xiangyu and Lei, Zhen},
  booktitle = {IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
  pages = {8662--8672},
  month = jun,
  year = {2023},
  doi = {},
  url = {https://tingtingliao.github.io/CAR/},
  month_numeric = {6}
}