Robust 3D Self-Portraits in Seconds

Zhe Li, Tao Yu, Chuanyu Pan, Zerong Zheng, Yebin Liu; The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1344-1353

Abstract


In this paper, we propose an efficient method for robust 3D self-portraits using a single RGBD camera. Benefiting from the proposed PIFusion and lightweight bundle adjustment algorithm, our method can generate detailed 3D self-portraits in seconds and shows the ability to handle subjects wearing extremely loose clothes. To achieve highly efficient and robust reconstruction, we propose PIFusion, which combines learning-based 3D recovery with volumetric non-rigid fusion to generate accurate sparse partial scans of the subject. Moreover, a non-rigid volumetric deformation method is proposed to continuously refine the learned shape prior. Finally, a lightweight bundle adjustment algorithm is proposed to guarantee that all the partial scans can not only "loop" with each other but also remain consistent with the selected live key observations. The results and experiments show that the proposed method achieves more robust and efficient 3D self-portraits compared with state-of-the-art methods.

Related Material


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[bibtex]
@InProceedings{Li_2020_CVPR,
author = {Li, Zhe and Yu, Tao and Pan, Chuanyu and Zheng, Zerong and Liu, Yebin},
title = {Robust 3D Self-Portraits in Seconds},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}