Paper Info
Title
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
Abstract
Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution. Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning. This provides context to an fine level which estimates highly detailed geometry by observing higher-resolution images. We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images.
Year
DOI
Venue
2020
10.1109/CVPR42600.2020.00016
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Keywords
DocType
ISSN
higher-resolution images,single image human shape reconstruction,multilevel pixel-aligned implicit function,high-resolution 3D human digitization,image-based 3D human shape estimation,deep neural networks,low resolution images,PIFuHD
Conference
1063-6919
ISBN
Citations 
PageRank 
978-1-7281-7169-2
5
0.40
References 
Authors
20
4
Name
Order
Citations
PageRank
Shunsuke Saito114610.36
Tomas Simon222213.27
Jason M. Saragih3166869.02
Hanbyul Joo41015.17