PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
Creative Technologies (United States) · Southern California University for Professional Studies · +3 more institutions
Abstract
We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu produces high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory…
Citation impact
- FWCI
- 117.69
- Percentile
- 100%
- References
- 94
Authors
6- SSShunsuke SaitoCorresponding
Creative Technologies (United States), Southern California University for Professional Studies, USC Institute for Creative Technologies, University of Southern California
- ZHZeng Huang
University of Southern California, USC Institute for Creative Technologies, Creative Technologies (United States), Southern California University for Professional Studies
- RNRyota Natsume
Waseda University
- SMShigeo Morishima
Waseda University
- HLHao Li
Creative Technologies (United States), Southern California University for Professional Studies, USC Institute for Creative Technologies, University of Southern California
Topics & keywords
- Computer science
- Artificial intelligence
- Pixel
- Digitization
- Computer vision
- Benchmark (surveying)
- Representation (politics)
- Context (archaeology)