IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-View Human Reconstruction
Abstract
We propose IntegratedPIFu, a new pixel-aligned implicit model that builds on the foundation set by PIFuHD. IntegratedPIFu shows how depth and human parsing information can be predicted and capitalized upon in a pixel-aligned implicit model. In addition, IntegratedPIFu introduces depth-oriented sampling, a novel training scheme that improve any pixel-aligned implicit model’s ability to reconstruct important human features without noisy artefacts. Lastly, IntegratedPIFu presents a new architecture that, despite using less model parameters than PIFuHD, is able to improves the structural correctness of reconstructed meshes. Our results show that IntegratedPIFu significantly outperforms existing state-of-the-arts methods on single-view human reconstruction. We provide the code in our supplementary materials. Our code is available at https://github.com/kcyt/IntegratedPIFu.
Cite
Text
Chan et al. "IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-View Human Reconstruction." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-20086-1_19Markdown
[Chan et al. "IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-View Human Reconstruction." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/chan2022eccv-integratedpifu/) doi:10.1007/978-3-031-20086-1_19BibTeX
@inproceedings{chan2022eccv-integratedpifu,
title = {{IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-View Human Reconstruction}},
author = {Chan, Kennard Yanting and Lin, Guosheng and Zhao, Haiyu and Lin, Weisi},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-20086-1_19},
url = {https://mlanthology.org/eccv/2022/chan2022eccv-integratedpifu/}
}