Learning Self-Shadowing for Clothed Human Bodies
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
This paper proposes to learn self-shadowing on full-body, clothed human postures from monocular colour image input, by supervising a deep neural model. The proposed approach implicitly learns the articulated body shape in order to generate self-shadow maps without seeking to reconstruct explicitly or estimate parametric 3D body geometry. Furthermore, it is generalisable to different people without per-subject pre-training, and has fast inference timings. The proposed neural model is trained on self-shadow maps rendered from 3D scans of real people for various light directions. Inference of shadow maps for a given illumination is performed from only 2D image input. Quantitative and qualitative experiments demonstrate comparable results to the state of the art whilst being monocular and achieving a considerably faster inference time. We provide ablations of our methodology and further show how the inferred self-shadow maps can benefit monocular full-body human relighting.
Description
CCS Concepts: Computing methodologies -> Image-based rendering; Visibility; Neural networks
@inproceedings{10.2312:sr.20241159,
booktitle = {Eurographics Symposium on Rendering},
editor = {Haines, Eric and Garces, Elena},
title = {{Learning Self-Shadowing for Clothed Human Bodies}},
author = {Einabadi, Farshad and Guillemaut, Jean-Yves and Hilton, Adrian},
year = {2024},
publisher = {The Eurographics Association},
ISSN = {1727-3463},
ISBN = {978-3-03868-262-2},
DOI = {10.2312/sr.20241159}
}