Dynamic Surface Function Networks for Clothed Human Bodies
2021
Conference Paper
ncs
We present a novel method for temporal coherent reconstruction and tracking of clothed humans. Given a monocular RGB-D sequence, we learn a person-specific body model which is based on a dynamic surface function network. To this end, we explicitly model the surface of the person using a multi-layer perceptron (MLP) which is embedded into the canonical space of the SMPL body model. With classical forward rendering, the represented surface can be rasterized using the topology of a template mesh. For each surface point of the template mesh, the MLP is evaluated to predict the actual surface location. To handle pose-dependent deformations, the MLP is conditioned on the SMPL pose parameters. We show that this surface representation as well as the pose parameters can be learned in a self-supervised fashion using the principle of analysis-by-synthesis and differentiable rasterization. As a result, we are able to reconstruct a temporally coherent mesh sequence from the input data. The underlying surface representation can be used to synthesize new animations of the reconstructed person including pose-dependent deformations.
Author(s): | Andrei Burov and Matthias Nießner and Justus Thies |
Book Title: | Proc. International Conference on Computer Vision (ICCV) |
Year: | 2021 |
Month: | October |
Department(s): | Neural Capture and Synthesis |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | International Conference on Computer Vision 2021 |
Event Place: | virtual (originally Montreal, Canada) |
BibTex @inproceedings{burov2021dsfn, title = {Dynamic Surface Function Networks for Clothed Human Bodies}, author = {Burov, Andrei and Nie{\ss}ner, Matthias and Thies, Justus}, booktitle = {Proc. International Conference on Computer Vision (ICCV)}, month = oct, year = {2021}, doi = {}, month_numeric = {10} } |