Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction

• URL: http://arxiv.org/abs/2103.16449v1
• Date: Tue, 30 Mar 2021 15:47:58 GMT
• Title: Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction
• Authors: Shanyan Guan, Jingwei Xu, Yunbo Wang, Bingbing Ni, Xiaokang Yang
• Abstract summary: This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos. We propose Bilevel Online Adaptation, which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks.
• Score: 94.25865526414717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model \cite{loper2015smpl} underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backgrounds, and occlusions. Our general idea is to dynamically fine-tune the source model on test video streams with additional temporal constraints, such that it can mitigate the domain gaps without over-fitting the 2D information of individual test frames. A subsequent challenge is how to avoid conflicts between the 2D and temporal constraints. We propose to tackle this problem using a new training algorithm named Bilevel Online Adaptation (BOA), which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training iteration. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks.

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