We present a real-time multi-person 3D pose estimation approach that does not use paired supervision.
Project page: https://sites.google.com/view/multiperson3D
arXiv: https://arxiv.org/abs/2008.01388
Authors:
Jogendra Nath Kundu, Ambareesh Revanur, Govind V Waghmare, Rahul M V, R. Venkatesh Babu
Indian Institute of Science, Bengaluru
Abstract:
We present a deployment friendly, fast bottom-up framework for multi-person 3D human pose estimation. We adopt a novel neural representation of multi-person 3D pose which unifies the position of person instances with their corresponding 3D pose representation. This is realized by learning a generative pose embedding which not only ensures plausible 3D pose predictions, but also eliminates the usual keypoint grouping operation as employed in prior bottom-up approaches. Further, we propose a practical deployment paradigm where paired 2D or 3D pose annotations are unavailable. In the absence of any paired supervision, we leverage a frozen network, as a teacher model, which is trained on an auxiliary task of multi-person 2D pose estimation. We cast the learning as a cross-modal alignment problem and propose training objectives to realize a shared latent space between two diverse modalities. We aim to enhance the model's ability to perform beyond the limiting teacher network by enriching the latent-to-3D pose mapping using artificially synthesized multi-person 3D scene samples. Our approach not only generalizes to in-the-wild images, but also yields a superior trade-off between speed and performance, compared to prior top-down approaches. Our approach also yields state-of-the-art multi-person 3D pose estimation performance among the bottom-up approaches under consistent supervision levels.
Citation:
@misc{kundu2020unsupervised,
title={Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation},
author={Jogendra Nath Kundu and Ambareesh Revanur and Govind Vitthal Waghmare and Rahul Mysore Venkatesh and R. Venkatesh Babu},
year={2020},
eprint={2008.01388},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
