Kundu, JN and Revanur, A and Waghmare, GV and Venkatesh, RM and Babu, RV (2020) Unsupervised Cross-Modal Alignment for Multi-person 3D Pose Estimation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 23-28 August 2020, Glasgow, pp. 35-52.
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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.
| Item Type: | Conference Proceedings |
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| Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Alignment; Computer vision; Economic and social effects, 3D human pose estimation; 3D pose estimation; Alignment Problems; Bottom up approach; Bottom-up frameworks; Grouping operations; Neural representations; Top down approaches, Modal analysis |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 23 Jan 2023 10:07 |
| Last Modified: | 23 Jan 2023 10:07 |
| URI: | https://eprints.iisc.ac.in/id/eprint/79263 |
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