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Unsupervised Cross-Modal Alignment for Multi-person 3D Pose Estimation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12358)

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.

Notes

Acknowledgement

This project is supported by a Indo-UK Joint Project (DST/INT/UK/P-179/2017), DST, Govt. of India and a WIRIN project.

Supplementary material

504454_1_En_3_MOESM1_ESM.pdf (7.5 mb)
Supplementary material 1 (pdf 7639 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Video Analytics LabIndian Institute of ScienceBangaloreIndia

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