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Poster
Non-local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Jogendra Nath Kundu · Siddharth Seth · Anirudh Jamkhandi · Pradyumna YM · Varun Jampani · Anirban Chakraborty · Venkatesh Babu R

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ Virtual
in Poster Session 1 »

Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domains, acquiring such supervision for each new target environment is highly inconvenient. To this end, we cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target. We propose to infer image-to-pose via two explicit mappings viz. image-to-latent and latent-to-pose where the latter is a pre-learned decoder obtained from a prior-enforcing generative adversarial auto-encoder. Next, we introduce relation distillation as a means to align the unpaired cross-modal samples i.e., the unpaired target videos and unpaired 3D pose sequences. To this end, we propose a new set of non-local relations in order to characterize long-range latent pose interactions, unlike general contrastive relations where positive couplings are limited to a local neighborhood structure. Further, we provide an objective way to quantify non-localness in order to select the most effective relation set. We evaluate different self-adaptation settings and demonstrate state-of-the-art 3D human pose estimation performance on standard benchmarks.

Keywords: Vision · Domain Adaptation

Author Information

Jogendra Nath Kundu (Indian Institute of Science)
Siddharth Seth (Intel)
Anirudh Jamkhandi (Indian Institute of Science)
Pradyumna YM (PESIT, Bangalore, India)
Varun Jampani (Google)
Anirban Chakraborty (Indian Institute of Science)
Venkatesh Babu R (Indian Institute of Science)

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