no code implementations • 27 Nov 2023 • Sunandini Sanyal, Ashish Ramayee Asokan, Suvaansh Bhambri, Pradyumna YM, Akshay Kulkarni, Jogendra Nath Kundu, R Venkatesh Babu
Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain.
no code implementations • NeurIPS 2021 • Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu
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.
Ranked #5 on Unsupervised 3D Human Pose Estimation on Human3.6M
no code implementations • CVPR 2022 • Jogendra Nath Kundu, Siddharth Seth, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu
The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations.
Ranked #8 on Unsupervised 3D Human Pose Estimation on Human3.6M
Monocular 3D Human Pose Estimation Unsupervised 3D Human Pose Estimation +2