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
no code implementations • 15 Mar 2022 • Susmit Agrawal, Prabhat Kumar, Siddharth Seth, Toufiq Parag, Maneesh Singh, Venkatesh Babu
Recent algorithms for image manipulation detection almost exclusively use deep network models.
no code implementations • 24 Jun 2020 • Jogendra Nath Kundu, Siddharth Seth, Rahul M. V, Mugalodi Rakesh, R. Venkatesh Babu, Anirban Chakraborty
However, generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable, as these models often perform unsatisfactorily on unseen in-the-wild environments.
no code implementations • CVPR 2020 • Jogendra Nath Kundu, Siddharth Seth, Varun Jampani, Mugalodi Rakesh, R. Venkatesh Babu, Anirban Chakraborty
Camera captured human pose is an outcome of several sources of variation.