most citedNon-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation

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cs.CV20223 cited

Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation

Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi +4

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 domai…

cs.CV2022

Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation

Jogendra Nath Kundu, Siddharth Seth, Pradyumna YM +3

The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in…

cs.CV2022

SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization

Susmit Agrawal, Prabhat Kumar, Siddharth Seth +3

Recent algorithms for image manipulation detection almost exclusively use deep network models. These approaches require either dense pixelwise groundtruth masks, camera ids, or ima…

cs.CV20201 cited

Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation

Jogendra Nath Kundu, Siddharth Seth, Rahul M +3

Estimation of 3D human pose from monocular image has gained considerable attention, as a key step to several human-centric applications. However, generalizability of human pose est…

cs.CV20201 cited

Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis

Jogendra Nath Kundu, Siddharth Seth, Varun Jampani +3

Camera captured human pose is an outcome of several sources of variation. Performance of supervised 3D pose estimation approaches comes at the cost of dispensing with variations, s…