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Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis
Camera captured human pose is an outcome of several sources of variation.
Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation
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.
SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization
Recent algorithms for image manipulation detection almost exclusively use deep network models.
Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation
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
Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
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
