An end-to-end video pose transformer built on PETR with spatio-temporal encoders and an instance consistency loss reaches 83.0 mAP on PoseTrack2017 and appears around 4x faster than DCPose.
Learning temporal pose esti- mation from sparsely-labeled videos
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An End-to-End Framework for Video Multi-Person Pose Estimation
An end-to-end video pose transformer built on PETR with spatio-temporal encoders and an instance consistency loss reaches 83.0 mAP on PoseTrack2017 and appears around 4x faster than DCPose.