This task aims to solve root-relative 3D multi-person pose estimation. No human bounding box and root joint coordinate groundtruth are used in testing time.
( Image credit: RootNet )
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Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case.
Ranked #2 on Root Joint Localization on Human3.6M
3D ABSOLUTE HUMAN POSE ESTIMATION 3D MULTI-PERSON POSE ESTIMATION (ABSOLUTE) 3D MULTI-PERSON POSE ESTIMATION (ROOT-RELATIVE) MONOCULAR 3D HUMAN POSE ESTIMATION MULTI-PERSON POSE ESTIMATION ROOT JOINT LOCALIZATION
Through a body-center-guided sampling process, the body mesh parameters of all people in the image can be easily extracted from the Mesh Parameter map.
Ranked #1 on 3D Human Pose Estimation on 3DPW (using extra training data)
In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimations, we present an end-to-end solution which directly operates in the $3$D space, therefore avoids making incorrect decisions in the 2D space.
Ranked #1 on 3D Multi-Person Pose Estimation on Campus
To further verify the scalability of our method, we propose a new large-scale multi-human dataset with 12 to 28 camera views.
Ranked #2 on 3D Multi-Person Pose Estimation on Campus
To tackle this problem, we propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses that do not require camera parameters.
3D ABSOLUTE HUMAN POSE ESTIMATION 3D MULTI-PERSON POSE ESTIMATION (ABSOLUTE) 3D MULTI-PERSON POSE ESTIMATION (ROOT-RELATIVE) 3D POSE ESTIMATION MONOCULAR 3D HUMAN POSE ESTIMATION MULTI-PERSON POSE ESTIMATION ROOT JOINT LOCALIZATION
In multi-person pose estimation actors can be heavily occluded, even become fully invisible behind another person.
Ranked #1 on 3D Multi-Person Pose Estimation on MuPoTS-3D
Besides the integration of top-down and bottom-up networks, unlike existing pose discriminators that are designed solely for single person, and consequently cannot assess natural inter-person interactions, we propose a two-person pose discriminator that enforces natural two-person interactions.