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Found 6 papers, 4 papers with code
MAMMA: Markerless & Automatic Multi-Person Motion Action Capture
Our approach offers competitive reconstruction quality compared to commercial marker-based motion-capture solutions, without the extensive manual cleanup.
BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion
BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes.
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
Our experimental results show significant improvement compared to state-of-the-art methods on albedo estimation, both in terms of accuracy and fairness.
SPEC: Seeing People in the Wild with an Estimated Camera
We then train a novel network that concatenates the camera calibration to the image features and uses these together to regress 3D body shape and pose.
Ranked #1 on
3D Multi-Person Pose Estimation
on AGORA
AGORA: Avatars in Geography Optimized for Regression Analysis
Additionally, we fine-tune methods on AGORA and show improved performance on both AGORA and 3DPW, confirming the realism of the dataset.
Populating 3D Scenes by Learning Human-Scene Interaction
Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art.
Ranked #4 on
Dense contact estimation
on MOW
