3D human pose and shape estimation
27 papers with code • 1 benchmarks • 2 datasets
Estimate 3D human pose and shape (e.g. SMPL) from images
EgoBody
3DOH50K
Most implemented papers
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild
akashsengupta1997/STRAPS-3DHumanShapePose
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Thus, we propose STRAPS (Synthetic Training for Real Accurate Pose and Shape), a system that utilises proxy representations, such as silhouettes and 2D joints, as inputs to a shape and pose regression neural network, which is trained with synthetic training data (generated on-the-fly during training using the SMPL statistical body model) to overcome data scarcity.
Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video
hongsukchoi/TCMR_RELEASE
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CVPR 2021
Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy.
Consistency-Aware Graph Network for Human Interaction Understanding
deepgogogo/CAGNet
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ICCV 2021
Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU).
PARE: Part Attention Regressor for 3D Human Body Estimation
Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong predictions although much of the body is observable.
EventHPE: Event-based 3D Human Pose and Shape Estimation
jimmyzou/eventhpe
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ICCV 2021
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.
Learning to Regress Bodies from Images using Differentiable Semantic Rendering
For Minimally-Clothed regions, we define the DSR-MC loss, which encourages a tight match between a rendered SMPL body and the minimally-clothed regions of the image.
AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation
robot-perception-group/airpose
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In this letter, we present a novel markerless 3D human motion capture (MoCap) system for unstructured, outdoor environments that uses a team of autonomous unmanned aerial vehicles (UAVs) with on-board RGB cameras and computation.
Recovering 3D Human Mesh from Monocular Images: A Survey
Since the release of statistical body models, 3D human mesh recovery has been drawing broader attention.
PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images
To address these issues, we propose a Pyramidal Mesh Alignment Feedback (PyMAF) loop in our regression network for well-aligned human mesh recovery and extend it as PyMAF-X for the recovery of expressive full-body models.
Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms
Experiments with 10 backbones, ranging from CNNs to transformers, show the knowledge learnt from a proximity task is readily transferable to human mesh recovery.
