Human Mesh Recovery
39 papers with code • 0 benchmarks • 2 datasets
Estimate 3D body mesh from images
Benchmarks
These leaderboards are used to track progress in Human Mesh Recovery
MoVi
FLAG3D
Most implemented papers
A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
zczcwh/gtrs
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We propose a pose analysis module that uses graph transformers to exploit structured and implicit joint correlations, and a mesh regression module that combines the extracted pose feature with the mesh template to reconstruct the final human mesh.
GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras
Since the joint reconstruction of human motions and camera poses is underconstrained, we propose a global trajectory predictor that generates global human trajectories based on local body movements.
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.
Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery
The ability to perceive 3D human bodies from a single image has a multitude of applications ranging from entertainment and robotics to neuroscience and healthcare.
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.
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
2024-MindSpore-1/Code9
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CVPR 2023
With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision.
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action
Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.
Deformable Mesh Transformer for 3D Human Mesh Recovery
yusukey03012/deformer
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CVPR 2023
DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop formed within a transformer decoder that is equipped with efficient body mesh driven attention modules: 1) body sparse self-attention and 2) deformable mesh cross attention.
Learning Analytical Posterior Probability for Human Mesh Recovery
netease-gameai/propose
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CVPR 2023
Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks.
