VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation
원문 초록
In the rapidly evolving field of computer vision, the task of accurately estimating the poses of multiple individuals from various viewpoints presents a formidable challenge, especially if the estimations should be reliable as well. This work presents an extensive evaluation of the generalization capabilities of multi-view multi-person pose estimators to unseen datasets and presents a new algorithm with strong performance in this task. It also studies the improvements by additionally using depth information. Since the new approach can not only generalize well to unseen datasets, but also to different keypoints, the first multi-view multi-person whole-body estimator is presented. To support further research on those topics, all of the work is publicly accessible.
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Multi-person Physics-based Pose Estimation for Combat Sports
PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
DETRPose: Real-time end-to-end transformer model for multi-person pose estimation
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation
HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose Estimation
토론
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