arXiv:1711.08229
공식 코드 · Papers with Code
Integral Human Pose Regression
원문 초록
State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi 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
AthleticsPose: Authentic Sports Motion Dataset on Athletic Field and Evaluation of Monocular 3D Pose Estimation Ability
Systematic Comparison of Projection Methods for Monocular 3D Human Pose Estimation on Fisheye Images
토론
이 논문과 연결된 포럼 글이 아직 없습니다.