4 accepted papers
Standard 3D human pose estimation (HPE) benchmarks employ root-centering, which normalizes poses relative to the pelvis but discards absolute root position information. While effective for evaluation, this approach limits real-world applications such as motion tracking, AR/VR, and human-computer int…
3D Human Pose Estimation (HPE) is a one-to-many problem by nature, making it challenging to estimate an accurate 3D pose from a single 2D pose. Some prior works have attempted to tackle this problem by using a conditional generative network. They generate 3D poses from a given 2D pose with noises fr…
3D Human Pose Estimation (HPE) is a fundamental task in the computer vision. Generalization in 3D HPE task is crucial due to the need for robustness across diverse environments and datasets. Existing methods often focus on learning relationships between joints to enhance the generalization capabilit…
In the field of 3D Human Pose Estimation (HPE), scalability and generalization across diverse real-world scenarios remain significant challenges. This paper addresses two key bottlenecks to scalability: limited data diversity caused by 'popularity bias' and increased 'one-to-many' depth ambiguity ar…