STRIDE: Single-video based Temporally Continuous Occlusion Robust 3D Pose Estimation

arXiv:2312.16221v2 Announce Type: replace
Abstract: The capability to accurately estimate 3D human poses is crucial for diverse fields such as action recognition, gait recognition, and virtual/augmented reality. However, a persistent and significant challenge within this field is the accurate prediction of human poses under conditions of severe occlusion. Traditional image-based estimators struggle with heavy occlusions due to a lack of temporal context, resulting in inconsistent predictions. While video-based models benefit from processing temporal data, they encounter limitations when faced with …

abstract action recognition arxiv augmented reality capability challenge continuous cs.cv diverse fields however human image prediction reality recognition robust type video virtual