2021/03/18 by Bruno Artacho, Artacho, Bruno, Andreas Savakis +1 · 31 citations
Computer Science · Engineering · Psychology · #Anomaly Detection Techniques and Applications #Artificial intelligence #Cartography #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Economics #Estimation #FOS: Computer and information sciences #FOS: Electrical engineering #Gait Recognition and Analysis #Geography #Human Pose and Action Recognition #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Management #Pose #Psychology #Scale (ratio) #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2103.10180
published in arXiv (Cornell University) (Cornell University) · arXiv admin note: text overlap with arXiv:2001.08095
arxiv created 2021/03/18 · openalex publication_date 2021/03/18 · arxiv updated 2021/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose OmniPose, a single-pass, end-to-end trainable framework, that achieves state-of-the-art results for multi-person pose estimation. Using a novel waterfall module, the OmniPose architecture leverages multi-scale feature representations that increase the effectiveness of backbone feature extractors, without the need for post-processing. OmniPose incorporates contextual information across scales and joint localization with Gaussian heatmap modulation at the multi-scale feature extractor to estimate human pose with state-of-the-art accuracy. The multi-scale representations, obtained by the improved waterfall module in OmniPose, leverage the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Our results on multiple datasets demonstrate that OmniPose, with an improved HRNet backbone and waterfall module, is a robust and efficient architecture for multi-person pose estimation that achieves state-of-the-art results.