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Pose Flow: Efficient Online Pose Tracking

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arxiv 1802.00977 v2 pith:WCE5Z4EX submitted 2018-02-03 cs.CV cs.AI

Yuliang Xiu , Jiefeng Li , Haoyu Wang , Yinghong Fang , Cewu Lu This is my paper · ORCID
classification cs.CVcs.AI
keywords posetrackingflowsonlineefficientflowmotaposes
verification ladder T0 review T1 audit T2 compute T3 formal
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Multi-person articulated pose tracking in unconstrained videos is an important while challenging problem. In this paper, going along the road of top-down approaches, we propose a decent and efficient pose tracker based on pose flows. First, we design an online optimization framework to build the association of cross-frame poses and form pose flows (PF-Builder). Second, a novel pose flow non-maximum suppression (PF-NMS) is designed to robustly reduce redundant pose flows and re-link temporal disjoint ones. Extensive experiments show that our method significantly outperforms best-reported results on two standard Pose Tracking datasets by 13 mAP 25 MOTA and 6 mAP 3 MOTA respectively. Moreover, in the case of working on detected poses in individual frames, the extra computation of pose tracker is very minor, guaranteeing online 10FPS tracking. Our source codes are made publicly available(https://github.com/YuliangXiu/PoseFlow).

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled Videos

    cs.CV 2025-01 conditional novelty 5.0 of 10

    STDPose combines a Dynamic-Aware Mask, temporal feature and heatmap fusion, and a mutual information loss to improve video pose propagation and pose estimation on sparsely-labeled videos, achieving new state-of-the-ar...

  2. An End-to-End Framework for Video Multi-Person Pose Estimation

    cs.CV 2025-09 reject novelty 4.0 of 10

    An end-to-end video pose transformer built on PETR with spatio-temporal encoders and an instance consistency loss reaches 83.0 mAP on PoseTrack2017 and appears around 4x faster than DCPose.

  3. FashionPose: Unified Text-Driven Fashion Synthesis with Joint Geometric and Photometric Control

    cs.CV 2025-07 reject novelty 4.0 of 10

    A single caption can drive pose generation, person-image synthesis, and relighting through a three-stage FashionPose pipeline, with reported text-to-pose gains on DF-PASS that are undermined by inconsistent tables.

  4. Optimizing Human Pose Estimation Through Focused Human and Joint Regions

    cs.CV 2025-01 conditional novelty 4.0 of 10

    VREMD combines human and keypoint masks with bidirectional deformable cross-attention to reach state-of-the-art mAP on three PoseTrack benchmarks.

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