IMPose: Interactive Multi-person Pose Estimation with Dynamic Correction Propagation
2026-06-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionHuman-Computer Interaction
AI summaryⓘ
The authors created IMPose, a tool that helps label human movements in videos faster and more accurately, even when multiple people appear together. It smartly spreads the corrections made on one frame to other frames, using two methods to keep track of each person and their movement over time. IMPose also remembers past poses to handle tricky situations like blurry images or blocked views. This reduces the amount of manual work needed, allowing for precise labels with fewer user clicks. They tested it on popular datasets and improved annotation efficiency significantly, sharing their tool and data for others to use.
human pose annotationmulti-person trackingkeypoint detectiontemporal correction propagationtrajectory banksequential modelingpose datasetsinteractive annotation toolmotion kinematicsPoseTrack21
Authors
Haoyang Ge, Jian Ma, Ziwen Wang, Qihe Wang, Jianqi Fan, Hongzhi Yu, Xingyu Chen, Kun Li
Abstract
High-quality dynamic human pose annotation equips AI with precise motion kinematics to enable human behavior mastery, yet remains labor-intensive and time-consuming. Current annotation tools either lack temporal correction propagation or fail in multi-person scenarios, necessitating excessive manual intervention. In this paper, we introduce IMPose, an interactive tool for multi-person dynamic pose annotation. It features a dual-level tracking mechanism that propagates one-frame multi-person pose corrections from annotators across entire videos. The keypoint-level ensures corrections temporal propagation via sequential modeling, while the instance-level employs keypoint-aware embedding with relative positional encoding to maintain multi-person cross-frame consistency. To further improve robustness, IMPose maintains historical pose and instance cues in a trajectory bank, which enhances long-range temporal association and stabilizes annotation in challenging cases such as occlusion and motion blur. By converting sparse human corrections into dense and coherent pose trajectories, our framework significantly reduces repeated manual refinement across frames. Extensive experiments show that IMPose consistently achieves a strong accuracy efficiency trade off under different interaction budgets, demonstrating particular advantages in low click annotation settings. IMPose achieves high precision annotation with high efficiency, requiring only 27 clicks per 1,050 frame video on 3DPW and 3 clicks per tracklet per 84-frame on PoseTrack21. We further expand PoseTrack21 with 188K pose instances (3.55M keypoints) at a minimal cost of 10 annotators in 10 hours. The annotation tool, codes, and extended dataset will be open-sourced.