MoBind: Motion Binding for Fine-Grained IMU-Video Pose Alignment

• URL: http://arxiv.org/abs/2602.19004v1
• Date: Sun, 22 Feb 2026 01:54:29 GMT
• Title: MoBind: Motion Binding for Fine-Grained IMU-Video Pose Alignment
• Authors: Duc Duy Nguyen, Tat-Jun Chin, Minh Hoai,
• Abstract summary: We introduce MoBind, a hierarchical contrastive learning framework designed to address three challenges.<n>To isolate motion-relevant cues, MoBind aligns IMU signals with skeletal motion sequences rather than raw pixels.<n>To capture detailed temporal correspondence, MoBind employs a hierarchical contrastive strategy that first aligns token-level temporal segments, then fuses local (body-part) alignment with global (body-wide) motion aggregation.
• Score: 32.178798481694834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We aim to learn a joint representation between inertial measurement unit (IMU) signals and 2D pose sequences extracted from video, enabling accurate cross-modal retrieval, temporal synchronization, subject and body-part localization, and action recognition. To this end, we introduce MoBind, a hierarchical contrastive learning framework designed to address three challenges: (1) filtering out irrelevant visual background, (2) modeling structured multi-sensor IMU configurations, and (3) achieving fine-grained, sub-second temporal alignment. To isolate motion-relevant cues, MoBind aligns IMU signals with skeletal motion sequences rather than raw pixels. We further decompose full-body motion into local body-part trajectories, pairing each with its corresponding IMU to enable semantically grounded multi-sensor alignment. To capture detailed temporal correspondence, MoBind employs a hierarchical contrastive strategy that first aligns token-level temporal segments, then fuses local (body-part) alignment with global (body-wide) motion aggregation. Evaluated on mRi, TotalCapture, and EgoHumans, MoBind consistently outperforms strong baselines across all four tasks, demonstrating robust fine-grained temporal alignment while preserving coarse semantic consistency across modalities. Code is available at https://github.com/bbvisual/ MoBind.

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