AssemblyHands-X: Modeling 3D Hand-Body Coordination for Understanding Bimanual Human Activities

• URL: http://arxiv.org/abs/2509.23888v1
• Date: Sun, 28 Sep 2025 13:52:14 GMT
• Title: AssemblyHands-X: Modeling 3D Hand-Body Coordination for Understanding Bimanual Human Activities
• Authors: Tatsuro Banno, Takehiko Ohkawa, Ruicong Liu, Ryosuke Furuta, Yoichi Sato,
• Abstract summary: We present AssemblyHands-X, the first markerless 3D hand-body benchmark for bimanual activities.<n>Our approach combines multi-view triangulation with SMPL-X mesh fitting, yielding reliable 3D registration of hands and upper body.<n>Our experiments show pose-based action inference is more efficient and accurate than video baselines.
• Score: 27.634829042887358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bimanual human activities inherently involve coordinated movements of both hands and body. However, the impact of this coordination in activity understanding has not been systematically evaluated due to the lack of suitable datasets. Such evaluation demands kinematic-level annotations (e.g., 3D pose) for the hands and body, yet existing 3D activity datasets typically annotate either hand or body pose. Another line of work employs marker-based motion capture to provide full-body pose, but the physical markers introduce visual artifacts, thereby limiting models' generalization to natural, markerless videos. To address these limitations, we present AssemblyHands-X, the first markerless 3D hand-body benchmark for bimanual activities, designed to study the effect of hand-body coordination for action recognition. We begin by constructing a pipeline for 3D pose annotation from synchronized multi-view videos. Our approach combines multi-view triangulation with SMPL-X mesh fitting, yielding reliable 3D registration of hands and upper body. We then validate different input representations (e.g., video, hand pose, body pose, or hand-body pose) across recent action recognition models based on graph convolution or spatio-temporal attention. Our extensive experiments show that pose-based action inference is more efficient and accurate than video baselines. Moreover, joint modeling of hand and body cues improves action recognition over using hands or upper body alone, highlighting the importance of modeling interdependent hand-body dynamics for a holistic understanding of bimanual activities.

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