HOSt3R: Keypoint-free Hand-Object 3D Reconstruction from RGB images

• URL: http://arxiv.org/abs/2508.16465v2
• Date: Mon, 25 Aug 2025 16:02:08 GMT
• Title: HOSt3R: Keypoint-free Hand-Object 3D Reconstruction from RGB images
• Authors: Anilkumar Swamy, Vincent Leroy, Philippe Weinzaepfel, Jean-Sébastien Franco, Grégory Rogez,
• Abstract summary: We propose a robust, keypoint detector-free approach to estimating hand-object 3D transformations from monocular motion video/images.<n>We further integrate this with a multi-view reconstruction pipeline to accurately recover hand-object 3D shape.<n>Our method, named HOSt3R, is unconstrained, does not rely on pre-scanned object templates or camera intrinsics, and reaches state-of-the-art performance.
• Score: 27.025336665386735
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Hand-object 3D reconstruction has become increasingly important for applications in human-robot interaction and immersive AR/VR experiences. A common approach for object-agnostic hand-object reconstruction from RGB sequences involves a two-stage pipeline: hand-object 3D tracking followed by multi-view 3D reconstruction. However, existing methods rely on keypoint detection techniques, such as Structure from Motion (SfM) and hand-keypoint optimization, which struggle with diverse object geometries, weak textures, and mutual hand-object occlusions, limiting scalability and generalization. As a key enabler to generic and seamless, non-intrusive applicability, we propose in this work a robust, keypoint detector-free approach to estimating hand-object 3D transformations from monocular motion video/images. We further integrate this with a multi-view reconstruction pipeline to accurately recover hand-object 3D shape. Our method, named HOSt3R, is unconstrained, does not rely on pre-scanned object templates or camera intrinsics, and reaches state-of-the-art performance for the tasks of object-agnostic hand-object 3D transformation and shape estimation on the SHOWMe benchmark. We also experiment on sequences from the HO3D dataset, demonstrating generalization to unseen object categories.

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