UniHOPE: A Unified Approach for Hand-Only and Hand-Object Pose Estimation

• URL: http://arxiv.org/abs/2503.13303v1
• Date: Mon, 17 Mar 2025 15:46:43 GMT
• Title: UniHOPE: A Unified Approach for Hand-Only and Hand-Object Pose Estimation
• Authors: Yinqiao Wang, Hao Xu, Pheng-Ann Heng, Chi-Wing Fu,
• Abstract summary: Existing methods are specialized, focusing on either bare-hand or hand interacting with object.<n>No method can flexibly handle both scenarios and their performance degrades when applied to the other scenario.<n>We propose UniHOPE, a unified approach for general 3D hand-object pose estimation.
• Score: 82.93208597526503
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Estimating the 3D pose of hand and potential hand-held object from monocular images is a longstanding challenge. Yet, existing methods are specialized, focusing on either bare-hand or hand interacting with object. No method can flexibly handle both scenarios and their performance degrades when applied to the other scenario. In this paper, we propose UniHOPE, a unified approach for general 3D hand-object pose estimation, flexibly adapting both scenarios. Technically, we design a grasp-aware feature fusion module to integrate hand-object features with an object switcher to dynamically control the hand-object pose estimation according to grasping status. Further, to uplift the robustness of hand pose estimation regardless of object presence, we generate realistic de-occluded image pairs to train the model to learn object-induced hand occlusions, and formulate multi-level feature enhancement techniques for learning occlusion-invariant features. Extensive experiments on three commonly-used benchmarks demonstrate UniHOPE's SOTA performance in addressing hand-only and hand-object scenarios. Code will be released on https://github.com/JoyboyWang/UniHOPE_Pytorch.

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