HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing

Zhenjie Yang; Xingyu Jiao; Guopeng Zhong; Shuzhe Yang; Shi Che; Chao Wu; Chenyu Jiang; Dongjie Zhang; Yideng Zhang; Zheng Zhang; Muyun Jiang; Haisheng Su; Shuang Jin; Donghang Zhang; Chao Yang; Li Chen; Hongyang Li; Zuxuan Wu; Yu-Gang Jiang; Xiaosong Jia; Junchi Yan

arXiv · 2608.12122

HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing

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Abstract

Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abundant egocentric videos of human hands offer a scalable alternative, the profound discrepancies in appearance, articulation, and camera viewpoints between human and robotic data raise significant challenges for co-training. Though existing general image-editing models demonstrate strong capabilities, they lack necessary embodiment-specific priors to fully bridge this gap. In this work, we present HandEdit, a unified large-scale embodiment-aware image-editing dataset and benchmark specifically designed to transform human hands and arms into various dexterous robotic embodiments within egocentric frames. HandEdit comprises over 200M editing instances derived from five diverse source datasets, covering 26 distinct URDFs, including 13 hand-only and 13 hand-arm configurations. Alongside the dataset, we establish a unified benchmark protocol with two tracks: Hand-only and Hand-Arm, supporting URDF-conditioned evaluation. We conduct extensive evaluations of 11 representative image-editing baselines using a multi-dimensional metric suite, including generic similarity metrics, VLM-based judgment, and embodiment-aware metrics. HandEdit serves as a critical resource at the intersection of image editing and robotics: it advances embodiment-aware editing models while enabling scalable dexterous robotic learning from abundant human video data, paving the way for more generalizable Embodied AI.

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Zhenjie Yang, Xingyu Jiao, Guopeng Zhong, Shuzhe Yang, Shi Che, Chao Wu, Chenyu Jiang, Dongjie Zhang, Yideng Zhang, Zheng Zhang, Muyun Jiang, Haisheng Su, Shuang Jin, Donghang Zhang, Chao Yang, Li Chen, Hongyang Li, Zuxuan Wu, Yu-Gang Jiang, Xiaosong Jia, Junchi Yan. 2026-08-12. HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing. https://arxiv.org/abs/2608.12122

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