AI 中文总结
HandEdit是含2亿+实例、覆盖26种URDF的统一具身感知图像编辑基准，用于弥合人类与机器人数据差异，可评估11种图像编辑基线，助力具身人工智能发展。
AI 中文摘要
灵巧手机器人操作是具身人工智能的基石，但收集具身感知遥操作数据的高成本阻碍了其发展。尽管大量人类手的自我中心视角视频提供了可扩展的替代方案，但人类与机器人数据在外观、关节活动度和相机视角上的显著差异，给协同训练带来了重大挑战。现有通用图像编辑模型虽具备强大能力，却缺乏必要的具身特定先验来完全弥合这一差距。本研究提出HandEdit，一个统一的大规模具身感知图像编辑数据集与基准，专门用于在自我中心视角下将人类手和手臂转换为各种灵巧机器人具身。HandEdit包含超过2亿个编辑实例，源自5个不同的源数据集，涵盖26种不同的URDF（统一机器人描述格式），包括13种仅手配置和13种手-臂配置。伴随该数据集，我们建立了统一的基准协议，包含两个任务：仅手任务和手-臂任务，支持URDF条件评估。我们使用多维度指标套件对11种代表性图像编辑基线进行了广泛评估，该套件包括通用相似度指标、基于视觉语言模型（VLM）的判断以及具身感知指标。HandEdit是图像编辑与机器人学交叉领域的关键资源：它推进了具身感知编辑模型，同时支持从大量人类视频数据中进行可扩展的灵巧机器人学习，为更具泛化性的具身人工智能铺平了道路。
英文摘要
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.
CommentsTechnical Report. Project Page: https://handedit.github.io/
