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Humans excel at learning from expert demonstrations and solving their ownproblems. To equip intelligent robots and assistants, such as AR glasses, withthis ability, it is essential to ground human hand interactions (i.e.,affordances) from demonstration videos and apply them to a target image like auser's AR glass view. The video-to-image affordance grounding task ischallenging due to (1) the need to predict fine-grained affordances, and (2)the limited training data, which inadequately covers video-image discrepanciesand negatively impacts grounding. To tackle them, we propose AffordanceTransformer (Afformer), which has a fine-grained transformer-based decoder thatgradually refines affordance grounding. Moreover, we introduce Mask AffordanceHand (MaskAHand), a self-supervised pre-training technique for synthesizingvideo-image data and simulating context changes, enhancing affordance groundingacross video-image discrepancies. Afformer with MaskAHand pre-training achievesstate-of-the-art performance on multiple benchmarks, including a substantial37% improvement on the OPRA dataset. Code is made available athttps://github.com/showlab/afformer.

Affordance Grounding from Demonstration Video to Target Image