Diffusion Models are Open-World Affordance Learners: Leveraging Generative Priors for 3D Affordance Learning

3D affordance grounding aims to understand how diverseobjects can be manipulated, making it a cornerstone of embodied inter-action. However, prior works struggle to generalize to out-of-distribution,open-world scenarios, leaving a critical gap between limited dataset per-formance and real-world application needs. Inspired by the saying: “WhatI can not create, I do not understand”, we find generative modelscan generate semantically valid HOI images, which indicates inherent en-coding of affordance concepts. Building on this insight, we propose DAG,the first innovative diffusion-based 3D affordance grounding frameworkthat extracts general affordance knowledge from text-to-image diffusionmodels for 3D affordance prediction. Specifically, we extract the affor-dance priors from a diffusion model to encode HOI priors, and designan affordance block with a multi-source affordance decoder for dense3D affordance prediction. Extensive experiments show that DAG con-sistently outperforms state-of-the-art methods and exhibits strong open-world generalization, even in the challenging one-shot setting. The codeof our method is released on https://github.com/hq-King/DAG.