Affogato: Learning Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908407381884928 |
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| author | Lee, Junha Park, Eunha Park, Chunghyun Kang, Dahyun Cho, Minsu |
| author_facet | Lee, Junha Park, Eunha Park, Chunghyun Kang, Dahyun Cho, Minsu |
| contents | Affordance grounding-localizing object regions based on natural language descriptions of interactions-is a critical challenge for enabling intelligent agents to understand and interact with their environments. However, this task remains challenging due to the need for fine-grained part-level localization, the ambiguity arising from multiple valid interaction regions, and the scarcity of large-scale datasets. In this work, we introduce Affogato, a large-scale benchmark comprising 150K instances, annotated with open-vocabulary text descriptions and corresponding 3D affordance heatmaps across a diverse set of objects and interactions. Building on this benchmark, we develop simple yet effective vision-language models that leverage pretrained part-aware vision backbones and a text-conditional heatmap decoder. Our models trained with the Affogato dataset achieve promising performance on the existing 2D and 3D benchmarks, and notably, exhibit effectiveness in open-vocabulary cross-domain generalization. The Affogato dataset is shared in public: https://huggingface.co/datasets/project-affogato/affogato |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12009 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Affogato: Learning Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale Lee, Junha Park, Eunha Park, Chunghyun Kang, Dahyun Cho, Minsu Computer Vision and Pattern Recognition Affordance grounding-localizing object regions based on natural language descriptions of interactions-is a critical challenge for enabling intelligent agents to understand and interact with their environments. However, this task remains challenging due to the need for fine-grained part-level localization, the ambiguity arising from multiple valid interaction regions, and the scarcity of large-scale datasets. In this work, we introduce Affogato, a large-scale benchmark comprising 150K instances, annotated with open-vocabulary text descriptions and corresponding 3D affordance heatmaps across a diverse set of objects and interactions. Building on this benchmark, we develop simple yet effective vision-language models that leverage pretrained part-aware vision backbones and a text-conditional heatmap decoder. Our models trained with the Affogato dataset achieve promising performance on the existing 2D and 3D benchmarks, and notably, exhibit effectiveness in open-vocabulary cross-domain generalization. The Affogato dataset is shared in public: https://huggingface.co/datasets/project-affogato/affogato |
| title | Affogato: Learning Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.12009 |