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- W4213203506 abstract "Perceiving affordances –the opportunities of interaction in a scene, is a fundamental ability of humans. It is an equally important skill for AI agents and robots to better understand and interact with the world. However, labeling affordances in the environment is not a trivial task. To address this issue, we propose a task-agnostic framework, named Inpaint2Learn, that generates affordance labels in a fully automatic manner and opens the door for affordance learning in the wild. To demonstrate its effectiveness, we apply it to three different tasks: human affordance prediction, Location2Object and 6D object pose hallucination. Our experiments and user studies show that our models, trained with the Inpaint2Learn scaffold, are able to generate diverse and visually plausible results in all three scenarios." @default.
- W4213203506 created "2022-02-24" @default.
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- W4213203506 date "2022-01-01" @default.
- W4213203506 modified "2023-09-28" @default.
- W4213203506 title "Inpaint2Learn: A Self-Supervised Framework for Affordance Learning" @default.
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- W4213203506 doi "https://doi.org/10.1109/wacv51458.2022.00383" @default.
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