摘要
arXiv:2502.07278v4 Announce Type: replace Abstract: We present ATOP (Articulate That Object Part), a novel few-shot method based on motion personalization to articulate a static 3D object with respect to a part and its motion as prescribed in a text prompt. Given the scarcity of available datasets with motion attribute annotations, existing methods struggle to generalize well in this task. In our work, the text input allows us to tap into the power of modern-day diffusion models to generate plausible motion samples for the right object category and part. In turn, the input 3D object provides ``image prompting'' to personalize the generated motion to the very input object. Our method starts with a few-shot finetuning to inject articulation awareness to current diffusion models to learn a unique motion identifier associated with the target object part.
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