ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912152485363712 |
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| author | Winter, Daniel Shul, Asaf Cohen, Matan Berman, Dana Pritch, Yael Rav-Acha, Alex Hoshen, Yedid |
| author_facet | Winter, Daniel Shul, Asaf Cohen, Matan Berman, Dana Pritch, Yael Rav-Acha, Alex Hoshen, Yedid |
| contents | This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specified by either an image or text. Existing methods struggle to fully meet the task's challenging objectives: (i) seamlessly composing the object into the scene with photorealistic pose and lighting, and (ii) preserving the object's identity. We hypothesize that achieving these goals requires large scale supervision, but manually collecting sufficient data is simply too expensive. The key observation in this paper is that many mass-produced objects recur across multiple images of large unlabeled datasets, in different scenes, poses, and lighting conditions. We use this observation to create massive supervision by retrieving sets of diverse views of the same object. This powerful paired dataset enables us to train a straightforward text-to-image diffusion architecture to map the object and scene descriptions to the composited image. We compare our method, ObjectMate, with state-of-the-art methods for object insertion and subject-driven generation, using a single or multiple references. Empirically, ObjectMate achieves superior identity preservation and more photorealistic composition. Differently from many other multi-reference methods, ObjectMate does not require slow test-time tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation Winter, Daniel Shul, Asaf Cohen, Matan Berman, Dana Pritch, Yael Rav-Acha, Alex Hoshen, Yedid Computer Vision and Pattern Recognition This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specified by either an image or text. Existing methods struggle to fully meet the task's challenging objectives: (i) seamlessly composing the object into the scene with photorealistic pose and lighting, and (ii) preserving the object's identity. We hypothesize that achieving these goals requires large scale supervision, but manually collecting sufficient data is simply too expensive. The key observation in this paper is that many mass-produced objects recur across multiple images of large unlabeled datasets, in different scenes, poses, and lighting conditions. We use this observation to create massive supervision by retrieving sets of diverse views of the same object. This powerful paired dataset enables us to train a straightforward text-to-image diffusion architecture to map the object and scene descriptions to the composited image. We compare our method, ObjectMate, with state-of-the-art methods for object insertion and subject-driven generation, using a single or multiple references. Empirically, ObjectMate achieves superior identity preservation and more photorealistic composition. Differently from many other multi-reference methods, ObjectMate does not require slow test-time tuning. |
| title | ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.08645 |