Instruction-based Image Manipulation by Watching How Things Move
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910747827634176 |
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| author | Cao, Mingdeng Zhang, Xuaner Zheng, Yinqiang Xia, Zhihao |
| author_facet | Cao, Mingdeng Zhang, Xuaner Zheng, Yinqiang Xia, Zhihao |
| contents | This paper introduces a novel dataset construction pipeline that samples pairs of frames from videos and uses multimodal large language models (MLLMs) to generate editing instructions for training instruction-based image manipulation models. Video frames inherently preserve the identity of subjects and scenes, ensuring consistent content preservation during editing. Additionally, video data captures diverse, natural dynamics-such as non-rigid subject motion and complex camera movements-that are difficult to model otherwise, making it an ideal source for scalable dataset construction. Using this approach, we create a new dataset to train InstructMove, a model capable of instruction-based complex manipulations that are difficult to achieve with synthetically generated datasets. Our model demonstrates state-of-the-art performance in tasks such as adjusting subject poses, rearranging elements, and altering camera perspectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12087 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Instruction-based Image Manipulation by Watching How Things Move Cao, Mingdeng Zhang, Xuaner Zheng, Yinqiang Xia, Zhihao Computer Vision and Pattern Recognition This paper introduces a novel dataset construction pipeline that samples pairs of frames from videos and uses multimodal large language models (MLLMs) to generate editing instructions for training instruction-based image manipulation models. Video frames inherently preserve the identity of subjects and scenes, ensuring consistent content preservation during editing. Additionally, video data captures diverse, natural dynamics-such as non-rigid subject motion and complex camera movements-that are difficult to model otherwise, making it an ideal source for scalable dataset construction. Using this approach, we create a new dataset to train InstructMove, a model capable of instruction-based complex manipulations that are difficult to achieve with synthetically generated datasets. Our model demonstrates state-of-the-art performance in tasks such as adjusting subject poses, rearranging elements, and altering camera perspectives. |
| title | Instruction-based Image Manipulation by Watching How Things Move |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.12087 |