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arXiv · 2412.12087

Instruction-based Image Manipulation by Watching How Things Move

Mingdeng Cao·Xuaner Zhang·Yinqiang Zheng·Zhihao Xia
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Abstract

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.

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Mingdeng Cao, Xuaner Zhang, Yinqiang Zheng, Zhihao Xia. 2024-12-16. Instruction-based Image Manipulation by Watching How Things Move. https://arxiv.org/abs/2412.12087

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