Motion Guidance: Diffusion-Based Image Editing with Differentiable Motion Estimators

Published: 16 Jan 2024, Last Modified: 12 Apr 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Optical Flow, Image Editing, Guidance, Diffusion Models
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TL;DR: We use diffusion guidance through an off-the-shelf optical flow network as a zero-shot method to edit images given a desired motion.
Abstract: Diffusion models are capable of generating impressive images conditioned on text descriptions, and extensions of these models allow users to edit images at a relatively coarse scale. However, the ability to precisely edit the layout, position, pose, and shape of objects in images with diffusion models is still difficult. To this end, we propose _motion guidance_, a zero-shot technique that allows a user to specify dense, complex motion fields that indicate where each pixel in an image should move. Motion guidance works by steering the diffusion sampling process with the gradients through an off-the-shelf optical flow network. Specifically, we design a guidance loss that encourages the sample to have the desired motion, as estimated by a flow network, while also being visually similar to the source image. By simultaneously sampling from a diffusion model and guiding the sample to have low guidance loss, we can obtain a motion-edited image. We demonstrate that our technique works on complex motions and produces high quality edits of real and generated images.
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Primary Area: generative models
Submission Number: 1566