Geometry-preserved image editing

초록

As generative models become more advanced and essential, interest in using these models for image editing is growing. Nevertheless, conventional image editing methods still have many problems and can alter the intrinsic properties (e.g. geometry) of an object during the editing process. In this article, a novel geometry-preserved image editing method is presented, where key points, keyframe, or canny edges are utilized as inputs for geometric constraints. A text prompt is fed into the inversion pipeline along with the input image and key points/keyframes/canny for editing. Then, new images are generated based on the editing pipeline of the denoising diffusion implicit model. Experiments demonstrate that the method outperforms its baselines. In this paper, we present a novel geometry-preserved image editing method, where key points, keyframe, or canny edges are utilized as inputs for geometric constraints. A text prompt is fed into the inversion pipeline along with the input image and key points/keyframes/canny for editing. Then, new images are generated based on the editing pipeline of the denoising diffusion implicit model. image

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