A diffusion-based model decomposes an image into a clean background and a transparent foreground layer that retains shadows and reflections, enabling object removal and spatial edits.
DESOBAv2: Towards Large-scale Real-world Dataset for Shadow Generation
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abstract
Image composition refers to inserting a foreground object into a background image to obtain a composite image. In this work, we focus on generating plausible shadow for the inserted foreground object to make the composite image more realistic. To supplement the existing small-scale dataset DESOBA, we create a large-scale dataset called DESOBAv2 by using object-shadow detection and inpainting techniques. Specifically, we collect a large number of outdoor scene images with object-shadow pairs. Then, we use pretrained inpainting model to inpaint the shadow region, resulting in the deshadowed images. Based on real images and deshadowed images, we can construct pairs of synthetic composite images and ground-truth target images. Dataset is available at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.
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cs.CV 1years
2024 1verdicts
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Generative Image Layer Decomposition with Visual Effects
A diffusion-based model decomposes an image into a clean background and a transparent foreground layer that retains shadows and reflections, enabling object removal and spatial edits.