Compositional Image Decomposition with Diffusion Models
This addresses the challenge of scene understanding and generation for computer vision applications, offering an unsupervised method for compositional manipulation.
The paper tackles the problem of decomposing a single natural image into compositional components like objects, lighting, and shadows, and demonstrates how these components can be flexibly combined to generate novel scenes, such as mixing objects from a bedroom with animals from a zoo under forest lighting.
Given an image of a natural scene, we are able to quickly decompose it into a set of components such as objects, lighting, shadows, and foreground. We can then envision a scene where we combine certain components with those from other images, for instance a set of objects from our bedroom and animals from a zoo under the lighting conditions of a forest, even if we have never encountered such a scene before. In this paper, we present a method to decompose an image into such compositional components. Our approach, Decomp Diffusion, is an unsupervised method which, when given a single image, infers a set of different components in the image, each represented by a diffusion model. We demonstrate how components can capture different factors of the scene, ranging from global scene descriptors like shadows or facial expression to local scene descriptors like constituent objects. We further illustrate how inferred factors can be flexibly composed, even with factors inferred from other models, to generate a variety of scenes sharply different than those seen in training time. Website and code at https://energy-based-model.github.io/decomp-diffusion.