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Compositional Image Decomposition with Diffusion Models (2406.19298v1)

Published 27 Jun 2024 in cs.CV and cs.LG

Abstract: 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.

Citations (1)

Summary

  • The paper introduces Decomp Diffusion, an unsupervised method that decomposes images into compositional concepts, advancing beyond traditional global and object-centric models.
  • It leverages energy-based diffusion models with a stable denoising training objective to capture both global scene descriptors and local elements, yielding high-fidelity reconstruction metrics.
  • The method enables flexible image editing and recombination, setting the stage for future research in adaptive and interpretable generative models.

Compositional Image Decomposition with Diffusion Models: An Expert Overview

The paper "Compositional Image Decomposition with Diffusion Models" presents a novel unsupervised method, termed Decomp Diffusion, for decomposing images into compositional concepts using diffusion models. The primary contribution of this work is identifying distinct components within an image that capture both global scene descriptors and local elements, allowing for flexible recombination, including across different models and datasets.

Background and Contribution

Previous approaches to compositional concept discovery have been largely divided into two main categories: global factor models and object-centric models. Global models typically involve representing data in a multi-dimensional vector space, where individual factors such as color or expression are isolated. However, these models are limited by fixed dimensionality, which restricts the combinability of multiple instances of a single concept. Object-centric models, on the other hand, focus on decomposing an image into factors defined by segmentation masks, which struggle with global relationships and concepts affecting entire scenes.

Decomp Diffusion overcomes these limitations by leveraging the connection between Energy-Based Models (EBMs) and diffusion models. This novel method allows for image decomposition into a set of factors, each represented by a separate diffusion model instance. Unlike the unstable training process of approaches like COMET, Decomp Diffusion benefits from a more stable denoising training objective, enabling the generation of high-resolution images.

Methodology and Results

The Decomp Diffusion method employs denoising networks where diffusion models act as parameterized energy functions. This approach facilitates the generation of images embodying specific factors of interest by sampling from a composed diffusion distribution. The paper presents strong evidence of the approach's ability to capture and recombine both global and local concepts.

Quantitatively, the method outperforms existing baselines in generating reconstructed images with high fidelity on datasets such as CelebA-HQ, Falcor3D, and Virtual KITTI 2, achieving lower Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Learned Perceptual Image Patch Similarity (LPIPS) scores. Additionally, the decomposition effectiveness is highlighted by high Mutual Information Gap (MIG) and Modularity-Classification-Complexity (MCC) scores, indicating robust disentanglement capabilities.

Implications and Future Directions

The implications of this work are twofold: practical and theoretical. Practically, this approach expands the potential for creating diverse image compositions by allowing individual image components to be manipulated and recombined in new, unseen ways, which could have significant applications in image editing, computer graphics, and generative art. Theoretically, the bridge between EBMs and diffusion models for compositional decomposition opens up new research avenues in unsupervised learning, especially in terms of generalizing visual generation across multiple datasets and nuances.

Future work might explore reducing the computational cost associated with maintaining multiple diffusion models and enhancing the method's adaptability to various encoder architectures. Additionally, developing principled approaches to adaptively determine the ideal number of decomposition factors could further refine the method’s versatility.

In summary, the paper presents a rigorous advancement in unsupervised image composition and decomposition using diffusion models, offering clear improvements over previous methods and setting a foundation for future explorations in compositional machine learning. The ability to generalize across modalities and datasets marks a significant step towards more adaptable and interpretable visual generative models.

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