DiffPop: Plausibility-Guided Object Placement Diffusion for Image Composition
This work addresses the challenge of generating realistic composite images for applications like data augmentation, though it is incremental as it builds on existing diffusion models with human-in-the-loop guidance.
The paper tackles the problem of plausible object placement for realistic image composition by proposing DiffPop, a framework that uses a plausibility-guided diffusion model to learn object scale and spatial relations, achieving superior performance on Cityscapes-OP and OPA datasets.
In this paper, we address the problem of plausible object placement for the challenging task of realistic image composition. We propose DiffPop, the first framework that utilizes plausibility-guided denoising diffusion probabilistic model to learn the scale and spatial relations among multiple objects and the corresponding scene image. First, we train an unguided diffusion model to directly learn the object placement parameters in a self-supervised manner. Then, we develop a human-in-the-loop pipeline which exploits human labeling on the diffusion-generated composite images to provide the weak supervision for training a structural plausibility classifier. The classifier is further used to guide the diffusion sampling process towards generating the plausible object placement. Experimental results verify the superiority of our method for producing plausible and diverse composite images on the new Cityscapes-OP dataset and the public OPA dataset, as well as demonstrate its potential in applications such as data augmentation and multi-object placement tasks. Our dataset and code will be released.