DiffPop: Plausibility-Guided Object Placement Diffusion for Image Composition

TL;DR

DiffPop introduces a plausibility-guided diffusion framework for realistic object placement in image composition, leveraging human-in-the-loop training and a structural plausibility classifier to generate diverse, plausible composite images.

Contribution

The paper presents the first plausibility-guided diffusion model for object placement, integrating human feedback and a structural classifier for improved realism in image composition.

Findings

Outperforms existing methods in generating plausible composite images

Demonstrates versatility in data augmentation and multi-object placement

Achieves superior results on Cityscapes-OP and OPA datasets

Abstract

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.