# ObjectStitch: Generative Object Compositing

Source: [ObjectStitch: Generative Object Compositing](<https://www.sota2.com/research/paper/68fa6e1b1e209131cea3dd5a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:49:11.447Z

Authors: Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, Daniel Aliaga

Published: 2022-12-02T02:15:13.000Z

## Abstract

Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training data pairs for compositing requires substantial manual effort from professionals, and is hardly scalable. Thus, with the recent advances in generative models, in this work, we propose a self-supervised framework for object compositing by leveraging the power of conditional diffusion models. Our framework can hollistically address the object compositing task in a unified model, transforming the viewpoint, geometry, color and shadow of the generated object while requiring no manual labeling. To preserve the input object's characteristics, we introduce a content adaptor that helps to maintain categorical semantics and object appearance. A data augmentation method is further adopted to improve the fidelity of the generator. Our method outperforms relevant baselines in both realism and faithfulness of the synthesized result images in a user study on various real-world images.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on DreamAssembly](<https://www.sota2.com/research/sota/image-classification-on-dreamassembly.md>): Image Classification; DreamAssembly; Accuracy: 66.67.

- [Image Synthesis on DreamAssembly (test)](<https://www.sota2.com/research/sota/image-synthesis-on-dreamassembly-test.md>): Image Synthesis; DreamAssembly (test); CLIP-I: 0.7621.

- [Image Synthesis on MureCom](<https://www.sota2.com/research/sota/image-synthesis-on-murecom.md>): Image Synthesis; MureCom; CLIP-I: 0.7891.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2212.00932>)

[PDF](<https://arxiv.org/pdf/2212.00932.pdf>)