IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation
<|reference_start|>IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation: Generative object compositing emerges as a promising new avenue for compositional image editing. However, the requirement of object identity preservation poses a significant challenge, limiting practical usage of most existing methods. In response, this paper introduces IMPRINT, a novel diffusion-based generative model trained with a two-stage learning framework that decouples learning of identity preservation from that of compositing. The first stage is targeted for context-agnostic, identity-preserving pretraining of the object encoder, enabling the encoder to learn an embedding that is both view-invariant and conducive to enhanced detail preservation. The subsequent stage leverages this representation to learn seamless harmonization of the object composited to the background. In addition, IMPRINT incorporates a shape-guidance mechanism offering user-directed control over the compositing process. Extensive experiments demonstrate that IMPRINT significantly outperforms existing methods and various baselines on identity preservation and composition quality.<|reference_end|>
@article{song2024imprint:,
    title={IMPRINT: Generative Object Compositing by Learning Identity-Preserving
  Representation},
    author={Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming
  Zhang, Soo Ye Kim, He Zhang, Wei Xiong, Daniel Aliaga},
    journal={arXiv preprint arXiv:2403.10701},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10701},
    primaryClass={cs.CV}
}