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IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation

Yizhi Song · Zhifei Zhang · Zhe Lin · Scott Cohen · Brian Price · Jianming Zhang · Soo Ye Kim · He Zhang · Wei Xiong · Daniel Aliaga

Arch 4A-E Poster #315
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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.

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