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
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.