Keskeiset käsitteet
IMPRINT introduces a novel two-stage framework for generative object compositing, excelling in identity preservation and background harmonization.
Tiivistelmä
The content discusses IMPRINT, a diffusion-based generative model for object compositing. It introduces a two-stage learning framework that separates identity preservation from compositing, achieving superior results. The first stage focuses on context-agnostic, identity-preserving pretraining of the object encoder. The second stage harmonizes the object with the background using the learned representation. IMPRINT outperforms existing methods in identity preservation and composition quality.
Introduction:
Advances in image compositing with diffusion models.
Importance of identity preservation and background harmonization.
Related Work:
Traditional image blending and GAN-based approaches.
Recent focus on appearance preservation in generative compositing.
Approach:
Description of the proposed two-stage framework, IMPRINT.
Details on context-agnostic ID-preserving training and object compositing stage.
Experiments:
Training details for both stages of IMPRINT.
Evaluation benchmark and quantitative/qualitative results.
Conclusion, Limitation, and Future Work:
Summary of IMPRINT's performance and limitations.
Data Extraction:
None present in the provided content.
Tilastot
arXiv:2403.10701v1 [cs.CV] 15 Mar 2024