High-Quality Pluralistic Image Completion via Code Shared VQGAN

Understand

PICNet pioneered the generation of multiple and diverse results for image completion task, but it required a careful balance between $\mathcal{KL}$ loss (diversity) and reconstruction loss (quality), resulting in a limited diversity and quality .

• Separately, iGPT-based architecture has been employed to infer distributions in a discrete space derived from a pixel-level pre-clustered palette, which however cannot generate high-quality results directly.
• In this work, we present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed.
• The core of our design lies in a simple yet effective code sharing mechanism that leads to a very compact yet expressive image representation in a discrete latent domain.