High-Quality Pluralistic Image Completion via Code Shared VQGAN

• URL: http://arxiv.org/abs/2204.01931v1
• Date: Tue, 5 Apr 2022 01:47:35 GMT
• Title: High-Quality Pluralistic Image Completion via Code Shared VQGAN
• Authors: Chuanxia Zheng and Guoxian Song and Tat-Jen Cham and Jianfei Cai and Dinh Phung and Linjie Luo
• Abstract summary: We present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed. Our framework is able to learn semantically-rich discrete codes efficiently and robustly, resulting in much better image reconstruction quality.
• Score: 51.7805154545948
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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. The compactness and the richness of the representation further facilitate the subsequent deployment of a transformer to effectively learn how to composite and complete a masked image at the discrete code domain. Based on the global context well-captured by the transformer and the available visual regions, we are able to sample all tokens simultaneously, which is completely different from the prevailing autoregressive approach of iGPT-based works, and leads to more than 100$\times$ faster inference speed. Experiments show that our framework is able to learn semantically-rich discrete codes efficiently and robustly, resulting in much better image reconstruction quality. Our diverse image completion framework significantly outperforms the state-of-the-art both quantitatively and qualitatively on multiple benchmark datasets.

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