We present a method for large-mask pluralistic image inpainting based on the
generative framework of discrete latent codes. Our method learns latent priors,
discretized as tokens, by only performing computations at the visible locations
of the image. This is realized by a restrictive partial encoder that predicts
the token label for each visible block, a bidirectional transformer that infers
the missing labels by only looking at these tokens, and a dedicated synthesis
network that couples the tokens with the partial image priors to generate
coherent and pluralistic complete image even under extreme mask settings.
Experiments on public benchmarks validate our design choices as the proposed
method outperforms strong baselines in both visual quality and diversity
metrics.

大掩蔽众生成图像修复方法，基于离散潜变量的生成框架，通过仅在图像可见位置执行计算的方式来学习潜在先验，利用限制性部分编码器预测每个可见块的令牌标签，双向转换器通过仅查看这些令牌推断缺失的标签，专用合成网络将令牌与部分图像先验相耦合，即使在极端掩蔽设置下也能生成连贯且多样的完整图像，公共基准测试表明所提方法在视觉质量和多样性指标上优于强基线模型。