Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE (2103.10022v1)

Abstract: Given an incomplete image without additional constraint, image inpainting natively allows for multiple solutions as long as they appear plausible. Recently, multiplesolution inpainting methods have been proposed and shown the potential of generating diverse results. However, these methods have difficulty in ensuring the quality of each solution, e.g. they produce distorted structure and/or blurry texture. We propose a two-stage model for diverse inpainting, where the first stage generates multiple coarse results each of which has a different structure, and the second stage refines each coarse result separately by augmenting texture. The proposed model is inspired by the hierarchical vector quantized variational auto-encoder (VQ-VAE), whose hierarchical architecture isentangles structural and textural information. In addition, the vector quantization in VQVAE enables autoregressive modeling of the discrete distribution over the structural information. Sampling from the distribution can easily generate diverse and high-quality structures, making up the first stage of our model. In the second stage, we propose a structural attention module inside the texture generation network, where the module utilizes the structural information to capture distant correlations. We further reuse the VQ-VAE to calculate two feature losses, which help improve structure coherence and texture realism, respectively. Experimental results on CelebA-HQ, Places2, and ImageNet datasets show that our method not only enhances the diversity of the inpainting solutions but also improves the visual quality of the generated multiple images. Code and models are available at: https://github.com/USTC-JialunPeng/Diverse-Structure-Inpainting.

• The paper introduces a two-stage hierarchical VQ-VAE that generates diverse structural layouts from incomplete images.
• It employs a structural attention module and novel feature losses to refine texture details for enhanced visual fidelity.
• Quantitative results on CelebA-HQ, Places2, and ImageNet show significant improvements in diversity and realism compared to prior methods.

Analysis of "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"

The paper "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE" by Peng et al. advances the field of image inpainting by addressing the limitations faced by prior methods in generating diverse and high-quality solutions. The paper emphasizes the disentanglement of structural and textural information using hierarchical VQ-VAE, which is crucial for improving both diversity and visual quality in image completion tasks.

The research builds on the insight that traditional image inpainting methods often yield a deterministic mapping from incomplete images to full reconstructions, restricting the capability to explore plausible alternatives that align with human perception. This work proposes a two-stage model leveraging the strengths of hierarchical vector quantized variational auto-encoder (VQ-VAE) architectures. The model first generates multiple diverse structural configurations at a coarse level before refining each with appropriate texture details, enhancing the overall inpainting capabilities.

Methodology Overview

Key to the approach is the use of a hierarchical VQ-VAE, allowing for the disentanglement of image structure from texture and enabling more sophisticated autoregressive modeling. The discrete nature of the latent variables in VQ-VAE is exploited to avoid the posterior collapse issue common in VAEs. The hierarchical architecture allocated specific latent spaces for structure and texture, thus effectively separating these components to be processed differently.

1. Diverse Structure Generation: The initial stage of their model generates varied structural solutions from an incomplete image input by sampling from a conditioned autoregressive distribution. This mechanism allows for multiple plausible structural outcomes while maintaining coherence with the existing parts of the image.
2. Texture Generation and Refinement: The generated structures then guide a texture completion stage that employs a structural attention module to align texture generation with the structural context, further refined by novel feature losses to ensure realism and coherence in the final output.

Experimental Results and Implications

Quantitative and qualitative assessments on well-known datasets such as CelebA-HQ, Places2, and ImageNet reveal the efficacy of the proposed method. Notably, the approach leads to significant improvements in Inception Score (IS), Modified Inception Score (MIS), and Fréchet Inception Distance (FID) when compared to prior multiple-solution methods like PIC and UCTGAN. Such metrics reflect both the diversity and fidelity of the generated results.

The implications of this work extend from theoretical contributions to practical applications:

• Theoretical Framework: It provides a viable pathway to enhance image inpainting with probabilistically diverse outputs, crucial for applications needing plausible variation like content creation and restoration.
• Enhancements in AI Techniques: Its dual-stage strategy, separating structure and texture, paves the way for further exploration in related fields such as texture synthesis and automated photo editing.

Future Directions

The paper concludes with potential extensions of the technique to high-resolution images and suggests possible applications in related conditional image generation tasks such as style transfer and super-resolution. Future research might build upon this foundational work by integrating more complex autoregressive models or exploring training schemas that balance the trade-offs between diversity and inpainting quality more effectively.

Overall, the paper makes substantial contributions to the ongoing progress in image inpainting, providing practical solutions and opening new avenues for exploration in AI-based image processing.