toplogo
Sign In

Image Inpainting via Dual Generation of Texture and Structure


Core Concepts
The core message of this paper is that image inpainting can be effectively achieved by jointly modeling structure-constrained texture synthesis and texture-guided structure reconstruction in a two-stream network architecture, which allows the two subtasks to better leverage each other for more plausible generation.
Abstract
The paper proposes a novel two-stream network for image inpainting, which casts the task into two collaborative subtasks: structure-constrained texture synthesis and texture-guided structure reconstruction. The two parallel-coupled streams are individually modeled and combined to complement each other. A Bi-directional Gated Feature Fusion (Bi-GFF) module is introduced to integrate the rebuilt structure and texture feature maps to enhance their consistency, along with a Contextual Feature Aggregation (CFA) module to highlight the clues from distant spatial locations to render finer details. The key highlights are: The two-stream architecture models structure and texture generation in a coupled manner, allowing them to better leverage each other for more plausible results. The Bi-GFF module exchanges and combines the structure and texture information to improve their consistency. The CFA module refines the generated contents by region affinity learning and multi-scale feature aggregation. Extensive experiments on multiple public benchmarks demonstrate the superiority of the proposed method over state-of-the-art approaches both qualitatively and quantitatively.
Stats
Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors. Current solutions are incompetent in handling the cases with large corruptions, and they generally suffer from distorted results.
Quotes
"Due to the lack of proper interaction with im- age texture during structure reconstruction, however, cur- rent solutions are incompetent in handling the cases with large corruptions, and they generally suffer from distorted results." "To deal with this problem, a number of multi-stage meth- ods are proposed to explicitly incorporate structure model- ing, which hallucinate structures of missing regions in the first stage and use them to guide pixel generation in the sec- ond stage."

Deeper Inquiries

How can the proposed dual generation architecture be extended to other low-level vision tasks beyond image inpainting

The proposed dual generation architecture can be extended to other low-level vision tasks beyond image inpainting by adapting the concept of structure-constrained texture synthesis and texture-guided structure reconstruction to tasks such as image denoising, super-resolution, and image restoration. For image denoising, the network can learn to generate clean textures while preserving the underlying structure of the image. In super-resolution tasks, the architecture can focus on generating high-resolution textures guided by the low-resolution structure. For image restoration, the network can reconstruct missing or damaged regions by leveraging the relationship between textures and structures. By applying the dual generation approach to these tasks, the model can produce more visually appealing and contextually accurate results.

What are the potential limitations of the current two-stream network design, and how can it be further improved to handle more challenging cases

One potential limitation of the current two-stream network design is its reliance on predefined structure priors, such as edges and contours, which may not always capture the full complexity of image structures. To address this limitation and handle more challenging cases, the network can be further improved in the following ways: Dynamic Structure Priors: Instead of relying solely on fixed structure priors like edges, the network can be enhanced to dynamically adapt and learn structure priors from the input data. This adaptive approach can help the model better capture diverse and intricate structures in images. Attention Mechanisms: Integrating attention mechanisms into the network can allow it to focus on relevant regions of the image during the generation process. This can help improve the quality of both texture synthesis and structure reconstruction by emphasizing important features. Generative Adversarial Training: Incorporating adversarial training techniques can enhance the network's ability to generate realistic textures and structures by learning from the distribution of real images. Adversarial training can help the model produce more visually convincing results in challenging scenarios.

What other types of structural priors beyond edges and contours could be explored to better guide the texture synthesis process

Beyond edges and contours, other types of structural priors that could be explored to better guide the texture synthesis process include: Semantic Segmentation Masks: Utilizing semantic segmentation masks as structural priors can help the network understand the semantic content of the image and generate textures that align with different object categories. This can lead to more contextually relevant and coherent inpainting results. Skeletons and Key Points: Incorporating skeletons or key points of objects in the image as structural priors can provide a more detailed and fine-grained guidance for texture synthesis. By focusing on key structural elements, the network can generate textures that align with the underlying object shapes and forms. Hierarchical Structures: Exploring hierarchical structures in images, such as object hierarchies or scene compositions, can offer a more comprehensive understanding of the image layout. By incorporating hierarchical structural priors, the network can generate textures that respect the spatial relationships and dependencies within the image, leading to more realistic and contextually consistent results.
0