Source-linked AI summary
Learning to Incorporate Structure Knowledge for Image Inpainting
Jie Yang, Zhiquan Qi, Yong Shi
TL;DR
Image inpainting approaches may struggle to incorporate plausible image structure, and two-stage structure pipelines have limitations. This paper jointly generates images and structures, adds structure embedding and attention, and reports superior performance to several state-of-the-art methods.
Problem
Conventional inpainting can struggle to produce semantically meaningful content for large missing regions, and prior deep methods may not adequately incorporate plausible structure knowledge.
Source: §1 · pp. 1–2Method
A shared generator jointly completes images and corresponding structures, with structure embedding, attention, and pyramid structure loss to incorporate structure information.
Source: §1 · p. 2Results
The approach shows superior performance compared with several state-of-the-art inpainting methods that ignore or do not well exploit structure knowledge.
Source: §5 · p. 7Takeaways & Limitations
Ablation studies verify each proposed component for incorporating structure knowledge, and qualitative comparisons report more plausible structures and detailed contents than EG.
Source: §4.2 · p. 6, §5 · p. 7
Abstract
from arXiv · showhide
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.
1 Introduction
The introduction argues that inpainting needs structure knowledge because conventional methods struggle with semantic content in large missing regions, while prior two-stage structure methods have practical limitations. It presents a shared multi-task generator, structure embedding, attention, and pyramid structure loss as the paper’s approach.
1 Introduction
- Conventional methods often struggle to generate semantically meaningful content when large image regions are missing or corrupted.They rely on low-level features, priors, or auxiliary data rather than high-level understanding of image content and structure.Source: §1 · p. 1
- Deep learning methods synthesize high-level semantic content, but their results can lack fine textures and contain visible artifacts.The introduction also notes that deep methods generally outperform conventional approaches on inpainting.Source: §1 · pp. 1–2
- Two-stage edge or contour methods produce more visually plausible results, but can require more parameters and expose completion to unreasonable structure preconditions.The introduction also warns that without explicit structure-loss guidance, sparse structures may be weakened or forgotten in deep networks.Source: §1 · p. 2
- The proposed multi-task framework uses a shared generator to complete the image and corresponding structures simultaneously, encouraging structure knowledge during inpainting.The paper motivates this sharing by the tasks’ common need for high-level understanding and shared image semantics.Source: §1 · p. 2
- Structure embedding supplies learned structure features as completion preconditions, while attention exploits recurrent image patterns to refine generated structures and contents.A pyramid structure loss supervises structure learning and embedding.Source: §1 · p. 2
2 Related Work
The related-work section reviews deep-learning inpainting methods through their approaches to semantic synthesis, refinement, irregular masks, and explicit image structure.
2 Related Work
- Context Encoder uses an adversarially trained encoder–decoder for semantic synthesis, but can lack fine textures and produce artifacts near missing-region borders.Iizuka et al. address the information bottleneck and local continuity, while still using Poisson blending for color inconsistency.Source: §2 · p. 2
- Coarse-to-fine methods refine initial estimates, while contextual attention, Patch-Swap, and shift-connect transfer information from known image regions.Coarse-to-fine approaches assume the first-stage estimate is reasonably accurate; other methods propagate or constrain relevant surrounding information.Source: §2 · pp. 2–3
- Partial convolutions and gated convolutions target irregular missing regions through mask updating, and gated convolution also supports user-guided sketches.Source: §2 · p. 3
- Explicit-structure methods estimate missing-region edges or contours and feed them to image completion; contour prediction is suited to salient objects.Nazeri et al.’s EdgeConnect uses an edge generator, while Xiong et al. use a contour generator.Source: §2 · p. 3
3 Method
The framework uses a shared generator to complete images and predict gradients at multiple scales, while embedding learned structure features and applying attention. Pyramid structure loss supervises structure learning and embedding, alongside a hybrid image loss for inpainting.
3 Method
- The shared generator completes the image and predicts gradient maps at multiple scales, using structure generation as an auxiliary source of cues for image completion.It receives masked image, gradient and edge maps, plus the mask, as preconditions.Source: §3 · p. 3
- Gradient maps carry edge information as well as texture and high-frequency details, making them useful structure targets for joint image generation.The maps are extracted with Sobel filters; edge maps are acquired with a Canny detector.Source: §3 · p. 3
3.1 Architecture
- The architecture embeds learned structure features into decoding at multiple scales and uses attention to transfer similar features from elsewhere in the image.Attention responses are weighted sums of input features and are added back through a residual connection.Source: §3.1 · pp. 3–4
3.2 Loss Functions
- Pyramid structure loss supervises predicted gradients at each scale and regularizes edge structure using a weighted edge mask.The mask is formed by Gaussian-filtering binary ground-truth edges, which also constrains nearby locations.Source: §3.2 · p. 5
- A hybrid image loss combines pixel reconstruction, perceptual, style, and adversarial losses to supervise image completion.Perceptual loss compares VGG-19 feature spaces; style loss compares Gram matrices of selected feature maps.Source: §3.2 · p. 5
4 Experiments
Experiments compare the approach with state-of-the-art methods on CelebA, Places2, and Facade, and evaluate its components through ablation. The reported results favor the proposed approach, with qualitative comparisons and an ablation study examining its structure-aware design.
4 Experiments
- The experiments compare state-of-the-art inpainting methods and assess the multi-task framework through ablations.The paper points to supplementary material for additional results.Source: §4 · p. 5
4.1 Experimental Settings
- The evaluation uses CelebA, Places2, and Facade, comparing qualitatively and quantitatively with GL, CA, PEN-Net, and EG.The methods are identified as the comparison set; available pre-trained models were used except PEN-Net, which was reimplemented.Source: §4.1 · p. 5
4.2 Qualitative Evaluation
- On CelebA, Places2, and Facade, examples show realistic inpaintings with sharp edges and fine-detailed textures under regular and irregular masks.The figure presents Input, Ours, and Ground truth from left to right.Source: §4.2 · p. 6, Figure 5 · p. 6
- On regular-mask CelebA and Places2 comparisons, the method shows sharper facial features and object boundaries than baselines, and more plausible structures and details than EG.The cited figures compare against baselines and ground truth; the text also reports CA, GL, and PEN-Net as less likely than Ours and EG to generate plausible contents.Source: §4.2 · p. 6, Figure 6 · p. 6, Figure 7 · p. 6
4.3 Quantitative Evaluation
- The approach achieves superior performance against all baselines on CelebA and Places2.Table 1 presents the quantitative comparisons; the supplied passages do not report its individual cell values.Source: §4.3 · p. 6, Table 1 · p. 7
4.4 Ablation Study
- As multi-task learning, structure embedding, and attention are added to the baseline, performance improves or is retained, with VIF and FID enhanced by a large margin.The ablation evaluated progressively added components on over one thousand test images with regular masks; the text attributes sharper structures to multi-task learning, sharper contours and textures to embedding, and final polishing to attention.Source: §4.4 · p. 7
5 Conclusion
The paper presents a framework combining multi-task learning, structure embedding, and attention for image inpainting. Experiments report superior performance to several state-of-the-art methods, and future work will adapt the framework to other inpainting architectures.
5 Conclusion
- The framework combines multi-task learning, explicit structure embedding, and attention to use image structure knowledge for inpainting.Table 2 reports quantitative ablation results, and Figure 8 presents qualitative ablation results.Source: §5 · p. 7, Table 2 · p. 7, Figure 8 · p. 7
- The proposed approach shows superior performance compared with several state-of-the-art methods that ignore or do not well exploit structure knowledge.The conclusion also reports that ablation studies verified each proposed component.Source: §5 · p. 7
- Future work will investigate adapting the multi-task framework to other specific inpainting architectures.Source: §5 · p. 7