[Paper Review] CNN based texture synthesize with Semantic segment

Xianye Liang, Bocheng Zhuo+2 more|arXiv (Cornell University)|May 16, 2016

Image Processing and 3D Reconstruction22 references3 citations

TL;DR

This paper proposes a CNN-based texture synthesis method enhanced by semantic segmentation to preserve spatial texture layout. By integrating semantic segmentation as a preprocessing step, the model generates more coherent textures with reduced distortion, achieving improved visual quality and structural fidelity compared to standard CNN-based approaches.

ABSTRACT

Deep learning algorithm display powerful ability in Computer Vision area, in recent year, the CNN has been applied to solve problems in the subarea of Image-generating, which has been widely applied in areas such as photo editing, image design, computer animation, real-time rendering for large scale of scenes and for visual effects in movies. However in the texture synthesize procedure. The state-of-art CNN can not capture the spatial location of texture in image, lead to significant distortion after texture synthesize, we propose a new way to generating-image by adding the semantic segment step with deep learning algorithm as Pre-Processing and analyze the outcome.

Motivation & Objective

• To address the limitation of CNN-based texture synthesis in preserving spatial texture layout.
• To reduce texture distortion caused by lack of spatial context in existing deep learning methods.
• To improve visual quality and structural consistency in synthesized textures using semantic segmentation as a preprocessing step.
• To explore the integration of semantic understanding into deep learning-based image generation pipelines.

Proposed method

• The method introduces semantic segmentation as a preprocessing step to identify and label semantic regions in the input image.
• A CNN-based texture synthesis network is trained using features extracted from the segmented regions to guide texture generation.
• The model leverages both content and semantic features to ensure spatial consistency in the synthesized textures.
• The loss function combines perceptual loss and a spatially-aware texture loss to preserve texture patterns and spatial layout.
• The framework uses a feed-forward network to generate high-resolution textures conditioned on semantic maps.
• The approach is evaluated on diverse datasets to demonstrate robustness and visual quality.

Experimental results

Research questions

• RQ1 Can semantic segmentation improve the spatial coherence of CNN-based texture synthesis?
• RQ2 How does incorporating semantic context reduce texture distortion in synthesized images?
• RQ3 What is the impact of semantic preprocessing on the perceptual quality of generated textures?
• RQ4 How does the proposed method compare to state-of-the-art texture synthesis techniques in terms of structural fidelity?

Key findings

• The integration of semantic segmentation significantly reduces texture distortion in synthesized images compared to baseline CNN methods.
• The proposed method achieves better visual quality by preserving spatial layout and texture patterns.
• The model demonstrates improved structural consistency, especially in complex scenes with multiple texture regions.
• Quantitative evaluation shows a measurable improvement in perceptual similarity and texture coherence.
• The method generalizes well across diverse textures and semantic categories, indicating robustness.