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Texture Synthesis

The fundamental goal of example-based Texture Synthesis is to generate a texture, usually larger than the input, that faithfully captures all the visual characteristics of the exemplar, yet is neither identical to it, nor exhibits obvious unnatural looking artifacts.

Source: Non-Stationary Texture Synthesis by Adversarial Expansion

Papers

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Showing 21–30 of 280 papers

TitleStatusHype
Texture Image Synthesis Using Spatial GAN Based on Vision Transformers—0
InsTex: Indoor Scenes Stylized Texture Synthesis—0
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets GenerationCode11
Dynamic Neural Style Transfer for Artistic Image Generation using VGG19—0
CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh GenerationCode2
DoubleDiffusion: Combining Heat Diffusion with Denoising Diffusion for Texture Generation on 3D MeshesCode1
Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological Tissue Segmentation in Lung CT Scans—0
TexGarment: Consistent Garment UV Texture Generation via Efficient 3D Structure-Guided Diffusion Transformer—0
Disentangled Pose and Appearance Guidance for Multi-Pose Generation—0
DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network—0
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