Texture Synthesis
71 papers with code • 0 benchmarks • 3 datasets
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
Benchmarks
These leaderboards are used to track progress in Texture Synthesis
3D-FUTURE
QST
CBTex
Latest papers
TexTile: A Differentiable Metric for Texture Tileability
crp94/textile
•
•
We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i. e., the tileability).
Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
Lakonik/MVEdit
•
•
Open-domain 3D object synthesis has been lagging behind image synthesis due to limited data and higher computational complexity.
3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors
3dtopia/3dtopia
•
•
Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping.
Generating Non-Stationary Textures using Self-Rectification
xiaorongjun000/self-rectification
•
•
This paper addresses the challenge of example-based non-stationary texture synthesis.
Iterative Token Evaluation and Refinement for Real-World Super-Resolution
chaofengc/iter
•
•
Distortion removal involves simple HQ token prediction with LQ images, while texture generation uses a discrete diffusion model to iteratively refine the distortion removal output with a token refinement network.
Enhancing Object Coherence in Layout-to-Image Synthesis
Layout-to-image synthesis is an emerging technique in conditional image generation.
Towards Garment Sewing Pattern Reconstruction from a Single Image
In this work, we explore the challenging problem of recovering garment sewing patterns from daily photos for augmenting these applications.
Generating Infinite-Size Textures using GANs with Patch-by-Patch Paradigm
ai4netzero/infinite_texture_gans
•
•
Existing texture synthesis techniques rely on generating large-scale textures using a single forward pass to the generative model; this approach limits the scalability and flexibility of the images produced.
ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering
qiminchen/shaddr
•
•
Furthermore, we showcase the ability of our method to learn geometric details and textures from shapes reconstructed from real-world photos.
A geometrically aware auto-encoder for multi-texture synthesis
pierrickch/textureautoencoder
•
•
We propose an auto-encoder architecture for multi-texture synthesis.
