Incorporating long-range consistency in CNN-based texture generation
Gatys et al. (2015) showed that pair-wise products of features in a convolutional network are a very effective representation of image textures. We propose a simple modification to that representation which makes it possible to incorporate long-range structure into image generation, and to render images that satisfy various symmetry constraints. We show how this can greatly improve rendering of regular textures and of images that contain other kinds of symmetric structure. We also present applications to inpainting and season transfer.
Code (4)
Tasks
Image GenerationTexture SynthesisSimilar Papers 제목 키워드 기반
Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations
The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos. The major drawback of existing dynamic texture synthesis models comes from poor treatment of…
Texture SynthesisMilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation
Video generative models have become increasingly powerful, but long-range consistency remains challenging to achieve because even a few dozen frames require impractically long transformer sequence lengths. We show that t…
Video GenerationTexGarment: Consistent Garment UV Texture Generation via Efficient 3D Structure-Guided Diffusion Transformer
This paper introduces TexGarment, an efficient method for synthesizing high-quality, 3D-consistent garment textures in UV space. Traditional approaches based on 2D-to-3D mapping often suffer from 3D inconsistency, wh…
Texture SynthesisGeometry-to-Image Synthesis-Driven Generative Point Cloud Registration
In this paper, we propose a novel 3D registration paradigm, Generative Point Cloud Registration, which bridges advanced 2D generative models with 3D matching tasks to enhance registration performance. Our key idea is to …
Point Cloud RegistrationPoint CloudsTowards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2
We present a new approach for universal texture synthesis by incorporating a multi-scale texton broadcasting module in the StyleGAN-2 framework. The texton broadcasting module introduces an inductive bias, enabling gener…
DiversityInductive BiasTexture Synthesis