Realistic Texture

Realistic texture generation aims to create convincingly lifelike surface details for digital objects and environments, bridging the gap between virtual and real-world appearances. Current research focuses on improving texture quality and diversity using diffusion models, generative adversarial networks (like StyleGAN), and neural fields, often incorporating guidance from text descriptions, reference images, or underlying 3D geometry to enhance realism and control. These advancements are impacting diverse fields, from creating high-fidelity 3D avatars and virtual environments to generating realistic satellite imagery and improving image compression techniques. The ultimate goal is to achieve universal texture synthesis, capable of generating a wide range of textures with high fidelity and efficiency.