Paint3D introduces a groundbreaking generative framework for creating high-resolution, lighting-less, and diverse 2K UV texture maps for untextured 3D meshes, conditioned on text or image inputs. Addressing the challenge of generating quality textures without embedded illumination information, the method employs a coarse-to-fine approach. It initially utilizes a pre-trained depth-aware 2D diffusion model for multi-view texture fusion, producing a coarse texture map. To overcome incomplete areas and illumination artifacts, separate UV Inpainting and UVHD diffusion models are trained for shape-aware refinement. The resulting process yields high-quality 2K UV textures with semantic consistency, allowing for significant advancements in texturing 3D objects and providing flexibility for re-lighting and editing within modern graphics pipelines.

Neural Cellular Automata (NCA) are capable of learning diverse behaviors and can solve complex tasks through massively parallel and inherently degenerate processes. The article focuses on applying NCA to the task of texture synthesis, reproducing the general appearance of a texture template rather than pixel-perfect copies. After training NCA models to reproduce textures, the article investigates their learned behaviors and observes surprising effects, suggesting that the cells learn distributed, local algorithms. The article employs NCA as a differentiable image parameterization to accomplish this.

Generative Adversarial Networks (GANs) are an exciting tool for artists, allowing them to create unique, unpredictable digital art. The GAN process simulates a game, with a Critic network and a generator network competing to create realistic images. The artist works as a curator, selecting the most interesting images produced by the generator. The author recommends using CycleGAN, a neural network architecture that transforms images from one dataset into the style of another, as it allows for high-resolution images and quick training. The author offers practical advice on using CycleGAN, such as fine-tuning models on smaller datasets and experimenting with different batch sizes. The author also emphasizes the importance of using unique, personal datasets for training. Overall, the author encourages artists to experiment with GANs and to let the unpredictability of the process inspire them to create something special.

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