To address this challenging problem, we introduce Deferred Neural Rendering, a new paradigm for image synthesis that combines the traditional graphics pipeline with learnable components. Specifically, we propose Neural Textures, which are learned feature maps that are trained as part of the scene ...
This repository implementsDeferred Neural Rendering: Image Synthesis using Neural Textures. Requirements Python 3.6+ argparse nni NumPy Pillow pytorch tensorboardX torchvision tqdm File Organization The root directory contains several subdirectories and files: ...
Therefore, to train CNNs we design a framework to perform FFT upsampling in feature space using deformable convolutions. Such design allows our framework to generalize to unseen images, and synthesize textures in a single pass. Extensive evaluations confirm that our method achieves state-of-the-...
Deep neural networks have been successfully applied to problems such as image segmentation, image super-resolution, coloration and image inpainting. In this work we propose the use of convolutional neural networks (CNN) for image inpainting of large regions in high-resolution textures. Due to limited...
Given that text description can contain details which can be useful on different layers of the network, we propose injecting repeated instances of the text description throughout the network. For example, color may be useful in the lower layers while textures may be useful in higher layers. The...
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows to apply them to image modification tasks such as inpainting directly ...
9.7.3 Neural textures Using neural textures [85] for novel view synthesis enables high fidelity rendering of objects with complex appearance, including the reproduction of view-dependent effects. Moreover, they can conceal imperfections of the geometry itself without ever touching it, thus being compl...
Training data augmentation using GANs can be already regarded as a standard approach (see also [124]), despite that it is used exclusively with the modern neural networks that became widespread only very recently. It is worth noting that the GANs are often “only” adding textures over cell ...
THE RANDOM NEURAL NETWORK MODEL FOR TEXTURE GENERATION The generation of artifical textures is a useful function in image synthesis systems. The purpose of this paper is to describe the use of the random neural... V Atalay,E Gelenbe,N Yalabik - 《International Journal of Pattern Recognition & ...
synthesis performance using only image diffusion models, while avoiding the pitfalls of previous distillation-based methods. The text-conditioning offers detailed control and we also do not rely on any ground truth 3D textures for training. This makes our method versatile and applicable to a broad ...