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: ...
Thies, J., Zollhöfer, M., Nießner, M.: Deferred neural rendering: Image synthesis using neural textures. Acm Trans. Graphic (TOG) 38(4), 1–12 (2019) Article Google Scholar Guo, Z., Yang, G., Zhang, D., Xia, M.: Rethinking gradient operator for exposing ai-enabled face ...
Textures for realistic image synthesis - GREENBERG, CAREY - 1985 () Citation Context ...se term, and is easy to use in a Monte Carlo framework. 1 Introduction Physically-based rendering systems describe reflection behavior using the bidirectional reflectance distribution function (BRDF) =-=[3]. ...
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...
GANs are composed of a generator and discriminator neural network, where the generator creates synthetic imagery from random vectors, and the discriminator tells the difference between them. This dynamic forces the generator to refine synthesis and produce more realistic imagery [308]. Halder et al....
Practical generation of video textures using the auto-regressive process Recently, there have been several attempts at creating 'video textures', that is, synthesising new (potentially infinitely long) video clips based on exist... N Campbell,C Dalton,D Gibson,... - 《Image & Vision Computing...
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 ...
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 ...
Segmentation Method of Texture Image Using a Wavelet Transform and Neural Network This paper deals with a segmentation method of an image composed of some kinds of textures with randomness by using a wavelet transform and neural networks... Y Shinohara,S Oe - 《Ieej Transactions on Electronics In...