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Generative Modelling of BRDF Textures from Flash Images

Philipp Henzler, Valentin Deschaintre, Niloy J. Mitra, Tobias Ritschel

University College London; Adobe Research; Imperial College London

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We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flashlight illumination, first it is converted into a latent material code. Then, in the second step, conditioned on the material code, our method produces an infinite and diverse spatial field of BRDF model parameters (diffuse albedo, normals, roughness, specular albedo) that subsequently allows rendering in complex scenes and illuminations, matching the appearance of the input photograph. Technically, we jointly embed all flash images into a latent space using a convolutional encoder, and -- conditioned on these latent codes -- convert random spatial fields into fields of BRDF parameters using a convolutional neural network (CNN). We condition these BRDF parameters to match the visual characteristics (statistics and spectra of visual features) of the input under matching light. A user study compares our approach favorably to previous work, even those with access to BRDF supervision.

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Textures in Graphics; Material Modeling

Two-Stream Convolutional Networks for Dynamic Texture Synthesis

Matthew Tesfaldet, Marcus A. Brubaker, Konstantinos G. Derpanis

York University; Pyerson University

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We introduce a two-stream model for dynamic texture synthesis. Our model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow prediction. Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the optical flow ConvNet model its dynamics. To generate a novel texture, a randomly initialized input sequence is optimized to match the feature statistics from each stream of an example texture. Inspired by recent work on image style transfer and enabled by the two-stream model, we also apply the synthesis approach to combine the texture appearance from one texture with the dynamics of another to generate entirely novel dynamic textures. We show that our approach generates novel, high quality samples that match both the framewise appearance and temporal evolution of input texture. Finally, we quantitatively evaluate our texture synthesis approach with a thorough user study.

High-Resolution Multi-Scale Neural Texture Synthesis

Xavier Snelgrove

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We introduce a novel multi-scale approach for synthesizing high-resolution natural textures using convolutional neural networks trained on image classification tasks. Previous breakthroughs were based on the observation that correlations between features at intermediate layers of the network are a powerful texture representation, however the fixed receptive field of network neurons limits the maximum size of texture features that can be synthesized. We show that rather than matching statistical properties at many layers of the CNN, better results can be achieved by matching a small number of network layers but across many scales of a Gaussian pyramid. This leads to qualitatively superior synthesized high-resolution textures.

Texture Synthesis Using Convolutional Neural Networks

Leon A. Gatys, Alexander S. Ecker, Matthias Bethge

Centre for Integrative Neuroscience, University of Tubingen; Bernstein Center for Computational Neuroscience; Graduate School of Neural Information Processing, University of Tubingen; Max Planck Institute for Biological Cybernetics; Baylor College of Medicine

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Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion. Within the model, textures are represented by the correlations between feature maps in several layers of the network. We show that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit. The model provides a new tool to generate stimuli for neuroscience and might offer insights into the deep representations learned by convolutional neural networks.

INS: Unified Implicit Neural Stylization

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Category: Gram Matrix

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