DOCSLIB.ORG

On Demand Solid Texture Synthesis Using Deep 3D Networks Jorge Gutierrez, Julien Rabin, Bruno Galerne, Thomas Hurtut

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
On Demand Solid Texture Synthesis Using Deep 3D Networks Jorge Gutierrez, Julien Rabin, Bruno Galerne, Thomas Hurtut On Demand Solid Texture Synthesis Using Deep 3D Networks Jorge Gutierrez, Julien Rabin, Bruno Galerne, Thomas Hurtut To cite this version: Jorge Gutierrez, Julien Rabin, Bruno Galerne, Thomas Hurtut. On Demand Solid Texture Synthesis Using Deep 3D Networks. 2018. hal-01678122v1 HAL Id: hal-01678122 https://hal.archives-ouvertes.fr/hal-01678122v1 Preprint submitted on 8 Jan 2018 (v1), last revised 20 Dec 2019 (v3) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. SOLID TEXTURE GENERATIVE NETWORK FROM A SINGLE IMAGE Jorge Gutierrez? Julien Rabin Bruno Galerney Thomas Hurtut? ?Polytechnique Montreal,´ Montreal,´ Canada Normandie Univ., UniCaen, ENSICAEN, CNRS, GREYC, Caen, France yLaboratoire MAP5, Universite´ Paris Descartes and CNRS, Sorbonne Paris Cite´ ABSTRACT loss function. Then, at run-time, the network is applied to new images to perform the task which is often achieved in This paper addresses the problem of generating volumetric real-time. data for solid texture synthesis. We propose a compact, mem- ory efficient, convolutional neural network (CNN) which is Contributions We introduce a solid texture generator based trained from an example image. The features to be repro- on convolutional neural networks that is capable of synthe- duced are analyzed from the example using deep CNN filters sizing solid textures from a random input at interactive rates. that are previously learnt from a dataset. After training the Taking inspiration from previous work on image texture syn- generator is capable of synthesizing solid textures of arbitrary thesis with CNN, a framework is proposed for the training of sizes controlled by the user. The main advantage of the pro- this solid texture generator. A volumetric loss function based posed approach in comparison to previous patch-based meth- on deep CNN filters is defined by comparing multiple view- ods is that the creation of a new volume of data can be done point slices of the generated textures to a single example im- in interactive rates. age. This model ensures that each cross-section of the gener- Index Terms— Texture synthesis, convolutional neural ated solid shares the same visual characteristics as the input networks, volumetric data generation, solid textures. example. To the best of our knowledge, there is no such archi- tecture proposed in the literature to generate volumetric color data. 1. INTRODUCTION Outline The paper is organized as follows: Section2 gives a short overview of the literature about solid texture synthesis Motivation and context Besides imaging problems which methods and 2D texture synthesis using neural networks, Sec- deal with volumetric data (such as MRI in medical imag- tion3 describes the proposed generative model and Section4 ing, or 3D seismic geological data), the main application of gives the implementation details and some visual results. solid texture synthesis originates from computer graphics. Since [1,2], this approach has been proposed to create 3D objects as an alternative to texture mapping or synthesis onto 2. RELATED WORKS ON TEXTURE SYNTHESIS 3D meshes. It is an efficient and elegant way of dealing with many problems that arise with the later approaches, such as Let us give a quick review on example-based texture synthe- time consuming artist design, mapping distorsion and seams, sis, first with a focus on solid texture synthesis and second on etc. Once the volumetric information is generated, surfaces convolutional networks for image generation. can be carved inside without further parametrization no mat- ter how intricate they are. However, several reasons still limit 2.1. Solid texture synthesis the use of such techniques in practice: i) storing volumetric data is prohibitive (HD quality requires several GB of mem- Procedural texture synthesis Procedural methods are a very ory); ii) learning to generate such data is a difficult task as special category which consists of evaluating a specific noise there is no 3D examples; iii) previous synthesis approaches function depending on the spatial coordinates to achieve real- based on optimizing a matching criterion do not achieve time synthesis with minimal memory requirements [2,3]. Un- real-time performance. fortunately, these methods often fail at reproducing real-world In this work, we investigate the use of Convolutional Neu- examples with structured features. ral Networks (CNN) for solid texture synthesis. The general Example-based solid texture synthesis Most successful principle of CNN is first to off-line train a network to achieve methods focus on generating volumetric color data (grids of a specific task, usually by optimizing its parameters given a voxels) whose cross-sections are visually similar to a respec- This work was partially funded by CONACYT and I2T2 Mexico tive input texture example. In [4,5] an analog “slicing” strategy is used, where the network to generate texture samples from random inputs that texture is generated iteratively, alternating between indepen- produce the same visual features as the texture example. As in dent 2D synthesis of the slices along each of the three or- the previous framework, the objective loss function to be min- thogonal axes of the cartesian grid, and 3D aggregation by imized relies on the comparison between feature activations in averaging. The main difference between the two approaches a pre-trained discriminative CNN from the output and the tex- is the features to be considered: steerable filters coefficients ture example. However, this time, instead of optimizing the and laplacian pyramid in [4] and spectral information in [5]. generated output image the training aims at tuning the param- Generalizing some previous work on texture synthesis, eters of the generative network. Such optimization is more in- Wei [6] designed an algorithm which consists of modifying tensive as much more variables are now involved, but it only the voxels of a solid texture initialized randomly by copying needs to be done once and for all. This is achieved in prac- the average color values from an image in a raster scan order tice using back propagation and a gradient-based optimization based on the most similar neighborhood extracted along each algorithm using batches of random inputs. Once trained, the axis. This strategy has been refined and accelerated by Dong generator is able to quickly generate samples similar to the in- et al. [7] by pre-processing compatible 3D neighborhoods ac- put example by forward evaluation. Feed-forward texture net- cordingly to the examples given along some axis. works methods generally produce results with slightly lower Closely related to these methods, patch-based approaches quality than the image optimization using CNN ones but it [8,9] iteratively modify the local 2D neighborhood of a voxel exhibits computation times extremely shorter. to match similar neighborhoods in examples, under statistical Originally these methods train one network per texture constraints on colors or spatial coordinates. sample. Li et al. [17] proposed a training scheme to allow While recent methods achieve state-of-the-art results, one network to have the capacity to generate and mix several their main limitation is the required computation time due different textures. In order to prevent the generator from pro- to iterative update of each voxel, (several minutes except for ducing identical images, they also incorporate in the objec- [7]) thus prohibiting real-time applications. tive function a term that encourages diversity in synthesized batches, similarly to [18]. 2.2. Neural networks on texture synthesis Aside from texture synthesis, other computer vision appli- cations dealing with volumetric benefit from CNN. As an ex- Several methods based on deep CNN have been recently emple, [19] proposed recently a GAN for 3D shape inpainting proposed for 2D texture synthesis that outperform previ- and [20] proposed Octree Networks for 3D shapes generation. ous ones based on more “shallow” representation (wavelets, In the more specific problem of generating 3D objects neighborhood-based representations, etc). In the following, from several 2D view images which is close to the problem we present and discuss the most successful of such architec- considered in this work, deep learning methods have also tures. been recently introduced, including [21] for dynamic facial Image optimization Gatys et al. [10] proposed a variational texture, and [22, 23, 24] for 3D object generation from 2D framework inspired from [11] to generate an image which views. aims at matching the features of an example image. The most important aspect of this framework is that the discrep- 3. SOLID TEXTURE NETWORKS ancy is defined as the distance between Gram matrices of the input and the example images; these matrices encode the fil- Similar to the 2D feed-forward texture networks, our method ters’ responses of an image at some layers of a deep convo- consists of a solid texture generator and a training framework lutional neural network which is pretrained on a large image incorporating a descriptor network and a 3D loss. One gener- dataset [12]. Starting from a random initialization, the in- ator is to be trained for each input example, with most of the put image is then optimized using back-propagation and a computations occurring in that process. Once the generator stochastic gradient descent algorithm. is trained it can be used to quickly generate new solid sam- Since then, several variants have been using this frame- ples of arbitrary sizes (only limited by the available memory). work to improve the quality of the synthesis: for structured Here we describe each element of the framework.
Load more

Recommended publications
  • On Demand Solid Texture Synthesis Using Deep 3D Networks
    On Demand Solid Texture Synthesis Using Deep 3D Networks J. Gutierrez1 J. Rabin2 B. Galerne3 T. Hurtut1 1Polytechnique Montréal, Canada 2Normandie Univ. UniCaen, ENSICAEN, CNRS, GREYC, France 3Institut Denis Poisson, Université d’Orléans, Université de Tours, CNRS, France Abstract This paper describes a novel approach for on demand volumetric texture synthesis based on a deep learning framework that allows for the generation of high quality 3D data at interactive rates. Based on a few example images of textures, a generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes that reproduce the visual characteristics of the examples along some directions. To cope with memory limitations and computation complexity that are inherent to both high resolution and 3D processing on the GPU, only 2D textures referred to as “slices” are generated during the training stage. These synthetic textures are compared to exemplar images via a perceptual loss function based on a pre-trained deep network. The proposed network is very light (less than 100k parameters), therefore it only requires sustainable training (i.e. few hours) and is capable of very fast generation (around a second for 2563 voxels) on a single GPU. Integrated with a spatially seeded PRNG the proposed generator network directly returns an RGB value given a set of 3D coordinates. The synthesized volumes have good visual results that are at least equivalent to the state-of-the-art patch based approaches. They are naturally seamlessly tileable and can be fully generated in parallel. Keywords : Solid texture; On demand texture synthesis; Generative networks; Deep learning; 1.
    [Show full text]
  • Style & Content Analysis
    !"#$%&'&()*"%*"&+*,$#-.- Style & Content Analysis § What is content, what is style in an image? § What are the methods of classification for each? § With computation-generated images, how can the distinction be defined between style and content? § What if the style is the content? -- § Visual Modality – the ability to see: How to evaluate the degree to which pictorial expression through color, detail, depth, tonal shades are used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
    [Show full text]
  • Conditional Generative Convnets for Exemplar-Based Texture Synthesis
    1 Conditional Generative ConvNets for Exemplar-based Texture Synthesis Zi-Ming Wang1, Meng-Han Li2, Gui-Song Xia1 1Wuhan University, Wuhan, China. 2Paris Descartes University, Paris, France. Abstract—The goal of exemplar-based texture synthesis is to by sampling from the learned texture model. These methods generate texture images that are visually similar to a given are better at balancing the repetitions and innovations nature exemplar. Recently, promising results have been reported by of textures, while they usually fail to reproduce textures with methods relying on convolutional neural networks (ConvNets) pretrained on large-scale image datasets. However, these methods highly structured elements. It is worth mentioning that a few have difficulties in synthesizing image textures with non-local of these methods can be extended to sound textures [14] and structures and extending to dynamic or sound textures. In this pa- dynamic ones [15]. Some recent surveys on EBTS can be per, we present a conditional generative ConvNet (cgCNN) model founded in [9], [16]. which combines deep statistics and the probabilistic framework Recently, parametric models has been revived by the use of generative ConvNet (gCNN) model. Given a texture exemplar, the cgCNN model defines a conditional distribution using deep of deep neural networks [12], [17]–[19]. These models em- statistics of a ConvNet, and synthesize new textures by sampling ploy deep ConvNets that are pretrained on large-scale image from the conditional distribution. In contrast to previous deep datasets instead of handcrafted filters as feature extractors, and texture models, the proposed cgCNN dose not rely on pre-trained generate new samples by seeking images that maximize certain ConvNets but learns the weights of ConvNets for each input similarity between their deep features and those from the exemplar instead.
    [Show full text]
  • Texture Generation with Neural Cellular Automata
    Texture Generation with Neural Cellular Automata Alexander Mordvintsev* Eyvind Niklasson* Ettore Randazzo Google Research fmoralex, eyvind, [email protected] Abstract derlying state manifold. Furthermore, we investigate prop- erties of the learned models that are both useful and in- Neural Cellular Automata (NCA1) have shown a remark- teresting, such as non-stationary dynamics and an inherent able ability to learn the required rules to ”grow” images robustness to damage. Finally, we make qualitative claims [20], classify morphologies [26] and segment images [28], that the behaviour exhibited by the NCA model is a learned, as well as to do general computation such as path-finding distributed, local algorithm to generate a texture, setting [10]. We believe the inductive prior they introduce lends our method apart from existing work on texture generation. arXiv:2105.07299v1 [cs.AI] 15 May 2021 itself to the generation of textures. Textures in the natural We discuss the advantages of such a paradigm. world are often generated by variants of locally interact- ing reaction-diffusion systems. Human-made textures are likewise often generated in a local manner (textile weaving, 1. Introduction for instance) or using rules with local dependencies (reg- Texture synthesis is an actively studied problem of com- ular grids or geometric patterns). We demonstrate learn- puter graphics and image processing. Most of the work in ing a texture generator from a single template image, with this area is focused on creating new images of a texture the generation method being embarrassingly parallel, ex- specified by the provided image pattern [8, 17]. These im- hibiting quick convergence and high fidelity of output, and ages should give the impression, to a human observer, that requiring only some minimal assumptions around the un- they are generated by the same stochastic process that gen- erated the provided sample.
    [Show full text]
  • Texturegan: Controlling Deep Image Synthesis with Texture Patches
    TextureGAN: Controlling Deep Image Synthesis with Texture Patches Wenqi Xian y1 Patsorn Sangkloy y1 Varun Agrawal 1 Amit Raj 1 Jingwan Lu 2 Chen Fang 2 Fisher Yu 3 James Hays 1;4 1Georgia Institute of Technology 2Adobe Research 3UC Berkeley 4Argo AI Figure 1. With TextureGAN, one can generate novel instances of common items from hand drawn sketches and simple texture patches. You can now be your own fashion guru! Top row: Sketch with texture patch overlaid. Bottom row: Results from TextureGAN. Abstract eling and rendering pipeline dates back at least 20 years to image-based rendering techniques [33]. These tech- In this paper, we investigate deep image synthesis guided niques and later “image-based” graphics approaches focus by sketch, color, and texture. Previous image synthesis on re-using image content from a database of training im- methods can be controlled by sketch and color strokes but ages [22]. For a limited range of image synthesis and edit- we are the first to examine texture control. We allow a user ing scenarios, these non-parametric techniques allow non- to place a texture patch on a sketch at arbitrary locations experts to author photorealistic imagery. and scales to control the desired output texture. Our gen- In the last two years, the idea of direct image synthe- erative network learns to synthesize objects consistent with sis without using the traditional rendering pipeline has got- these texture suggestions. To achieve this, we develop a lo- ten significant interest because of promising results from cal texture loss in addition to adversarial and content loss deep network architectures such as Variational Autoen- to train the generative network.
    [Show full text]
  • Texture Synthesis and Image Inpainting for Video Compression Project Report for CSE-252C
    Texture Synthesis and Image Inpainting for Video Compression Project Report for CSE-252C Sanjeev Kumar Abstract little amount of original data. Although, building a practical video codec based on these principles would require more In this report we investigate the application of texture syn- research effort, present work is intended to be a small step thesis and image inpainting techniques for video compres- in that direction. sion. Working in the non-parametric framework, we use 3D We build upon the work of [3] which uses non-parametric patches for matching and copying. This ensures temporal texture synthesis approach for image inpainting with appro- continuity to some extent which is not possible to obtain priate choice of fill order. The contribution of present work by working with individual frames. Since, in present ap- is two fold. First, we extend the work of [3] to three dimen- plication, patches might contain arbitrary shaped and mul- sions, which, we argue, is more natural setting for video. tiple disconnected holes, fast fourier transform (FFT) and Second, we extend the work of [8], which allows to use summed area table based sum of squared difference (SSD) FFT based SSD calculation for all possible translations of a calculation [8] cannot be used. We propose a modifica- rectangular patch, to arbitrary shaped regions, thus making tion of above scheme which allows its use in present appli- it applicable to image inpainting. cation. This results in significant gain of efficiency since This report is organised as follows. In section 2 we briefly search space is typically huge for video applications.
    [Show full text]
  • Gramgan: Deep 3D Texture Synthesis from 2D Exemplars
    GramGAN: Deep 3D Texture Synthesis From 2D Exemplars Tiziano Portenier Siavash Bigdeli Orcun Goksel ETH Zurich CSEM ETH Zurich Switzerland Switzerland Switzerland Abstract We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural texture synthesis, we train deep neural models to generate textures by non-linearly combining learned noise frequencies. To achieve a highly realistic output conditioned on an exemplar patch, we propose a novel loss function that combines ideas from both style transfer and generative adversarial networks. In particular, we train the synthesis network to match the Gram matrices of deep features from a discriminator network. In addition, we propose two architectural concepts and an extrapolation strategy that significantly improve generalization performance. In particular, we inject both model input and condition into hidden network layers by learning to scale and bias hidden activations. Quantitative and qualitative evaluations on a diverse set of exemplars motivate our design decisions and show that our system performs superior to previous state of the art. Finally, we conduct a user study that confirms the benefits of our framework. 1 Introduction Texture synthesis is an important field of research with a multitude of applications in both computer vision and graphics. While vision applications mainly focus on texture analysis and understanding, in computer graphics textures are most important in the field of image manipulation and photo- realistic rendering. In this context, applications require synthesis techniques that can generate highly realistic textures. Due to the problem being highly complex, today’s applications largely rely on techniques that generate textures based on real photos, which is a time-consuming process that requires expert knowledge.
    [Show full text]
  • Deep Geometric Texture Synthesis
    Deep Geometric Texture Synthesis AMIR HERTZ∗, Tel Aviv University RANA HANOCKA∗, Tel Aviv University RAJA GIRYES, Tel Aviv University DANIEL COHEN-OR, Tel Aviv University Recently, deep generative adversarial networks for image generation have advanced rapidly; yet, only a small amount of research has focused on gener- ative models for irregular structures, particularly meshes. Nonetheless, mesh generation and synthesis remains a fundamental topic in computer graphics. In this work, we propose a novel framework for synthesizing geometric textures. It learns geometric texture statistics from local neighborhoods (i.e., local triangular patches) of a single reference 3D model. It learns deep features on the faces of the input triangulation, which is used to subdivide and generate offsets across multiple scales, without parameterization of the reference or target mesh. Our network displaces mesh vertices in any direction (i.e., in the normal and tangential direction), enabling synthesis of geometric textures, which cannot be expressed by a simple 2D displace- ment map. Learning and synthesizing on local geometric patches enables a genus-oblivious framework, facilitating texture transfer between shapes of different genus. CCS Concepts: • Computing methodologies → Neural networks; Shape analysis. Additional Key Words and Phrases: Geometric Deep Learning, Shape Syn- thesis ACM Reference Format: Amir Hertz, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or. 2020. Deep Geometric Texture Synthesis. ACM Trans. Graph. 39, 4, Article 108 (July 2020), 11 pages. https://doi.org/10.1145/3386569.3392471 1 INTRODUCTION In recent years, neural networks for geometry processing have emerged rapidly and changed the way we approach geometric prob- lems. Yet, common 3D modeling representations are irregular and unordered, which challenges the straightforward adaptation from image-based techniques.
    [Show full text]
  • Tiled Texture Synthesis
    International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 16 (2014), pp. 1667-1672 © International Research Publications House http://www. irphouse.com Tiled Texture Synthesis Komal Arora1, Radha Kathuria2 1SPGOI College of Engineering, MD University, Rohtak (India) 2M.Tech Student, SPGOI College of Engineering, MD University, Rohtak (India) Abstract Texture Synthesis main purpose is to construct a large digital image from a small digital sample image by taking advantage of its structural content. It is object of research to computer graphics and is used in many fields, 3D computer graphics and post-production of films. It can be used to fill in holes in images, create large non-repetitive background images and expand small pictures. It is important for rendering synthetic images and animations. 1. Introduction Texture synthesis is one of the hottest topics in the field of Computer Graphics, Computer Vision and Image Processing. The goal of texture synthesis is as follows: We are provide with a texture sample, we synthesized a new texture that, when seen by a human observer, appears to be generated by the same underlying process. Nowadays neighborhood-based methods are popularly used in texture synthesis, including point-based techniques [1] and patch-based techniques [2]. Hybrid texture synthesis method has also already been developed. These methods attempt to synthesize larger textures by copying selected regions (pixels or patches) of the sample texture and, in some way, obscuring region boundaries. Most of the methods can produce high quality results, but the speeds are very slow. They cannot avoid the heavy searching process in order to get a suited pixel or patch for the output image.
    [Show full text]
  • Spatio-Temporal Texture Synthesis and Image Inpainting for Video Applications
    SPATIO-TEMPORAL TEXTURE SYNTHESIS AND IMAGE INPAINTING FOR VIDEO APPLICATIONS Sanjeev Kumar, Mainak Biswas, Serge J. Belongie and Truong Q. Nguyen University of California, San Diego ABSTRACT texture synthesis is somewhat complementary to DCT etc. Texture synthesis works on more global level. The moti- In this paper we investigate the application of texture syn- vation behind present work was the perceptual quality of thesis and image inpainting techniques for video applica- output of texture synthesis and image inpainting methods tions. Working in the non-parametric framework, we use using little amount of original data. Intra block prediction 3D patches for matching and copying. This ensures tempo- and context based arithmetic coding in H.264, try to exploit ral continuity to some extent which is not possible to obtain some redundancies which are not exploited by DCT alone, by working with individual frames. Since, in present ap- but these still work on a very local level and fail to exploit plication, patches might contain arbitrary shaped and mul- redundacies at more global level. Although, building a prac- tiple disconnected holes, fast fourier transform (FFT) and tical video codec based on these principles would require summed area table based sum of squared difference (SSD) more research effort, present work is intended to be a small calculation [1] cannot be used. We propose a modification step in that direction. of above scheme which allows its use in present application. Our approach is similar to [2] which uses non-parametric This results in significant gain of efficiency since search texture synthesis approach for image inpainting with appro- space is typically huge for video applications.
    [Show full text]
  • Texturegan: Controlling Deep Image Synthesis with Texture Patches
    TextureGAN: Controlling Deep Image Synthesis with Texture Patches Wenqi Xian †1 Patsorn Sangkloy †1 Varun Agrawal 1 Amit Raj 1 Jingwan Lu 2 Chen Fang 2 Fisher Yu 3 James Hays 1,4 1Georgia Institute of Technology 2Adobe Research 3UC Berkeley 4Argo AI Figure 1. With TextureGAN, one can generate novel instances of common items from hand drawn sketches and simple texture patches. You can now be your own fashion guru! Top row: Sketch with texture patch overlaid. Bottom row: Results from TextureGAN. Abstract eling and rendering pipeline dates back at least 20 years to image-based rendering techniques [33]. These tech- In this paper, we investigate deep image synthesis guided niques and later “image-based” graphics approaches focus by sketch, color, and texture. Previous image synthesis on re-using image content from a database of training im- methods can be controlled by sketch and color strokes but ages [22]. For a limited range of image synthesis and edit- we are the first to examine texture control. We allow a user ing scenarios, these non-parametric techniques allow non- to place a texture patch on a sketch at arbitrary locations experts to author photorealistic imagery. and scales to control the desired output texture. Our gen- In the last two years, the idea of direct image synthe- erative network learns to synthesize objects consistent with sis without using the traditional rendering pipeline has got- these texture suggestions. To achieve this, we develop a lo- ten significant interest because of promising results from cal texture loss in addition to adversarial and content loss deep network architectures such as Variational Autoen- to train the generative network.
    [Show full text]