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Item Neural Smoke Stylization with Color Transfer(The Eurographics Association, 2020) Christen, Fabienne; Kim, Byungsoo; Azevedo, Vinicius C.; Solenthaler, Barbara; Wilkie, Alexander and Banterle, FrancescoArtistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies workflows as it transfers the style of arbitrary input images onto 3D smoke simulations. However, the method only modifies the shape of the fluid but omits color information. In this work, we therefore extend the previous approach to obtain a complete pipeline for transferring shape and color information onto 2D and 3D smoke simulations with neural networks. Our results demonstrate that our method successfully transfers colored style features consistently in space and time to smoke data for different input textures.Item Neural Denoising for Spectral Monte Carlo Rendering(The Eurographics Association, 2022) Rouphael, Robin; Noizet, Mathieu; Prévost, Stéphanie; Deleau, Hervé; Steffenel, Luiz-Angelo; Lucas, Laurent; Sauvage, Basile; Hasic-Telalovic, JasminkaSpectral Monte Carlo (MC) rendering is still to be largely adopted partially due to the specific noise, called color noise, induced by wavelength-dependent phenomenons. Motivated by the recent advances in Monte Carlo noise reduction using Deep Learning, we propose to apply the same approach to color noise. Our implementation and training managed to reconstruct a noise-free output while conserving high-frequency details despite a loss of contrast. To address this issue, we designed a three-step pipeline using the contribution of a secondary denoiser to obtain high-quality results.Item Is Drawing Order Important?(The Eurographics Association, 2023) Qiu, Sherry; Wang, Zeyu; McMillan, Leonard; Rushmeier, Holly; Dorsey, Julie; Babaei, Vahid; Skouras, MelinaThe drawing process is crucial to understanding the final result of a drawing. There has been a long history of understanding human drawing; what kinds of strokes people use and where they are placed. An area of interest in Artificial Intelligence is developing systems that simulate human behavior in drawing. However, there has been little work done to understand the order of strokes in the drawing process. Without sufficient understanding of natural drawing order, it is difficult to build models that can generate natural drawing processes. In this paper, we present a study comparing multiple types of stroke orders to confirm findings from previous work and demonstrate that multiple orderings of the same set of strokes can be perceived as human-drawn and different stroke order types achieve different perceived naturalness depending on the type of image prompt.Item From Capture to Immersive Viewing of 3D HDR Point Clouds(The Eurographics Association, 2022) Loscos, Celine; Souchet, Philippe; Barrios, Théo; Valenzise, Giuseppe; Cozot, Rémi; Hahmann, Stefanie; Patow, Gustavo A.The collaborators of the ReVeRY project address the design of a specific grid of cameras, a cost-efficient system that acquires at once several viewpoints, possibly under several exposures and the converting of multiview, multiexposed, video stream into a high quality 3D HDR point cloud. In the last two decades, industries and researchers proposed significant advances in media content acquisition systems in three main directions: increase of resolution and image quality with the new ultra-high-definition (UHD) standard; stereo capture for 3D content; and high-dynamic range (HDR) imaging. Compression, representation, and interoperability of these new media are active research fields in order to reduce data size and be perceptually accurate. The originality of the project is to address both HDR and depth through the entire pipeline. Creativity is enhanced by several tools, which answer challenges at the different stages of the pipeline: camera setup, data processing, capture visualisation, virtual camera controller, compression, perceptually guided immersive visualisation. It is the experience acquired by the researchers of the project that is exposed in this tutorial.Item Interactive Flat Coloring of Minimalist Neat Sketches(The Eurographics Association, 2020) Parakkat, Amal Dev; Madipally, Prudhviraj; Gowtham, Hari Hara; Cani, Marie-Paule; Wilkie, Alexander and Banterle, FrancescoWe introduce a simple Delaunay-triangulation based algorithm for the interactive coloring of neat line-art minimalist sketches, ie. vector sketches that may include open contours. The main objective is to minimize user intervention and make interaction as natural as with the flood-fill algorithm while extending coloring to regions with open contours. In particular, we want to save the user from worrying about parameters such as stroke weight and size. Our solution works in two steps, 1) a segmentation step in which the input sketch is automatically divided into regions based on the underlying Delaunay structure and 2) the interactive grouping of neighboring regions based on user input. More precisely, a region adjacency graph is computed from the segmentation result, and is interactively partitioned based on user input to generate the final colored sketch. Results show that our method is as natural as a bucket fill tool and powerful enough to color minimalist sketches.Item Deep-Eyes: Fully Automatic Anime Character Colorization with Painting of Details on Empty Pupils(The Eurographics Association, 2020) Akita, Kenta; Morimoto, Yuki; Tsuruno, Reiji; Wilkie, Alexander and Banterle, FrancescoMany studies have recently applied deep learning to the automatic colorization of line drawings. However, it is difficult to paint empty pupils using existing methods because the networks are trained with pupils that have edges, which are generated from color images using image processing. Most actual line drawings have empty pupils that artists must paint in. In this paper, we propose a novel network model that transfers the pupil details in a reference color image to input line drawings with empty pupils. We also propose a method for accurately and automatically coloring eyes. In this method, eye patches are extracted from a reference color image and automatically added to an input line drawing as color hints using our eye position estimation network.Item Robust Image Denoising using Kernel Predicting Networks(The Eurographics Association, 2021) Cai, Zhilin; Zhang, Yang; Manzi, Marco; Oztireli, Cengiz; Gross, Markus; Aydin, Tunç Ozan; Theisel, Holger and Wimmer, MichaelWe present a new method for designing high quality denoisers that are robust to varying noise characteristics of input images. Instead of taking a conventional blind denoising approach or relying on explicit noise parameter estimation networks as well as invertible camera imaging pipeline models, we propose a two-stage model that first processes an input image with a small set of specialized denoisers, and then passes the resulting intermediate denoised images to a kernel predicting network that estimates per-pixel denoising kernels. We demonstrate that our approach achieves robustness to noise parameters at a level that exceeds comparable blind denoisers, while also coming close to state-of-the-art denoising quality for camera sensor noise.Item Luminance-Preserving and Temporally Stable Daltonization(The Eurographics Association, 2023) Ebelin, Pontus; Crassin, Cyril; Denes, Gyorgy; Oskarsson, Magnus; Åström, Kalle; Akenine-Möller, Tomas; Babaei, Vahid; Skouras, MelinaWe propose a novel, real-time algorithm for recoloring images to improve the experience for a color vision deficient observer. The output is temporally stable and preserves luminance, the most important visual cue. It runs in 0.2 ms per frame on a GPU.Item UV Completion with Self-referenced Discrimination(The Eurographics Association, 2020) Kang, Jiwoo; Lee, Seongmin; Lee, Sanghoon; Wilkie, Alexander and Banterle, FrancescoA facial UV map is used in many applications such as facial reconstruction, synthesis, recognition, and editing. However, it is difficult to collect a number of the UVs needed for accuracy using 3D scan device, or a multi-view capturing system should be required to construct the UV. An occluded facial UV with holes could be obtained by sampling an image after fitting a 3D facial model by recent alignment methods. In this paper, we introduce a facial UV completion framework to train the deep neural network with a set of incomplete UV textures. By using the fact that the facial texture distributions of the left and right half-sides are almost equal, we devise an adversarial network to model the complete UV distribution of the facial texture. Also, we propose the self-referenced discrimination scheme that uses the facial UV completed from the generator for training real distribution. It is demonstrated that the network can be trained to complete the facial texture with incomplete UVs comparably to when utilizing the ground-truth UVs.Item Neural Film Grain Rendering(The Eurographics Association and John Wiley & Sons Ltd., 2025) Lesné, Gwilherm; Gousseau, Yann; Ladjal, Saïd; Newson, Alasdair; Bousseau, Adrien; Day, AngelaFilm grain refers to the specific texture of film-acquired images, due to the physical nature of photographic film. Being a visual signature of such images, there is a strong interest in the film-industry for the rendering of these textures for digital images. Some previous works are able to closely mimic the physics of films and produce high quality results, but are computationally expensive. We propose a method based on a lightweight neural network and a texture aware loss function, achieving realistic results with very low complexity, even for large grains and high resolutions. We evaluate our algorithm both quantitatively and qualitatively with respect to previous work.