Temporally Coherent Video Stylization

International audienceThe transformation of video clips into stylized animations remains an active research topic in Computer Graphics. A key challenge is to reproduce the look of traditional artistic styles whilst minimizing distracting flickering and sliding artifacts; i.e. with temporal coherence. This chapter surveys the spectrum of available video stylization techniques, focusing on algorithms encouraging the temporally coherent placement of rendering marks, and discusses the trade-offs necessary to achieve coherence. We begin with flow-based adaptations of stroke based rendering (SBR) and texture advection capable of painting video. We then chart the development of the field, and its fusion with Computer Vision, to deliver coherent mid-level scene representations. These representations enable the rotoscoping of rendering marks on to temporally coherent video regions, enhancing the diversity and temporal coherence of stylization. In discussing coherence, we formalize the problem of temporal coherence in terms of three defined criteria, and compare and contrast video stylization using these

A workflow for designing stylized shading effects

In this report, we describe a workflow for designing stylized shading effects on a 3D object, targeted at technical artists. Shading design, the process of making the illumination of an object in a 3D scene match an artist vision, is usually a time-consuming task because of the complex interactions between materials, geometry, and lighting environment. Physically based methods tend to provide an intuitive and coherent workflow for artists, but they are of limited use in the context of non-photorealistic shading styles. On the other hand, existing stylized shading techniques are either too specialized or require considerable hand-tuning of unintuitive parameters to give a satisfactory result. Our contribution is to separate the design process of individual shading effects in three independent stages: control of its global behavior on the object, addition of procedural details, and colorization. Inspired by the formulation of existing shading models, we expose different shading behaviors to the artist through parametrizations, which have a meaningful visual interpretation. Multiple shading effects can then be composited to obtain complex dynamic appearances. The proposed workflow is fully interactive, with real-time feedback, and allows the intuitive exploration of stylized shading effects, while keeping coherence under varying viewpoints and light configurations. Furthermore, our method makes use of the deferred shading technique, making it easily integrable in existing rendering pipelines.Dans ce rapport, nous décrivons un outil de création de modèles d'illumination adapté à la stylisation de scènes 3D. Contrairement aux modèles d'illumination photoréalistes, qui suivent des contraintes physiques, les modèles d'illumination stylisés répondent à des contraintes artistiques, souvent inspirées de la représentation de la lumière en illustration. Pour cela, la conception de ces modèles stylisés est souvent complexe et coûteuse en temps. De plus, ils doivent produire un résultat cohérent sous une multitude d'angles de vue et d'éclairages. Nous proposons une méthode qui facilite la création d'effets d'illumination stylisés, en décomposant le processus en trois parties indépendantes: contrôle du comportement global de l'illumination, ajout de détails procéduraux, et colorisation.Différents comportements d'illumination sont accessibles à travers des paramétrisations, qui ont une interprétation visuelle, et qui peuvent être combinées pour obtenir des apparences plus complexes. La méthode proposée est interactive, et permet l'exploration efficace de modèles d'illumination stylisés. La méthode est implémentée avec la technique de deferred shading, ce qui la rend facilement utilisable dans des pipelines de rendu existants

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

A Natural Image Pointillism with Controlled Ellipse Dots

This paper presents an image-based artistic rendering algorithm for the automatic Pointillism style. At first, ellipse dot locations are randomly generated based on a source image; then dot orientations are precalculated with help of a direction map; a saliency map of the source image decides long and short radius of the ellipse dot. At last, the rendering runs layer-by-layer from large size dots to small size dots so as to reserve the detailed parts of the image. Although only ellipse dot shape is adopted, the final Pointillism style performs well because of variable characteristics of the dot

Gabor Noise revisité

International audienceGabor noise ingredients — points distribution, weights, kernel — can be changed. We show that minor implementation changes allow for huge 17 − 24× speed-up with same or better quality.Les ingrédients du Gabor noise — distribution de points, poids, kernel — peuvent être changés. Nous montrons que des modifications mineurs de l'implémentation permettent des gains en performance entre 17 et 24 fois tout en maintenant voire améliorant la qualité du résultat

Noise-based Enhancement for Foveated Rendering

Human visual sensitivity to spatial details declines towards the periphery. Novel image synthesis techniques, so-called foveated rendering, exploit this observation and reduce the spatial resolution of synthesized images for the periphery, avoiding the synthesis of high-spatial-frequency details that are costly to generate but not perceived by a viewer. However, contemporary techniques do not make a clear distinction between the range of spatial frequencies that must be reproduced and those that can be omitted. For a given eccentricity, there is a range of frequencies that are detectable but not resolvable. While the accurate reproduction of these frequencies is not required, an observer can detect their absence if completely omitted. We use this observation to improve the performance of existing foveated rendering techniques. We demonstrate that this specific range of frequencies can be efficiently replaced with procedural noise whose parameters are carefully tuned to image content and human perception. Consequently, these fre- quencies do not have to be synthesized during rendering, allowing more aggressive foveation, and they can be replaced by noise generated in a less expensive post-processing step, leading to improved performance of the ren- dering system. Our main contribution is a perceptually-inspired technique for deriving the parameters of the noise required for the enhancement and its calibration. The method operates on rendering output and runs at rates exceeding 200 FPS at 4K resolution, making it suitable for integration with real-time foveated rendering systems for VR and AR devices. We validate our results and compare them to the existing contrast enhancement technique in user experiments