Applications of computer-graphics animation for motion-perception research

The advantages and limitations of using computer animated stimuli in studying motion perception are presented and discussed. Most current programs of motion perception research could not be pursued without the use of computer graphics animation. Computer generated displays afford latitudes of freedom and control that are almost impossible to attain through conventional methods. There are, however, limitations to this presentational medium. At present, computer generated displays present simplified approximations of the dynamics in natural events. Very little is known about how the differences between natural events and computer simulations influence perceptual processing. In practice, the differences are assumed to be irrelevant to the questions under study, and that findings with computer generated stimuli will generalize to natural events

Time-varying volume visualization

Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin

Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

Efficient Shadows from Sampled Environment Maps

This paper addresses the problem of efficiently calculating shadows from environment maps. Since accurate rendering of shadows from environment maps requires hundreds of lights, the expensive computation is determining visibility from each pixel to each light direction, such as by ray-tracing. We show that coherence in both spatial and angular domains can be used to reduce the number of shadow rays that need to be traced. Specifically, we use a coarse-to-fine evaluation of the image, predicting visibility by reusing visibility calculations from four nearby pixels that have already been evaluated. This simple method allows us to explicitly mark regions of uncertainty in the prediction. By only tracing rays in these and neighboring directions, we are able to reduce the number of shadow rays traced by up to a factor of 20 while maintaining error rates below 0.01%. For many scenes, our algorithm can add shadowing from hundreds of lights at twice the cost of rendering without shadows

Path tracing multivue adaptatif

International audienceRendering photo-realistic image sequences using path tracing and Monte Carlo integration often requires sampling a large number of paths to get converged results. In the context of rendering multiple views or animated sequences, such sampling can be highly redundant. Several methods have been developed to share sampled paths between spatially or temporarily similar views. However, such sharing is challenging since it can lead to bias in the final images. Our contribution is a Monte Carlo sampling technique which generates paths, taking into account several cameras. First, we sample the scene from all the cameras to generate hit points. Then, an importance sampling technique generates bouncing directions which are shared by a subset of cameras. This set of hit points and bouncing directions is then used within a regular path tracing solution. For animated scenes, paths remain valid for a fixed time only, but sharing can still occur between cameras as long as their exposure time intervals overlap. We show that our technique generates less noise than regular path tracing and does not introduce noticeable bias.Le rendu de séquences d'images photoréalistes en lancer de rayons nécessite souvent l'échantillonnage d'un grand nombre de chemins pour obtenir des résultats convergés. Dans le contexte du rendu multi-vue ou de séquences animées, cet échantillonnage peut être redondant. Plusieurs méthodes ont été développées pour réutiliser des chemins échantillonnés entre des vues proches spatialement ou temporellement. Cependant, un telle réutilisation est complexe car elle peut mener à du biais dans les images. Notre contribution est une technique d'échantillonnage qui génère des chemins en prenant en compte plusieurs caméras. Tout d'abord, nous échantillonnons la scène depuis toutes les caméras pour générer des points visibles. Ensuite, nous générons des directions par importance qui sont partagées par un sous-ensemble de caméras. Cet ensemble de points et de directions est ensuite utilisé dans une solution de lancer de rayons classique. Pour les scènes animées, les chemins ne sont réutilisables qu'à temps fixe, mais un partage peut toujours avoir lieu entre les caméras si leurs intervalles d'exposition se recouvrent. Notre technique génère moins de bruit que le path tracing classique à temps de calcul équivalent et n’introduit pas de biais perceptible