Weatherscapes

Snow and ice animation methods are becoming increasingly popular in the field of computer graphics (CG). The applications of snow and ice in CG are varied, ranging from generating realistic background landscapes to avalanches and physical interaction with objects in movies, games, etc. Over the past two decades, several methods have been proposed to capture the time‐evolving physical appearance or simulation of snow and ice using different models at different scales. This state‐of‐the‐art report aims to identify existing animation methods in the field, provide an up‐to‐date summary of the research in CG, and identify gaps for promising future work. Furthermore, we also attempt to identify the primarily related work done on snow and ice in some other disciplines, such as civil or mechanical engineering, and draw a parallel with the similarities and differences in CG.

“…(a) Physics‐based winter storm captured in [HHP*21], (b) snowfall modeled through artist‐generated textures in [WW04]. …”

Section: Snow In Cgmentioning

confidence: 99%

“…[DZC*13] use a particle system to simulate the rain and snow effect in real‐time, together with large textures that help reduce the particle number. Herrera et al [HHP*21] propose a unified physics‐based framework to simulate weather at interactive rates (Fig. 12a).…”

Section: Snow In Cgmentioning

confidence: 99%

Snow and Ice Animation Methods in Computer Graphics

Goswami

2024

“…While significant effort within computer graphics has been devoted to the simulation of clouds [WCGG18, HMP*20, VGL*20, HHP*21], smoke [FSJ01, WXCT19] and lava flow [SAC*99, NKL*07, ZKL*17], there has to date been relatively little focus on volcanic plumes. While plumes share some characteristics with related phenomena, such as the convective velocity of cumulonimbus clouds, they are unique in regard to the extremes of temperature and vertical scale.…”

Section: Related Workmentioning

confidence: 99%

Volcanic Skies: coupling explosive eruptions with atmospheric simulation to create consistent skyscapes

Pretorius,

Gain,

Lastic

et al. 2024

Explosive volcanic eruptions rank among the most terrifying natural phenomena, and are thus frequently depicted in films, games, and other media, usually with a bespoke once‐off solution. In this paper, we introduce the first general‐purpose model for bi‐directional interaction between the atmosphere and a volcano plume. In line with recent interactive volcano models, we approximate the plume dynamics with Lagrangian disks and spheres and the atmosphere with sparse layers of 2D Eulerian grids, enabling us to focus on the transfer of physical quantities such as temperature, ash, moisture, and wind velocity between these sub‐models. We subsequently generate volumetric animations by noise‐based procedural upsampling keyed to aspects of advection, convection, moisture, and ash content to generate a fully‐realized volcanic skyscape. Our model captures most of the visually salient features emerging from volcano‐sky interaction, such as windswept plumes, enmeshed cap, bell and skirt clouds, shockwave effects, ash rain, and sheathes of lightning visible in the dark.

“…However, path tracing of atmospheres is a lengthy process, and moreover, the models do not directly support the rendering of clouds and overcast skies. For that, one would render fully volumetric clouds from a simulation [HMP*20, HHP*21] with expensive volumetric path tracing, or use machine learning [KMM*17] for improved efficiency.…”

Section: Related Workmentioning

confidence: 99%

SkyGAN: Realistic Cloud Imagery for Image‐based Lighting

Mirbauer,

Rittig,

Iser

et al. 2023

Achieving photorealism when rendering virtual scenes in movies or architecture visualizations often depends on providing a realistic illumination and background. Typically, spherical environment maps serve both as a natural light source from the Sun and the sky, and as a background with clouds and a horizon. In practice, the input is either a static high‐resolution HDR photograph manually captured on location in real conditions, or an analytical clear sky model that is dynamic, but cannot model clouds. Our approach bridges these two limited paradigms: a user can control the sun position and cloud coverage ratio, and generate a realistically looking environment map for these conditions. It is a hybrid data‐driven analytical model based on a modified state‐of‐the‐art GAN architecture, which is trained on matching pairs of physically‐accurate clear sky radiance and HDR fisheye photographs of clouds. We demonstrate our results on renders of outdoor scenes under varying time, date and cloud covers. Our source code and a dataset of 39 000 HDR sky images are publicly available at https://github.com/CGGMFF/SkyGAN.