Deep Learning Advances on Different 3D Data Representations: A Survey

Much of the attention surrounding deep learning and its impact on visual effects has focused on image-based techniques, in particular the compelling examples produced by denoising, segmentation, and generative adversarial models. Research continues, however, into application of machine learning to 3D data – meshes, point clouds, voxel grids, and so on – and how to…Read More

tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow

Generative Adversarial Networks (GANs) have been on a tear these last few months, providing rapid advances particularly in the field of realistic image generation. This application to fluids is interesting not only because it extends the architecture into 3D but also for what it allows. It’s long been desired to use low resolution proxy layout…Read More

Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

Some great results from Disney, to be presented at this year’s SIGGRAPH Asia: “We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds—e.g. the characteristic silverlining and the…Read More