4 research outputs found
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Efficient simulation of the Navier-Stokes equations for fluid flow is a long
standing problem in applied mathematics, for which state-of-the-art methods
require large compute resources. In this work, we propose a data-driven
approach that leverages the approximation power of deep-learning with the
precision of standard solvers to obtain fast and highly realistic simulations.
Our method solves the incompressible Euler equations using the standard
operator splitting method, in which a large sparse linear system with many free
parameters must be solved. We use a Convolutional Network with a highly
tailored architecture, trained using a novel unsupervised learning framework to
solve the linear system. We present real-time 2D and 3D simulations that
outperform recently proposed data-driven methods; the obtained results are
realistic and show good generalization properties.Comment: Significant revisio
Zero-Shot Multi-Modal Artist-Controlled Retrieval and Exploration of 3D Object Sets
When creating 3D content, highly specialized skills are generally needed to
design and generate models of objects and other assets by hand. We address this
problem through high-quality 3D asset retrieval from multi-modal inputs,
including 2D sketches, images and text. We use CLIP as it provides a bridge to
higher-level latent features. We use these features to perform a multi-modality
fusion to address the lack of artistic control that affects common data-driven
approaches. Our approach allows for multi-modal conditional feature-driven
retrieval through a 3D asset database, by utilizing a combination of input
latent embeddings. We explore the effects of different combinations of feature
embeddings across different input types and weighting methods