Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids
arXiv:2106.05143 · doi:10.1145/3480147
Abstract
We present a novel up-resing technique for generating high-resolution liquids based on scene flow estimation using deep neural networks. Our approach infers and synthesizes small- and large-scale details solely from a low-resolution particle-based liquid simulation. The proposed network leverages neighborhood contributions to encode inherent liquid properties throughout convolutions. We also propose a particle-based approach to interpolate between liquids generated from varying simulation discretizations using a state-of-the-art bidirectional optical flow solver method for fluids in addition to a novel key-event topological alignment constraint. In conjunction with the neighborhood contributions, our loss formulation allows the inference model throughout epochs to reward important differences in regard to significant gaps in simulation discretizations. Even when applied in an untested simulation setup, our approach is able to generate plausible high-resolution details. Using this interpolation approach and the predicted displacements, our approach combines the input liquid properties with the predicted motion to infer semi-Lagrangian advection. We furthermore showcase how the proposed interpolation approach can facilitate generating large simulation datasets with a subset of initial condition parameters.
arXiv comment14 pages, 18 figures, and 3 tables
References in corpus (7)
- Deep Fluids: A Generative Network for Parameterized Fluid Simulations
- Skeleton-Aware Networks for Deep Motion Retargeting
- 3D Sketching using Multi-View Deep Volumetric Prediction
- tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
- Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors
- A Multi-Pass GAN for Fluid Flow Super-Resolution
- Interpolations of Smoke and Liquid Simulations