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Found 14 papers, 5 papers with code
Learning a Generalized Physical Face Model From Data
Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-collisions, respond to external forces and perform realistic anatomy edits.
An Implicit Physical Face Model Driven by Expression and Style
At the core, we present a framework for learning implicit physics-based actuations for multiple subjects simultaneously, trained on a few arbitrary performance capture sequences from a small set of identities.
Implicit Neural Representation for Physics-driven Actuated Soft Bodies
Active soft bodies can affect their shape through an internal actuation mechanism that induces a deformation.
Efficient Incremental Potential Contact for Actuated Face Simulation
We present a quasi-static finite element simulator for human face animation.
Spatially Adaptive Cloth Regression with Implicit Neural Representations
The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics.
Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision
Despite the complexity of this task, we show that it is possible to train the corresponding networks without requiring any 3D ground truth for training.
Global Transport for Fluid Reconstruction with Learned Self-Supervision
We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation.
Lagrangian Neural Style Transfer for Fluids
Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production.
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow
By selectively overwriting parts of the predicted latent space points, our proposed method is capable to robustly predict long-term sequences of complex physics problems.
Neural Smoke Stylization with Color Transfer
Artistically controlling fluid simulations requires a large amount of manual work by an artist.
Graphics
Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks
Convolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters.
Physics-Aware Flow Data Completion Using Neural Inpainting
In this paper we propose a physics-aware neural network for inpainting fluid flow data.
Transport-Based Neural Style Transfer for Smoke Simulations
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.
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters.
