Keyframe control of smoke simulations

By Adrien Treuille, A. McNamara, Zoran Popovic, J. Stam. ACM Transactions on Graphics, 2003

319 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/d2cddbf71c1e41f3422d7fa8879b833cb84721df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

1. Frank Losasso
2. Ronald Fedkiw
3. Andrew Selle
4. Frédéric Gibou
5. Ken Museth
6. Nils Thürey
7. Eran Guendelman
8. Zherong Pan

In Computer Vision and Pattern Recognition

1. Frank Losasso
2. Ronald Fedkiw
3. Andrew Selle
4. Hujun Bao
5. Nils Thürey
6. Frédéric Gibou
7. Zherong Pan
8. Eran Guendelman

In Ocean Engineering

1. Vladlen Koltun
2. Hujun Bao
3. Ronald Fedkiw
4. Dinesh Manocha
5. Frank Losasso

Citations in the corpus

• Interactive localized liquid motion editing (2013, methodology): “As a viable alternative to explicitly tuning parameters and boundary conditions, some previous methods [Treuille et al. 2003; McNamara et al. 2004; Wojtan et al. 2006] proposed to specify a priori a set of global fluid shape keyframes and rely on an offline numerical optimization scheme to find the ”
• Coupling water and smoke to thin deformable and rigid shells (2005, methodology): “2003] combined interpolation with two dimensional simulations to create three dimensional nuclear explosions, [Treuille et al. 2003; Fattal and Lischinski 2004] proposed methods for control, and [Stam 2003] solved these equations on surfaces creating beautiful imagery.”
• Guiding of smoke animations through variational coupling of simulations at different resolutions (2009, background): “Later Treuille et al. [ TMPS03 ] proposed a gradient descent based optimization framework for keyframe control of smoke simulations.”
• Fluid directed rigid body control using deep reinforcement learning (2018, methodology): “Numerous methods, such as [Fattal and Lischinski 2004; McNamara et al. 2004; Pan et al. 2013; Pan and Manocha 2017; Shi and Yu 2005; Thuerey 2016; Treuille et al. 2003], have been proposed to this end.”
• Learning to Control PDEs with Differentiable Physics (2020, methodology): “Depending on the physical system, this method may have to be adjusted for specific problems (Treuille et al., 2003).”
• Simulating water and smoke with an octree data structure (2004, methodology): “[Treuille et al. 2003] proposed a method for control and used it to make letters out of smoke, and [Stam 2003] solved these equations on surfaces creating beautiful imagery.”