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FLUID CONTROL USING THE ADJOINT METHOD
Antoine McNamara, Adrien Treuille, Zoran Popović, Jos Stam
ACM Transactions on Graphics (ACM SIGGRAPH 2004)
Paper (4 MB, PDF).
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Abstract
We describe a novel method for controlling physics-based fluid
simulations through gradient-based nonlinear optimization. Using a
technique known as the adjoint method, derivatives can be
computed efficiently, even for large 3D simulations with millions of
control parameters. In addition, we introduce the first method for the
full control of free-surface liquids. We show how to compute adjoint
derivatives through each step of the simulation, including the fast
marching algorithm, and describe a new set of control parameters
specifically designed for liquids.
Results
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A ball of smoke forms the Stanford Bunny.
Video 1 (0BM, Divx AVI)
Video 2 (0BM, Divx AVI)
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A ball of smoke forms the Stanford Armadillo.
Video 1 (456k, Divx AVI)
Video 2 (669k, Divx AVI)
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A ball of clay splats on the ground, creating several shapes.
Video 1 (241k, Divx AVI)
Video 3 (264k, Divx AVI)
Video 2 (264k, Divx AVI)
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Fancy splashes are sculpted through keyframing.
Video 1 (970k, Divx AVI)
Video 2 (1.0M, Divx AVI)
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Smoke interpolates a laser range scan of a man punching.
Video 1 (78k, Divx AVI)
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A motion captured man runs, solved both as smoke and water.
Video 1 (356k, Divx AVI)
Video 2 (430k, Divx AVI)
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The full SIGGRAPH video.
Video 1 (149M, Quicktime)
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KEYFRAME CONTROL OF SMOKE SIMULATIONS
Adrien Treuille, Antoine McNamara, Zoran Popović, Jos Stam
ACM Transactions on Graphics (ACM SIGGRAPH 2003)
Paper (1 MB, PDF).
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Abstract
We describe a method for controlling smoke simulations through
user-specified keyframes. To achieve the desired behavior, a
continuous quasi-Newton optimization solves for appropriate "wind"
forces to be applied to the underlying velocity field throughout the
simulation. The cornerstone of our approach is a method to
efficiently compute exact derivatives through the steps of a fluid
simulation. We formulate an objective function corresponding to how
well a simulation matches the user's keyframes, and use the
derivatives to solve for force parameters that minimize this function.
For animations with several keyframes, we present a novel
multiple-shooting approach. By splitting large problems into smaller
overlapping subproblems, we greatly speed up the optimization process
while avoiding certain local minima.
Results
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The full SIGGRAPH video.
Video 1 (227M, Quicktime)
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