Visualization 1.mp4

The image changes with the temperature, in other words, the phase of liquid crystal

Details of the denoising convolutional neural network architecture.

Details of the denoising convolutional neural network architecture.</p

Separable binomial filter with size <i>n</i>_filter = 3.

The acceleration value (i, j) shown in the center is the weighted sum of the neighboring values.</p

Training data.

The training datas are presented in the Supporting Information. (ZIP)</p

Examples used in film and game.

(a) Disney animation ‘Moana’ [65], (b) NVIDIA PhysX [66].</p

Denoising convolutional neural network architecture (input: x(128, 128, 3), [weight], [bias], #x(width, height, depth), final output: x(4, 4, 512)).

Denoising convolutional neural network architecture (input: x(128, 128, 3), [weight], [bias], #x(width, height, depth), final output: x(4, 4, 512)).</p

Comparison of aliasing artifacts.

The pixels are colored according to the magnitude of dij from red (high) to the blue (low). (a) Before, (b) After.</p

Examples by simulation.

Examples by simulation.</p

Foam effects with previous method and same scene as Fig 13 (inset image: Refined acceleration map with DANet [68]). (a) Frame 20, (b) Frame 130.

Foam effects with previous method and same scene as Fig 13 (inset image: Refined acceleration map with DANet [68]). (a) Frame 20, (b) Frame 130.</p

Foam effects with our method in two rotating boxes (inset image: Simulation view).

(a) Frame 20, (b) Frame 130.</p