Details of the denoising convolutional neural network architecture.

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

Examples used in film and game.

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

Training data.

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

Comparison of foam particles (number).

Comparison of foam particles (number).</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 our method in a rotating-emitter (inset image: Acceleration map).

(a) Frame 50, (b) Frame 140.</p