TransportBased Neural Style Transfer for Smoke Simulations Artistically controlling fluids has always been a challenging task. 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. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulence structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first transportbased Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general contentaware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transportbased approach allows direct control over the divergence of the stylization velocity field by optimizing divergence and curlfree potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.

Coherent Rendering of Virtual Smile Previews with Fast Neural Style Transfer Coherent rendering in augmented reality deals with synthesizing virtual content that seamlessly blends in with the real content. Unfortunately, capturing or modeling every real aspect in the virtual rendering process is often unfeasible or too expensive. We present a postprocessing method that improves the look of rendered overlays in a dental virtual tryon application. We combine the original frame and the default rendered frame in an autoencoder neural network in order to obtain a more natural output, inspired by artistic style transfer research. Specifically, we apply the original frame as style on the rendered frame as content, repeating the process with each new pair of frames. Our method requires only a single forward pass, our shallow architecture ensures fast execution, and our internal feedback loop inherently enforces temporal consistency.