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Images and Textures

2020-07-03T14:20:00Z


Semi-Procedural Textures Using Point Process Texture Basis Functions

Pascal Guehl, Remi Allegre, Jean-Michel Dischler, Bedrich Benes, Eric Galin

We introduce a novel semi-procedural approach that avoids drawbacks of procedural textures and leverages advantages of data-driven texture synthesis. We split synthesis in two parts: 1) structure synthesis, based on a procedural parametric model and 2) color details synthesis, being data-driven. The procedural model consists of a generic Point Process Texture Basis Function (PPTBF), which extends sparse convolution noises by defining rich convolution kernels. They consist of a window function multiplied with a statistical mixture of Gabor functions, both designed to encapsulate a large span of spatial stochastic structures, including cells, cracks, grains, scratches, spots, stains, and waves. Parameters can be prescribed automatically by supplying binary structure exemplars. As for noise-based Gaussian textures, the PPTBF is used as stand-alone function, avoiding classification tasks that occur when handling multiple procedural assets. Because the PPTBF is based on a single set of parameters it allows for continuous transitions between different visual structures and an easy control over its visual characteristics. Color is consistently synthesized from the exemplar using a novel multiscale parallel texture synthesis by numbers, constrained by the PPTBF. The generated procedural noises are parametric, infinite, continuous, and they avoid repetition. The synthesis of the data-driven part is automatic and guarantees strong visual resemblance with inputs.


High-Resolution Neural Face Swapping for Visual Effects

Jacek Naruniec, Leonhard Helminger, Christopher Schroers, Romann Weber

In this paper, we propose an algorithm for fully automatic neural face swapping in images and videos. To the best of our knowledge, this is the first method capable of rendering photo-realistic and temporally coherent results at megapixel resolution. To this end, we introduce a progressively trained multi-way {comb network} and a light- and contrast-preserving blending method. We also show that while progressive training enables generation of high-resolution images, extending the architecture and training data beyond two people allows us to achieve higher fidelity in generated expressions. When compositing the generated expression onto the target face, we show how to adapt the blending strategy to preserve contrast and low-frequency lighting. Finally, we incorporate a refinement strategy into the face landmark stabilization algorithm to achieve temporal stability, which is crucial for working with high-resolution videos. We conduct an extensive ablation study to show the influence of our design choices on the quality of the swap and compare our work with popular state-of-the-art methods.