Abstract We present StyleBlit—an efficient example‐based style transfer algorithm that can deliver high‐quality stylized renderings in real‐time on a single‐core CPU. Our technique is especially suitable for style transfer applications that use local guidance ‐ descriptive guiding channels containing large spatial variations. Local guidance encourages transfer of content from the source exemplar to the target image in a semantically meaningful way. Typical local guidance includes, e.g., normal values, texture coordinates or a displacement field. Contrary to previous style transfer techniques, our approach does not involve any computationally expensive optimization. We demonstrate that when local guidance is used, optimization‐based techniques converge to solutions that can be well approximated by simple pixel‐level operations. Inspired by this observation, we designed an algorithm that produces results visually similar to, if not better than, the state‐of‐the‐art, and is several orders of magnitude faster. Our approach is suitable for scenarios with low computational budget such as games and mobile applications.

We introduce a new example-based approach to video stylization, with a focus on preserving the visual quality of the style, user controllability and applicability to arbitrary video. Our method gets as input one or more keyframes that the artist chooses to stylize with standard painting tools. It then automatically propagates the stylization to the rest of the sequence. To facilitate this while preserving visual quality, we developed a new type of guidance for state-of-art patch-based synthesis, that can be applied to any type of video content and does not require any additional information besides the video itself and a user-specified mask of the region to be stylized. We further show a temporal blending approach for interpolating style between keyframes that preserves texture coherence, contrast and high frequency details. We evaluate our method on various scenes from real production setting and provide a thorough comparison with prior art.

We introduce a novel approach to example-based stylization of portrait videos that preserves both the subject's identity and the visual richness of the input style exemplar. Unlike the current state-of-the-art based on neural style transfer [Selim et al. 2016], our method performs non-parametric texture synthesis that retains more of the local textural details of the artistic exemplar and does not suffer from image warping artifacts caused by aligning the style exemplar with the target face. Our method allows the creation of videos with less than full temporal coherence [Ruder et al. 2016]. By introducing a controllable amount of temporal dynamics, it more closely approximates the appearance of real hand-painted animation in which every frame was created independently. We demonstrate the practical utility of the proposed solution on a variety of style exemplars and target videos.

We present an approach to example-based stylization of 3D renderings that better preserves the rich expressiveness of hand-created artwork. Unlike previous techniques, which are mainly guided by colors and normals, our approach is based on light propagation in the scene. This novel type of guidance can distinguish among context-dependent illumination effects, for which artists typically use different stylization techniques, and delivers a look closer to realistic artwork. In addition, we demonstrate that the current state of the art in guided texture synthesis produces artifacts that can significantly decrease the fidelity of the synthesized imagery, and propose an improved algorithm that alleviates them. Finally, we demonstrate our method's effectiveness on a variety of scenes and styles, in applications like interactive shading study or autocompletion.

In this paper we present Brushables—a novel approach to example-based painting that respects user-specified shapes at the global level and preserves textural details of the source image at the local level. We formulate the synthesis as a joint optimization problem that simultaneously synthesizes the interior and the boundaries of the region, transferring relevant content from the source to meaningful locations in the target. We also provide an intuitive interface to control both local and global direction of textural details in the synthesized image. A key advantage of our approach is that it enables a “combing” metaphor in which the user can incrementally modify the target direction field to achieve the desired look. Based on this, we implement an interactive texture painting tool capable of handling more complex textures than ever before, and demonstrate its versatility on difficult inputs including vegetation, textiles, hair and painting media.

In this paper we present a novel approach to appearance transfer for fluid animations based on flow-guided texture synthesis. In contrast to common practice where pre-captured sets of fluid elements are combined in order to achieve desired motion and look, we bring the possibility of fine-tuning motion properties in advance using CG techniques, and then transferring the desired look from a selected appearance exemplar. We demonstrate that such a practical work-flow cannot be simply implemented using current state-of-the-art techniques, analyze what the main obstacles are, and propose a solution to resolve them. In addition, we extend the algorithm to allow for synthesis with rich boundary effects and video exemplars. Finally, we present numerous results that demonstrate the versatility of the proposed approach.

Abstract We present an example‐based approach to rendering hand‐colored animations which delivers visual richness comparable to real artwork while enabling control over the amount of perceived temporal noise. This is important both for artistic purposes and viewing comfort, but is tedious or even intractable to achieve manually. We analyse typical features of real hand‐colored animations and propose an algorithm that tries to mimic them using only static examples of drawing media. We apply the algorithm to various animations using different drawing media and compare the quality of synthetic results with real artwork. To verify our method perceptually, we conducted experiments confirming that our method delivers distinguishable noise levels and reduces eye strain. Finally, we demonstrate the capabilities of our method to mask imperfections such as shower‐door artifacts.

In this paper we propose a reinterpretation of the brush and the fill tools for digital image painting. The core idea is to provide an intuitive approach that allows users to paint in the visual style of arbitrary example images. Rather than a static library of colors, brushes, or fill patterns, we offer users entire images as their palette, from which they can select arbitrary contours or textures as their brush or fill tool in their own creations. Compared to previous example-based techniques related to the painting-by-numbers paradigm we propose a new strategy where users can generate salient texture boundaries by our randomized graph-traversal algorithm and apply a content-aware fill to transfer textures into the delimited regions. This workflow allows users of our system to intuitively create visually appealing images that better preserve the visual richness and fluidity of arbitrary example images. We demonstrate the potential of our approach in various applications including interactive image creation, editing and vector image stylization.