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Subject: Image processing ×

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    Artist-Inator: Text-based, Gloss-aware Non-photorealistic Stylization
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Subias, Jose Daniel; Daniel-Soriano, Saúl; Gutierrez, Diego; Serrano, Ana; Wang, Beibei; Wilkie, Alexander
    Large diffusion models have made a remarkable leap synthesizing high-quality artistic images from text descriptions. However, these powerful pre-trained models still lack control to guide key material appearance properties, such as gloss. In this work, we present a threefold contribution: (1) we analyze how gloss is perceived across different artistic styles (i.e., oil painting, watercolor, ink pen, charcoal, and soft crayon); (2) we leverage our findings to create a dataset with 1,336,272 stylized images of many different geometries in all five styles, including automatically-computed text descriptions of their appearance (e.g., ''A glossy bunny hand painted with an orange soft crayon''); and (3) we train ControlNet to condition Stable Diffusion XL synthesizing novel painterly depictions of new objects, using simple inputs such as edge maps, hand-drawn sketches, or clip arts. Compared to previous approaches, our framework yields more accurate results despite the simplified input, as we show both quantitative and qualitatively.
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    StructuReiser: A Structure-preserving Video Stylization Method
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Spetlik, Radim; Futschik, David; Sýkora, Daniel; Wang, Beibei; Wilkie, Alexander
    We introduce StructuReiser, a novel video-to-video translation method that transforms input videos into stylized sequences using a set of user-provided keyframes. Unlike most existing methods, StructuReiser strictly adheres to the structural elements of the target video, preserving the original identity while seamlessly applying the desired stylistic transformations. This provides a level of control and consistency that is challenging to achieve with text-driven or keyframe-based approaches, including large video models. Furthermore, StructuReiser supports real-time inference on standard graphics hardware as well as custom keyframe editing, enabling interactive applications and expanding possibilities for creative expression and video manipulation.
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    MatSwap: Light-aware Material Transfers in Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Lopes, Ivan; Deschaintre, Valentin; Hold-Geoffroy, Yannick; Charette, Raoul de; Wang, Beibei; Wilkie, Alexander
    We present MatSwap, a method to transfer materials to designated surfaces in an image realistically. Such a task is non-trivial due to the large entanglement of material appearance, geometry, and lighting in a photograph. In the literature, material editing methods typically rely on either cumbersome text engineering or extensive manual annotations requiring artist knowledge and 3D scene properties that are impractical to obtain. In contrast, we propose to directly learn the relationship between the input material-as observed on a flat surface-and its appearance within the scene, without the need for explicit UV mapping. To achieve this, we rely on a custom light- and geometry-aware diffusion model. We fine-tune a large-scale pre-trained text-toimage model for material transfer using our synthetic dataset, preserving its strong priors to ensure effective generalization to real images. As a result, our method seamlessly integrates a desired material into the target location in the photograph while retaining the identity of the scene. MatSwap is evaluated on synthetic and real images showing that it compares favorably to recent works. Our code and data are made publicly available on https://github.com/astra-vision/MatSwap
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    Continuous-Line Image Stylization Based on Hilbert Curve
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Tong, Zhifang; Zuo, Bolei; Yang, Xiaoxia; Liu, Shengjun; Liu, Xinru; Wang, Beibei; Wilkie, Alexander
    Horizontal and vertical lines hold significant aesthetic and psychological importance, providing a sense of order, stability, and security. This paper presents an image stylization method that quickly generates non-self-intersecting and regular continuous lines based on the Hilbert curve, a well-known space-filling curve consisting of only horizontal and vertical segments. We first calculate the grayscale threshold based on gray quantization for the original image and recursively subdivide the cells according to the density in each cell. To avoid generating new feature curves due to limited gray quantization, a recursive subdivision with probability is designed to smooth the density. Then, we utilize the rule of Hilbert curve to generate continuous lines connecting all the cells. Between different degrees of Hilbert curves, bridge curves composed of horizontal and vertical lines are constructed, which are also intersection-free, instead of a straight line linking them directly. There are two parameters provided for feasibly adjusting variate effects. The image stylization framework could be generalized to other space-filling curves like the Peano curve. Compared to existing methods, our approach can generate pleasing results quickly and is fully automated. Many results show our method is robust and effective.