Browsing by Author "Riso, Marzia"

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    pEt: Direct Manipulation of Differentiable Vector Patterns
    (The Eurographics Association, 2023) Riso, Marzia; Pellacini, Fabio; Ritschel, Tobias; Weidlich, Andrea
    Procedural assets are used in computer graphics applications since variations can be obtained by changing the parameters of the procedural programs. As the number of parameters increases, editing becomes cumbersome as users have to manually navigate a large space of choices. Many methods in the literature have been proposed to estimate parameters from example images, which works well for initial starting points. For precise edits, inverse manipulation approaches let users manipulate the output asset interactively, while the system determines the procedural parameters. In this work, we focus on editing procedural vector patterns, which are collections of vector primitives generated by procedural programs. Recent work has shown how to estimate procedural parameters from example images and sketches, that we complement here by proposing a method for direct manipulation. In our work, users select and interactively transform a set of shape points, while also constraining other selected points. Our method then optimizes for the best pattern parameters using gradient-based optimization of the differentiable procedural functions. We support edits on large variety of patterns with different shapes, symmetries, continuous and discrete parameters, and with or without occlusions.
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    Structured Pattern Expansion with Diffusion Models
    (The Eurographics Association, 2025) Riso, Marzia; Vecchio, Giuseppe; Pellacini, Fabio; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
    Recent advances in diffusion models have significantly improved the synthesis of materials, textures, and 3D shapes. By conditioning these models on text or images, users can guide the generation, reducing the time required to create digital assets. In this paper, we address the synthesis of structured, stationary patterns, where diffusion models are generally less reliable and, more importantly, less controllable. Our approach leverages the generative capabilities of diffusion models specifically adapted to the pattern domain. It enables users to exercise direct control over the synthesis by expanding a partially hand-drawn pattern into a larger design while preserving the structure and details of the input. To enhance pattern quality, we fine-tune an image-pretrained diffusion model on structured patterns using Low-Rank Adaptation (LoRA), apply a noise rolling technique to ensure tileability, and utilize a patch-based approach to facilitate the generation of large-scale assets. We demonstrate the effectiveness of our method through a comprehensive set of experiments, showing that it outperforms existing models in generating diverse, consistent patterns that respond directly to user input. Code will be released at publication time at: https://github.com/marzia-riso/structured_pattern_expansion.