Rendering 2023 - Symposium Track

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https://diglib.eg.org/handle/10.2312/2633357

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Browsing Rendering 2023 - Symposium Track by Subject "Computer graphics"

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    Learning Projective Shadow Textures for Neural Rendering of Human Cast Shadows from Silhouettes
    (The Eurographics Association, 2023) Einabadi, Farshad; Guillemaut, Jean-Yves; Hilton, Adrian; Ritschel, Tobias; Weidlich, Andrea
    This contribution introduces a two-step, novel neural rendering framework to learn the transformation from a 2D human silhouette mask to the corresponding cast shadows on background scene geometries. In the first step, the proposed neural renderer learns a binary shadow texture (canonical shadow) from the 2D foreground subject, for each point light source, independent of the background scene geometry. Next, the generated binary shadows are texture-mapped to transparent virtual shadow map planes which are seamlessly used in a traditional rendering pipeline to project hard or soft shadows for arbitrary scenes and light sources of different sizes. The neural renderer is trained with shadow images rendered from a fast, scalable, synthetic data generation framework. We introduce the 3D Virtual Human Shadow (3DVHshadow) dataset as a public benchmark for training and evaluation of human shadow generation. Evaluation on the 3DVHshadow test set and real 2D silhouette images of people demonstrates the proposed framework achieves comparable performance to traditional geometry-based renderers without any requirement for knowledge or computationally intensive, explicit estimation of the 3D human shape. We also show the benefit of learning intermediate canonical shadow textures, compared to learning to generate shadows directly in camera image space. Further experiments are provided to evaluate the effect of having multiple light sources in the scene, model performance with regard to the relative camera-light 2D angular distance, potential aliasing artefacts related to output image resolution, and effect of light sources' dimensions on shadow softness.
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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.