SCA 2024 - Posters

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https://diglib.eg.org/handle/10.2312/3607034

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Browsing SCA 2024 - Posters by Subject "Computing methodologies → Animation"

Now showing 1 - 2 of 2
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    Brittle Fracture Animation with VQ-VAE-Based Generative Method
    (The Eurographics Association, 2024) Huang, Yuhang; Kanai, Takashi; Zordan, Victor
    We propose a novel learning-based approach for predicting fractured shapes based on collision dynamics at run-time and seamlessly integrating realistic brittle fracture animations with rigid-body simulations. Our method utilizes BEM brittle fracture simulations to create training data. We introduce generative geometric segmentation, distinct from instance and semantic segmentation, to represent 3D fracture shapes. We adopt the concept of a neural discrete representation learning framework to optimize multiple discrete fractured patterns with a continuous latent code. Additionally, we propose a novel SDF-based cagecutting method to create fragments by cutting the original shape with the predicted fracture pattern. Our experimental results demonstrate that our approach can generate significantly more detailed brittle fractures compared to existing techniques, while reducing computational time typically when compared to traditional simulation methods at comparable resolutions.
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    A Differentiable Material Point Method Framework for Shape Morphing
    (The Eurographics Association, 2024) Xu, Michael; Song, Chang Yong; Levin, David; Hyde, David; Zordan, Victor
    We present a novel, physically-based morphing technique for elastic shapes, leveraging the differentiable material point method (MPM) with space-time control through per-particle deformation gradients to accommodate complex topology changes. This approach, grounded in MPM's natural handling of dynamic topologies, is enhanced by a chained iterative optimization technique, allowing for the creation of both succinct and extended morphing sequences that maintain coherence over time. Demonstrated across various challenging scenarios, our method is able to produce detailed elastic deformation and topology transitions, all grounded within our physics-based simulation framework.