DeepFracture: A Generative Approach for Predicting Brittle Fractures

• URL: http://arxiv.org/abs/2310.13344v1
• Date: Fri, 20 Oct 2023 08:15:13 GMT
• Title: DeepFracture: A Generative Approach for Predicting Brittle Fractures
• Authors: Yuhang Huang, Takashi Kanai
• Abstract summary: This paper introduces a novel learning-based approach for seamlessly merging realistic brittle fracture animations with rigid-body simulations. Our method utilizes BEM brittle fracture simulations to create fractured patterns and collision conditions for a given shape. Our experimental results demonstrate that our approach can generate significantly more detailed brittle fractures compared to existing techniques.
• Score: 2.7669937245634757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of brittle fracture animation, generating realistic destruction animations with physics simulation techniques can be computationally expensive. Although methods using Voronoi diagrams or pre-fractured patterns work for real-time applications, they often lack realism in portraying brittle fractures. This paper introduces a novel learning-based approach for seamlessly merging realistic brittle fracture animations with rigid-body simulations. Our method utilizes BEM brittle fracture simulations to create fractured patterns and collision conditions for a given shape, which serve as training data for the learning process. To effectively integrate collision conditions and fractured shapes into a deep learning framework, we introduce the concept of latent impulse representation and geometrically-segmented signed distance function (GS-SDF). The latent impulse representation serves as input, capturing information about impact forces on the shape's surface. Simultaneously, a GS-SDF is used as the output representation of the fractured shape. To address the challenge of optimizing multiple fractured pattern targets with a single latent code, we propose an eight-dimensional latent space based on a normal distribution code within our latent impulse representation design. This adaptation effectively transforms our neural network into a generative one. Our experimental results demonstrate that our approach can generate significantly more detailed brittle fractures compared to existing techniques, all while maintaining commendable computational efficiency during run-time.

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