DeepFracture: A Generative Approach for Predicting Brittle Fractures
Yuhang Huang Takashi Kanai
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.