  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.
