Neural State Machine for Character-Scene Interactions / Displacement-Correlated XFEM for Simulating Brittle Fracture

Speaker:        Professor Taku Komura
                The Institute of Perception, Action and Behaviour
                School of Informatics
                University of Edinburgh

Title:          "Neural State Machine for Character-Scene Interactions /
                 Displacement-Correlated XFEM for Simulating Brittle 
                 Fracture"

Date:            Thursday, 20 February, 2020

Time:            2:30pm - 3:30pm

Zoom Meeting:    https://hkust.zoom.us/j/993572323
Meeting ID:      993-572-323


Abstract:

In this talk I will talk about the following two works, that are recently
done in our group.

First, I will talk about the Neural State Machine, a novel data-driven
framework to guide characters to achieve goal-driven actions with precise
scene interactions. Even a seemingly simple task such as sitting on a
chair is notoriously hard to model with supervised learning. This
difficulty is because such a task involves complex planning with periodic
and non-periodic motions reacting to the scene geometry to precisely
position and orient the character. Our proposed deep auto-regressive
framework enables modeling of multi-modal scene interaction behaviors
purely from data. Given high-level instructions such as the goal location
and the action to be launched there, our system computes a series of
movements and transitions to reach the goal in the desired state. To allow
characters to adapt to a wide range of geometry such as different shapes
of furniture and obstacles, we incorporate an efficient data augmentation
scheme to randomly switch the 3D geometry while maintaining the context of
the original motion. To increase the precision to reach the goal during
runtime, we introduce a control scheme that combines egocentric inference
and goal-centric inference. We demonstrate the versatility of our model
with various scene interaction tasks such as sitting on a chair, avoiding
obstacles, opening and entering through a door, and picking and carrying
objects generated in real-time just from a single model.

Second, I will talk about a remeshing-free brittle fracture simulation
method under the assumption of quasi-static linear elastic fracture
mechanics. To achieve this, we devise two algorithms. First, we develop an
approximate volumetric simulation, based on the extended Finite Element
Method (XFEM) to initialize and propagate Lagrangian crack-fronts. We
model the geometry of fracture explicitly as a surface mesh, which allows
us to generate high-resolution crack surfaces that are decoupled from the
resolution of the deformation mesh. Our second contribution is a mesh
cutting algorithm, which produces fragments of the input mesh using the
fracture surface. We do this by directly operating on the half-edge data
structures of two surface meshes, which enables us to cut general surface
meshes including those of concave polyhedra and meshes with abutting
concave polygons. Since we avoid triangulation for cutting, the
connectivity of the resulting fragments is identical to the (uncut) input
mesh except at edges introduced by the cut. We evaluate our simulation and
cutting algorithms and show that they outperform state-of-the-art
approaches both qualitatively and quantitatively.


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Biography:

Taku Komura is a Professor at the Institute of Perception, Action and
Behaviour, School of Informatics, University of Edinburgh. As the leader
of the Computer Graphics and Visualization Unit his research has focused
on data-driven character animation, physically-based character animation,
crowd simulation, cloth animation, anatomy-based modelling, and robotics.
Recently, his main research interests have been the application of machine
learning techniques for animation synthesis. He received the Royal Society
Industry Fellowship (2014) and the Google AR/VR Research Award (2017).