17 research outputs found
FLSH -- Friendly Library for the Simulation of Humans
Computer models of humans are ubiquitous throughout computer animation and
computer vision. However, these models rarely represent the dynamics of human
motion, as this requires adding a complex layer that solves body motion in
response to external interactions and according to the laws of physics. FLSH is
a library that facilitates this task for researchers and developers who are not
interested in the nuisances of physics simulation, but want to easily integrate
dynamic humans in their applications. FLSH provides easy access to three
flavors of body physics, with different features and computational complexity:
skeletal dynamics, full soft-tissue dynamics, and reduced-order modeling of
soft-tissue dynamics. In all three cases, the simulation models are built on
top of the pseudo-standard SMPL parametric body model.Comment: Project website: https://gitlab.com/PabloRamonPrieto/fls
Variational Bonded Discrete Element Method with Manifold Optimization
This paper proposes a novel approach that combines variational integration
with the bonded discrete element method (BDEM) to achieve faster and more
accurate fracture simulations. The approach leverages the efficiency of
implicit integration and the accuracy of BDEM in modeling fracture phenomena.
We introduce a variational integrator and a manifold optimization approach
utilizing a nullspace operator to speed up the solving of
quaternion-constrained systems. Additionally, the paper presents an element
packing and surface reconstruction method specifically designed for bonded
discrete element methods. Results from the experiments prove that the proposed
method offers 2.8 to 12 times faster state-of-the-art methods
DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics
Synthesizing realistic human movements, dynamically responsive to the
environment, is a long-standing objective in character animation, with
applications in computer vision, sports, and healthcare, for motion prediction
and data augmentation. Recent kinematics-based generative motion models offer
impressive scalability in modeling extensive motion data, albeit without an
interface to reason about and interact with physics. While
simulator-in-the-loop learning approaches enable highly physically realistic
behaviors, the challenges in training often affect scalability and adoption. We
introduce DROP, a novel framework for modeling Dynamics Responses of humans
using generative mOtion prior and Projective dynamics. DROP can be viewed as a
highly stable, minimalist physics-based human simulator that interfaces with a
kinematics-based generative motion prior. Utilizing projective dynamics, DROP
allows flexible and simple integration of the learned motion prior as one of
the projective energies, seamlessly incorporating control provided by the
motion prior with Newtonian dynamics. Serving as a model-agnostic plug-in, DROP
enables us to fully leverage recent advances in generative motion models for
physics-based motion synthesis. We conduct extensive evaluations of our model
across different motion tasks and various physical perturbations, demonstrating
the scalability and diversity of responses.Comment: SIGGRAPH Asia 2023, Video https://youtu.be/tF5WW7qNMLI, Website:
https://stanford-tml.github.io/drop