235 research outputs found
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
Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)
Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we
introduce a novel crowd simulation method that runs at interactive rates for
hundreds of thousands of agents. Our method enables the detailed modeling of
per-agent behavior in a Lagrangian formulation. We model short-range and
long-range collision avoidance to simulate both sparse and dense crowds. On the
particles representing agents, we formulate a set of positional constraints
that can be readily integrated into a standard PBD solver. We augment the
tentative particle motions with planning velocities to determine the preferred
velocities of agents, and project the positions onto the constraint manifold to
eliminate colliding configurations. The local short-range interaction is
represented with collision and frictional contact between agents, as in the
discrete simulation of granular materials. We incorporate a cohesion model for
modeling collective behaviors and propose a new constraint for dealing with
potential future collisions. Our new method is suitable for use in interactive
games.Comment: 9 page
Fast GPU-Based Two-Way Continuous Collision Handling
Step-and-project is a popular way to simulate non-penetrated deformable
bodies in physically-based animation. First integrating the system in time
regardless of contacts and post resolving potential intersections practically
strike a good balance between plausibility and efficiency. However, existing
methods could be defective and unsafe when the time step is large, taking risks
of failures or demands of repetitive collision testing and resolving that
severely degrade performance. In this paper, we propose a novel two-way method
for fast and reliable continuous collision handling. Our method launches the
optimization at both ends of the intermediate time-integrated state and the
previous intersection-free state, progressively generating a piecewise-linear
path and finally reaching a feasible solution for the next time step.
Technically, our method interleaves between a forward step and a backward step
at a low cost, until the result is conditionally converged. Due to a set of
unified volume-based contact constraints, our method can flexibly and reliably
handle a variety of codimensional deformable bodies, including volumetric
bodies, cloth, hair and sand. The experiments show that our method is safe,
robust, physically faithful and numerically efficient, especially suitable for
large deformations or large time steps
DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact
Cloth simulation has wide applications in computer animation, garment design,
and robot-assisted dressing. This work presents a differentiable cloth
simulator whose additional gradient information facilitates cloth-related
applications. Our differentiable simulator extends a state-of-the-art cloth
simulator based on Projective Dynamics (PD) and with dry frictional contact. We
draw inspiration from previous work to propose a fast and novel method for
deriving gradients in PD-based cloth simulation with dry frictional contact.
Furthermore, we conduct a comprehensive analysis and evaluation of the
usefulness of gradients in contact-rich cloth simulation. Finally, we
demonstrate the efficacy of our simulator in a number of downstream
applications, including system identification, trajectory optimization for
assisted dressing, closed-loop control, inverse design, and real-to-sim
transfer. We observe a substantial speedup obtained from using our gradient
information in solving most of these applications
Jade: A Differentiable Physics Engine for Articulated Rigid Bodies with Intersection-Free Frictional Contact
We present Jade, a differentiable physics engine for articulated rigid
bodies. Jade models contacts as the Linear Complementarity Problem (LCP).
Compared to existing differentiable simulations, Jade offers features including
intersection-free collision simulation and stable LCP solutions for multiple
frictional contacts. We use continuous collision detection to detect the time
of impact and adopt the backtracking strategy to prevent intersection between
bodies with complex geometry shapes. We derive the gradient calculation to
ensure the whole simulation process is differentiable under the backtracking
mechanism. We modify the popular Dantzig algorithm to get valid solutions under
multiple frictional contacts. We conduct extensive experiments to demonstrate
the effectiveness of our differentiable physics simulation over a variety of
contact-rich tasks
TacIPC: Intersection- and Inversion-free FEM-based Elastomer Simulation For Optical Tactile Sensors
Tactile perception stands as a critical sensory modality for human
interaction with the environment. Among various tactile sensor techniques,
optical sensor-based approaches have gained traction, notably for producing
high-resolution tactile images. This work explores gel elastomer deformation
simulation through a physics-based approach. While previous works in this
direction usually adopt the explicit material point method (MPM), which has
certain limitations in force simulation and rendering, we adopt the finite
element method (FEM) and address the challenges in penetration and mesh
distortion with incremental potential contact (IPC) method. As a result, we
present a simulator named TacIPC, which can ensure numerically stable
simulations while accommodating direct rendering and friction modeling. To
evaluate TacIPC, we conduct three tasks: pseudo-image quality assessment,
deformed geometry estimation, and marker displacement prediction. These tasks
show its superior efficacy in reducing the sim-to-real gap. Our method can also
seamlessly integrate with existing simulators. More experiments and videos can
be found in the supplementary materials and on the website:
https://sites.google.com/view/tac-ipc
Towards Real-Time Simulation Of Hyperelastic Materials
We propose a new method for physics-based simulation supporting many different types of hyperelastic materials from mass-spring systems to three-dimensional finite element models, pushing the performance of the simulation towards real-time. Fast simulation methods such as Position Based Dynamics exist, but support only limited selection of materials; even classical materials such as corotated linear elasticity and Neo-Hookean elasticity are not supported. Simulation of these types of materials currently relies on Newton\u27s method, which is slow, even with only one iteration per timestep. In this work, we start from simple material models such as mass-spring systems or as-rigid-as-possible materials. We express the widely used implicit Euler time integration as an energy minimization problem and introduce auxiliary projection variables as extra unknowns. After our reformulation, the minimization problem becomes linear in the node positions, while all the non-linear terms are isolated in individual elements. We then extend this idea to efficiently simulate a more general spatial discretization using finite element method. We show that our reformulation can be interpreted as a quasi-Newton method. This insight enables very efficient simulation of a large class of hyperelastic materials. The quasi-Newton interpretation also allows us to leverage ideas from numerical optimization. In particular, we show that our solver can be further accelerated using L-BFGS updates (Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm). Our final method is typically more than ten times faster than one iteration of Newton\u27s method without compromising quality. In fact, our result is often more accurate than the result obtained with one iteration of Newton\u27s method. Our method is also easier to implement, implying reduced software development costs
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