16,238 research outputs found
Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control
Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control
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
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
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
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than re-simulating the data with the underlying CPU
solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019),
additional materials: http://www.byungsoo.me/project/deep-fluids
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