20 research outputs found
Predicting Static Stability with Data-Driven Physics in Air Cargo Palletizing
Proposing air cargo palletizing solutions requires an assessment by a physics engine of whether a solution is physically stable, which can take up a disproportionate amount of computation and, thus, produce a bottleneck in the optimization pipeline. This problem can be tackled by replacing the physics engine with a data-driven model that learns to map proposed packing pattern solutions directly to its stability value. We develop a prototype of such a datadriven model and find that this approach yields practicable results and does so multiple orders of magnitudes faster than a commonly used physics engine
TextDeformer: Geometry Manipulation using Text Guidance
We present a technique for automatically producing a deformation of an input
triangle mesh, guided solely by a text prompt. Our framework is capable of
deformations that produce both large, low-frequency shape changes, and small
high-frequency details. Our framework relies on differentiable rendering to
connect geometry to powerful pre-trained image encoders, such as CLIP and DINO.
Notably, updating mesh geometry by taking gradient steps through differentiable
rendering is notoriously challenging, commonly resulting in deformed meshes
with significant artifacts. These difficulties are amplified by noisy and
inconsistent gradients from CLIP. To overcome this limitation, we opt to
represent our mesh deformation through Jacobians, which updates deformations in
a global, smooth manner (rather than locally-sub-optimal steps). Our key
observation is that Jacobians are a representation that favors smoother, large
deformations, leading to a global relation between vertices and pixels, and
avoiding localized noisy gradients. Additionally, to ensure the resulting shape
is coherent from all 3D viewpoints, we encourage the deep features computed on
the 2D encoding of the rendering to be consistent for a given vertex from all
viewpoints. We demonstrate that our method is capable of smoothly-deforming a
wide variety of source mesh and target text prompts, achieving both large
modifications to, e.g., body proportions of animals, as well as adding fine
semantic details, such as shoe laces on an army boot and fine details of a
face
LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields
Linear reduced-order modeling (ROM) simplifies complex simulations by
approximating the behavior of a system using a simplified kinematic
representation. Typically, ROM is trained on input simulations created with a
specific spatial discretization, and then serves to accelerate simulations with
the same discretization. This discretization-dependence is restrictive.
Becoming independent of a specific discretization would provide flexibility
to mix and match mesh resolutions, connectivity, and type (tetrahedral,
hexahedral) in training data; to accelerate simulations with novel
discretizations unseen during training; and to accelerate adaptive simulations
that temporally or parametrically change the discretization.
We present a flexible, discretization-independent approach to reduced-order
modeling. Like traditional ROM, we represent the configuration as a linear
combination of displacement fields. Unlike traditional ROM, our displacement
fields are continuous maps from every point on the reference domain to a
corresponding displacement vector; these maps are represented as implicit
neural fields.
With linear continuous ROM (LiCROM), our training set can include multiple
geometries undergoing multiple loading conditions, independent of their
discretization. This opens the door to novel applications of reduced order
modeling. We can now accelerate simulations that modify the geometry at
runtime, for instance via cutting, hole punching, and even swapping the entire
mesh. We can also accelerate simulations of geometries unseen during training.
We demonstrate one-shot generalization, training on a single geometry and
subsequently simulating various unseen geometries
Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation
Authoring dynamic garment shapes for character animation on body motion is one of the fundamental steps in the CG industry. Established workflows are either time and labor consuming (i.e., manual editing on dense frames with controllers), or lack keyframe-level control (i.e., physically-based simulation). Not surprisingly, garment authoring remains a bottleneck in many production pipelines. Instead, we present a deep-learning-based approach for semi-automatic authoring of garment animation, wherein the user provides the desired garment shape in a selection of keyframes, while our system infers a latent representation for its motion-independent intrinsic parameters (e.g., gravity, cloth materials, etc.). Given new character motions, the latent representation allows to automatically generate a plausible garment animation at interactive rates. Having factored out character motion, the learned intrinsic garment space enables smooth transition between keyframes on a new motion sequence. Technically, we learn an intrinsic garment space with an motion-driven autoencoder network, where the encoder maps the garment shapes to the intrinsic space under the condition of body motions, while the decoder acts as a differentiable simulator to generate garment shapes according to changes in character body motion and intrinsic parameters. We evaluate our approach qualitatively and quantitatively on common garment types. Experiments demonstrate our system can significantly improve current garment authoring workflows via an interactive user interface. Compared with the standard CG pipeline, our system significantly reduces the ratio of required keyframes from 20% to 1 -- 2%
Deep Conservation: A latent-dynamics model for exact satisfaction of physical conservation laws
This work proposes an approach for latent-dynamics learning that exactly
enforces physical conservation laws. The method comprises two steps. First, the
method computes a low-dimensional embedding of the high-dimensional
dynamical-system state using deep convolutional autoencoders. This defines a
low-dimensional nonlinear manifold on which the state is subsequently enforced
to evolve. Second, the method defines a latent-dynamics model that associates
with the solution to a constrained optimization problem. Here, the objective
function is defined as the sum of squares of conservation-law violations over
control volumes within a finite-volume discretization of the problem; nonlinear
equality constraints explicitly enforce conservation over prescribed subdomains
of the problem. Under modest conditions, the resulting dynamics model
guarantees that the time-evolution of the latent state exactly satisfies
conservation laws over the prescribed subdomains
Implicit Neural Representation for Physics-driven Actuated Soft Bodies
Active soft bodies can affect their shape through an internal actuation
mechanism that induces a deformation. Similar to recent work, this paper
utilizes a differentiable, quasi-static, and physics-based simulation layer to
optimize for actuation signals parameterized by neural networks. Our key
contribution is a general and implicit formulation to control active soft
bodies by defining a function that enables a continuous mapping from a spatial
point in the material space to the actuation value. This property allows us to
capture the signal's dominant frequencies, making the method discretization
agnostic and widely applicable. We extend our implicit model to mandible
kinematics for the particular case of facial animation and show that we can
reliably reproduce facial expressions captured with high-quality capture
systems. We apply the method to volumetric soft bodies, human poses, and facial
expressions, demonstrating artist-friendly properties, such as simple control
over the latent space and resolution invariance at test time.Comment: Accepted to SIGGRAPH 2022. Project page:
https://studios.disneyresearch.com/2022/07/24/implicit-neural-representation-for-physics-driven-actuated-soft-bodies/
Video: https://www.youtube.com/watch?v=9EERe_CTaz
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
We propose a hybrid neural network and physics framework for reduced-order
modeling of elastoplasticity and fracture. State-of-the-art scientific
computing models like the Material Point Method (MPM) faithfully simulate
large-deformation elastoplasticity and fracture mechanics. However, their long
runtime and large memory consumption render them unsuitable for applications
constrained by computation time and memory usage, e.g., virtual reality. To
overcome these barriers, we propose a reduced-order framework. Our key
innovation is training a low-dimensional manifold for the Kirchhoff stress
field via an implicit neural representation. This low-dimensional neural stress
field (NSF) enables efficient evaluations of stress values and,
correspondingly, internal forces at arbitrary spatial locations. In addition,
we also train neural deformation and affine fields to build low-dimensional
manifolds for the deformation and affine momentum fields. These neural stress,
deformation, and affine fields share the same low-dimensional latent space,
which uniquely embeds the high-dimensional simulation state. After training, we
run new simulations by evolving in this single latent space, which drastically
reduces the computation time and memory consumption. Our general
continuum-mechanics-based reduced-order framework is applicable to any
phenomena governed by the elastodynamics equation. To showcase the versatility
of our framework, we simulate a wide range of material behaviors, including
elastica, sand, metal, non-Newtonian fluids, fracture, contact, and collision.
We demonstrate dimension reduction by up to 100,000X and time savings by up to
10X