About

Jernej Barbic received an undergraduate Mathematics degree from the University of Ljubljana, Slovenia, and a Ph.D. in computer science from Carnegie Mellon University in 2007. He was a postdoctoral researcher at MIT from 2007 to 2009 before joining the faculty of the Computer Science Department at the University of Southern California in August 2009. He was promoted to Associate Professor with tenure in 2015 and to Full Professor in 2021. His research focuses on computer graphics, addressing interdisciplinary problems in computer animation, graphics, simulation, mechanics, medical imaging, and haptics. The main emphasis of his work is on physically based simulation of digital humans, animals, plants, and man-made objects, with applications in the film industry, computer games, interactive CAD/CAM for manufacturing, and virtual medicine such as surgery simulation. His interests include computer graphics, animation, real-time simulation, numerical mathematics, the Finite Element Method, applied mechanics, collision detection, contact, medical imaging, human body modeling, haptic rendering, and model reduction. Throughout his career, he has received numerous awards, including the ACM Distinguished Member in 2020, the Infinity Festival Monolith Award for Technology in 2019, the USC Stevens Commercialization Award in 2017, a Sloan Foundation Fellowship in 2014, an Okawa Foundation Research Grant in 2014, and recognition as one of MIT Technology Review's 35 Innovators Under 35 in 2011. He has also been honored with the USC Viterbi School of Engineering Viterbi Early Career Chair and the NSF CAREER Award.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Computer graphics (images)
• Theoretical computer science
• Algorithm
• Structural engineering
• Telecommunications
• Mathematical analysis
• Geometry
• Engineering
• Combinatorics

Selected publications

• ANIME-Rod: Adjustable Nonlinear Isotropic Materials for Elastic Rods

  ACM Transactions on Graphics · 2025-07-27

  articleOpen accessSenior author

  We give a method to simulate large deformations of 3D elastic rods under arbitrary nonlinear isotropic 3D solid materials. Rod elastic energies in existing graphics literature are derived from volumetric models under the small-strain linearization assumptions. While the resulting equations can and are commonly applied to large deformations, the material modeling has been limited to a single material, namely linear Hooke law. Starting from any 3D solid nonlinear isotropic elastic energy density function ψ , we derive our rod elastic energy by subjecting the 3D solid volumetric material to the limit process whereby rod thickness is decreased to zero. This enables us to explain rod stretching, bending and twisting in a unified model. Care must be taken to adequately model cross-sectional in-plane and out-of-plane deformations. Our key insight is to compute the three cross-sectional deformation modes corresponding to bending (in the two directions) and twisting, using linear theory. Then, given any ψ , we use these modes to derive an analytical formula for a 5D "macroscopic" large-deformation rod elastic energy function of the local longitudinal stretch, radial scaling, the two bending curvatures and torsion. Our model matches linear theory for small deformations, including cross-sectional shrinkage due to Poisson's effect, and produces correct bending and torsional constants. Our experiments demonstrate that our energy closely matches volumetric FEM even under large stretches and curvatures, whereas commonly used methods in graphics deviate from it. We also compare to closely related work from mechanics literature; we give an explicit expansion of all energy terms in terms of the rod cross-section diameter, allowing independent adjustment of stretching, bending and twisting. Finally, we observe an inherent limitation in the ability of rod models to control nonlinear bendability and twistability. We propose to "relax" rod physics to more easily control nonlinear bending and twisting in computer graphics applications.

  PublisherDOI

• Optimal r-Adaptive In-Timestep Remeshing for Elastodynamics

  ACM Transactions on Graphics · 2025-07-27

  article

  We propose a coupled mesh-adaptation model and physical simulation algorithm to jointly generate, per timestep, optimal adaptive remeshings and implicit solutions for the simulation of frictionally contacting elastodynamics. To do so, we begin with Ferguson et al.'s [2023] recently developed in-timestep remeshing (ITR) framework, which proposes an Incremental Potential based objective for mesh refinement, and a corresponding, locally greedy remeshing algorithm to minimize it. While this initial ITR framework demonstrates significant improvements, its greedy remeshing does not generate optimal meshes, and so does not converge to improving physical solutions with increasing mesh resolution. In practice, due to lack of optimality, the original ITR framework can and will find mesh and state solutions with unnecessarily low-quality geometries and corresponding physical solution artifacts. At the same time, we also identify additional fundamental challenges to adaptive simulation in terms of both ITR's original remeshing objective and its corresponding optimization problem formulation. In this work, in order to extend the ITR framework to high-quality, optimal in-timestep remeshing, we first construct a new remeshing objective function built from simple, yet critical, updates to the Incremental Potential energy, and a corresponding constrained model problem, whose minimizers provide locally optimal remeshings for physical problems. We then propose a new in-timestep remeshing optimization that jointly solves, per-timestep, for a new locally optimal remeshing and the next physical state defined upon it. To evaluate and demonstrate our extension of the ITR framework, we apply it to the optimal r-adaptive ITR simulation of frictionally contacting elasto-dynamics and statics. To enable r-adaptivity we additionally propose a new numerical method to robustly compute derivatives of the L 2 -projection operator necessary for optimal mesh-to-mesh state mappings within solves, a constraint model to enable on-boundary node adaptivity, and an efficient Newton-type optimization method for practically solving each per-timestep r-adaptive ITR solution. We extensively evaluate our method on challenging large-deformation and frictionally contacting scenarios. Here we observe optimal r-adaptivity captures comparable and better accuracy than unadapted meshes orders-of-magnitude larger, with corresponding significant advantages in both computation speedup and decrease in memory usage.

  PublisherDOI

• Seeing the Wind from a Falling Leaf

  ArXiv.org · 2025-11-30

  preprintOpen access

  A longstanding goal in computer vision is to model motions from videos, while the representations behind motions, i.e. the invisible physical interactions that cause objects to deform and move, remain largely unexplored. In this paper, we study how to recover the invisible forces from visual observations, e.g., estimating the wind field by observing a leaf falling to the ground. Our key innovation is an end-to-end differentiable inverse graphics framework, which jointly models object geometry, physical properties, and interactions directly from videos. Through backpropagation, our approach enables the recovery of force representations from object motions. We validate our method on both synthetic and real-world scenarios, and the results demonstrate its ability to infer plausible force fields from videos. Furthermore, we show the potential applications of our approach, including physics-based video generation and editing. We hope our approach sheds light on understanding and modeling the physical process behind pixels, bridging the gap between vision and physics. Please check more video results in our \href{https://chaoren2357.github.io/seeingthewind/}{project page}.

  PublisherOA PDFDOI

• Volume Rendering of Human Hand Anatomy

  arXiv (Cornell University) · 2024-11-14

  preprintOpen accessSenior author

  We study the design of transfer functions for volumetric rendering of magnetic resonance imaging (MRI) datasets of human hands. Human hands are anatomically complex, containing various organs within a limited space, which presents challenges for volumetric rendering. We focus on hand musculoskeletal organs because they are volumetrically the largest inside the hand, and most important for the hand's main function, namely manipulation of objects. While volumetric rendering is a mature field, the choice of the transfer function for the different organs is arguably just as important as the choice of the specific volume rendering algorithm; we demonstrate that it significantly influences the clarity and interpretability of the resulting images. We assume that the hand MRI scans have already been segmented into the different organs (bones, muscles, tendons, ligaments, subcutaneous fat, etc.). Our method uses the hand MRI volume data, and the geometry of its inner organs and their known segmentation, to produce high-quality volume rendering images of the hand, and permits fine control over the appearance of each tissue. We contribute two families of transfer functions to emphasize different hand tissues of interest, while preserving the visual context of the hand. We also discuss and reduce artifacts present in standard volume ray-casting of human hands. We evaluate our volumetric rendering on five challenging hand motion sequences. Our experimental results demonstrate that our method improves hand anatomy visualization, compared to standard surface and volume rendering techniques.

  PublisherOA PDFDOI

• Tuning Nonlinear Elastic Materials under Small and Large Deformations

  arXiv (Cornell University) · 2024-12-21

  preprintOpen accessSenior author

  In computer graphics and engineering, nonlinear elastic material properties of 3D volumetric solids are typically adjusted by selecting a material family, such as St. Venant Kirchhoff, Linear Corotational, (Stable) Neo-Hookean, Ogden, etc., and then selecting the values of the specific parameters for that family, such as the Lame parameters, Ogden exponents, or whatever the parameterization of a particular family may be. However, the relationships between those parameter values, and visually intuitive material properties such as object's "stiffness", volume preservation, or the "amount of nonlinearity", are less clear and can be tedious to tune. For an arbitrary isotropic hyperelastic energy density function psi that is not parameterized in terms of the Lame parameters, it is not even clear what the Lame parameters and Young's modulus and Poisson's ratio are. Starting from psi, we first give a concise definition of Lame parameters, and therefore Young's modulus and Poisson's ratio. Second, we give a method to adjust the object's three salient properties, namely two small-deformation properties (overall "stiffness", and amount of volume preservation, prescribed by object's Young's modulus and Poisson's ratio), and one large-deformation property (material nonlinearity). We do this in a manner whereby each of these three properties is decoupled from the other two properties, and can therefore be set independently. This permits a new ability, namely "normalization" of materials: starting from two distinct materials, we can "normalize" them so that they have the same small deformation properties, or the same large-deformation nonlinearity behavior, or both. Furthermore, our analysis produced a useful theoretical result, namely it establishes that Linear Corotational materials (arguably the most widely used materials in computer graphics) are the simplest possible nonlinear materials.

  PublisherOA PDFDOI

• Multi-Resolution Real-Time Deep Pose-Space Deformation

  ACM Transactions on Graphics · 2024-11-19 · 1 citations

  articleOpen accessSenior author

  We present a hard-real-time multi-resolution mesh shape deformation technique for skeleton-driven soft-body characters. Producing mesh deformations at multiple levels of detail is very important in many applications in computer graphics. Our work targets applications where the multi-resolution shapes must be generated at fast speeds ("hard-real-time", e.g., a few milliseconds at most and preferably under 1 millisecond), as commonly needed in computer games, virtual reality and Metaverse applications. We assume that the character mesh is driven by a skeleton, and that high-quality character shapes are available in a set of training poses originating from a high-quality (slow) rig such as volumetric FEM simulation. Our method combines multi-resolution analysis, mesh partition of unity, and neural networks, to learn the pre-skinning shape deformations in an arbitrary character pose. Combined with linear blend skinning, this makes it possible to reconstruct the training shapes, as well as interpolate and extrapolate them. Crucially, we simultaneously achieve this at hard real-time rates and at multiple mesh resolution levels. Our technique makes it possible to trade deformation quality for memory and computation speed, to accommodate the strict requirements of modern real-time systems. Furthermore, we propose memory layout and code improvements to boost computation speeds. Previous methods for realtime approximations of quality shape deformations did not focus on hard real-time, or did not investigate the multi-resolution aspect of the problem. Compared to a "naive" approach of separately processing each hierarchical level of detail, our method offers a substantial memory reduction as well as computational speedups. It also makes it possible to construct the shape progressively level by level and interrupt the computation at any time, enabling graceful degradation of the deformation detail. We demonstrate our technique on several examples, including a stylized human character, human hands, and an inverse-kinematics-driven quadruped animal.

  PublisherOA PDFDOI

• Large-Strain Surface Modeling Using Plasticity

  IEEE Transactions on Visualization and Computer Graphics · 2023-06-27

  articleSenior author

  Modeling arbitrarily large deformations of surfaces smoothly embedded in three-dimensional space is challenging. We give a new method to represent surfaces undergoing large spatially varying rotations and strains, based on differential geometry, and surface first and second fundamental forms. Methods that penalize the difference between the current shape and the rest shape produce sharp spikes under large strains, and variational methods produce wiggles, whereas our method naturally supports large strains and rotations without any special treatment. For stable and smooth results, we demonstrate that the deformed surface has to locally satisfy compatibility conditions (Gauss-Codazzi equations) on the first and second fundamental forms. We then give a method to locally modify the surface first and second fundamental forms in a compatible way. We use those fundamental forms to define surface plastic deformations, and finally recover output surface vertex positions by minimizing the surface elastic energy under the plastic deformations. We demonstrate that our method makes it possible to smoothly deform triangle meshes to large spatially varying strains and rotations, while meeting user constraints.

  PublisherDOI

• Capturing Animation-Ready Isotropic Materials Using Systematic Poking

  ACM Transactions on Graphics · 2023-12-05 · 3 citations

  articleOpen accessSenior author

  Capturing material properties of real-world elastic solids is both challenging and highly relevant to many applications in computer graphics, robotics and related fields. We give a non-intrusive, in-situ and inexpensive approach to measure the nonlinear elastic energy density function of man-made materials and biological tissues. We poke the elastic object with 3d-printed rigid cylinders of known radii, and use a precision force meter to record the contact force as a function of the indentation depth, which we measure using a force meter stand, or a novel unconstrained laser setup. We model the 3D elastic solid using the Finite Element Method (FEM), and elastic energy using a compressible Valanis-Landel material that generalizes Neo-Hookean materials by permitting arbitrary tensile behavior under large deformations. We then use optimization to fit the nonlinear isotropic elastic energy so that the FEM contact forces and indentations match their measured real-world counterparts. Because we use carefully designed cubic splines, our materials are accurate in a large range of stretches and robust to inversions, and are therefore "animation-ready" for computer graphics applications. We demonstrate how to exploit radial symmetry to convert the 3D elastostatic contact problem to the mathematically equivalent 2D problem, which vastly accelerates optimization. We also greatly improve the theory and robustness of stretch-based elastic materials, by giving a simple and elegant formula to compute the tangent stiffness matrix, with rigorous proofs and singularity handling. We also contribute the observation that volume compressibility can be estimated by poking with rigid cylinders of different radii, which avoids optical cameras and greatly simplifies experiments. We validate our method by performing full 3D simulations using the optimized materials and confirming that they match real-world forces, indentations and real deformed 3D shapes. We also validate it using a "Shore 00" durometer, a standard device for measuring material hardness.

  PublisherDOI

• Kirchhoff-Love Shells with Arbitrary Hyperelastic Materials

  ACM Transactions on Graphics · 2023-12-05 · 8 citations

  articleOpen accessSenior author

  Kirchhoff-Love shells are commonly used in many branches of engineering, including in computer graphics, but have so far been simulated only under limited nonlinear material options. We derive the Kirchhoff-Love thin-shell mechanical energy for an arbitrary 3D volumetric hyperelastic material, including isotropic materials, anisotropic materials, and materials whereby the energy includes both even and odd powers of the principal stretches. We do this by starting with any 3D hyperelastic material, and then analytically computing the corresponding thin-shell energy limit. This explicitly identifies and separates in-plane stretching and bending terms, and avoids numerical quadrature. Thus, in-plane stretching and bending are shown to originate from one and the same process (volumetric elasticity of thin objects), as opposed to from two separate processes as done traditionally in cloth simulation. Because we can simulate materials that include both even and odd powers of stretches, we can accommodate standard mesh distortion energies previously employed for 3D solid simulations, such as Symmetric ARAP and Co-rotational materials. We relate the terms of our energy to those of prior work on Kirchhoff-Love thin-shells in computer graphics that assumed small in-plane stretches, and demonstrate the visual difference due to the presence of our exact stretching and bending terms. Furthermore, our formulation allows us to categorize all distinct hyperelastic Kirchhoff-Love thin-shell energies. Specifically, we prove that for Kirchhoff-Love thin-shells, the space of all hyperelastic materials collapses to two-dimensional hyperelastic materials. This observation enables us to create an interface for the design of thin-shell Kirchhoff-Love mechanical energies, which in turn enables us to create thin-shell materials that exhibit arbitrary stiffness profiles under large deformations.

  PublisherOA PDFDOI

• Parameter Estimation for Deformable Objects in Robotic Manipulation Tasks

  Springer proceedings in advanced robotics · 2023-01-01 · 1 citations

  book-chapter
  PublisherDOI

Recent grants

• CHS: Small: Highly Realistic Virtual Human Hands using Anatomically Based Modeling

  NSF · $499k · 2019–2024

• CAREER: Interactive Physically Based Animation and Optimal Control Using Model Reduction

  NSF · $487k · 2011–2018

• CHS: Small: Fast simulation of geometrically complex multibody systems in contact and self-contact

  NSF · $484k · 2014–2019

Frequent coauthors

• Yili Zhao

  Southwest Forestry University

  17 shared

• Hongyi Xu

  Shanghai Xuhui Central Hospital

  17 shared

• Mianlun Zheng

  University of Southern California

  16 shared

• Doug L. James

  Stanford University

  15 shared

• Yijing Li

  Chengdu University of Technology

  14 shared

• Funshing Sin

  13 shared

• Bohan Wang

  13 shared

• Paul Debevec

  Netflix (United States)

  12 shared

Awards & honors

• 2020 ACM Distinguished Member
• 2019 Infinity Festival Monolith Award for Technology
• 2017 USC Stevens Commercialization Award
• 2014 Sloan Foundation Sloan Fellowship
• 2014 Okawa Foundation Research Grant