Physics-Based Animation

Donglai Xiang, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier Romero, Weipeng Xu, Shunsuke Saito, Jingfan Guo, Breannan Smith, Takaaki Shiratori, Yaser Sheikh, Jessica Hodgins, Chenglei Wu

Despite recent progress in developing animatable full-body avatars, realistic modeling of clothing – one of the core aspects of human self-expression – remains an open challenge. State-of-the-art physical simulation methods can generate realistically behaving clothing geometry at interactive rates. Modeling photorealistic appearance, however, usually requires physically-based rendering which is too expensive for interactive applications. On the other hand, data-driven deep appearance models are capable of efficiently producing realistic appearance, but struggle at synthesizing geometry of highly dynamic clothing and handling challenging body-clothing configurations. To this end, we introduce pose-driven avatars with explicit modeling of clothing that exhibit both photorealistic appearance learned from real-world data and realistic clothing dynamics. The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry. Our core contribution is a physically-inspired appearance network, capable of generating photorealistic appearance with view-dependent and dynamic shadowing effects even for unseen body-clothing configurations. We conduct a thorough evaluation of our model and demonstrate diverse animation results on several subjects and different types of clothing. Unlike previous work on photorealistic full-body avatars, our approach can produce much richer dynamics and more realistic deformations even for many examples of loose clothing. We also demonstrate that our formulation naturally allows clothing to be used with avatars of different people while staying fully animatable, thus enabling, for the first time, photorealistic avatars with novel clothing.

Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing

Xudong Feng, Wenchao Huang, Weiwei Xu, Huamin Wang

Real-world fabrics often possess complicated nonlinear, anisotropic bending stiffness properties. Measuring the physical parameters of such properties for physics-based simulation is difficult yet unnecessary, due to the persistent existence of numerical errors in simulation technology. In this work, we propose to adopt a simulation-in-the-loop strategy: instead of measuring the physical parameters, we estimate the simulation parameters to minimize the discrepancy between reality and simulation. This strategy offers good flexibility in test setups, but the associated optimization problem is computationally expensive to solve by numerical methods. Our solution is to train a regression-based neural network for inferring bending stiffness parameters, directly from drape features captured in the real world. Specifically, we choose the Cusick drape test method and treat multiple-view depth images as the feature vector. To effectively and efficiently train our network, we develop a highly expressive and physically validated bending stiffness model, and we use the traditional cantilever test to collect the parameters of this model for 618 real-world fabrics. Given the whole parameter data set, we then construct a parameter subspace, generate new samples within the subspace, and finally simulate and augment synthetic data for training purposes. The experiment shows that our trained system can replace cantilever tests for quick, reliable and effective estimation of simulation-ready parameters. Thanks to the use of the system, our simulator can now faithfully simulate bending effects comparable to those in the real world.

Learning-Based Bending Stiffness Parameter Estimation by a Drape Tester

Hugo Bertiche, Meysam Madadi, Sergio Escalera

We present a general framework for the garment animation problem through unsupervised deep learning inspired in physically based simulation. Existing trends in the literature already explore this possibility. Nonetheless, these approaches do not handle cloth dynamics. Here, we propose the first methodology able to learn realistic cloth dynamics unsupervisedly, and henceforth, a general formulation for neural cloth simulation. The key to achieve this is to adapt an existing optimization scheme for motion from simulation based methodologies to deep learning. Then, analyzing the nature of the problem, we devise an architecture able to automatically disentangle static and dynamic cloth subspaces by design. We will show how this improves model performance. Additionally, this opens the possibility of a novel motion augmentation technique that greatly improves generalization. Finally, we show it also allows to control the level of motion in the predictions. This is a useful, never seen before, tool for artists. We provide of detailed analysis of the problem to establish the bases of neural cloth simulation and guide future research into the specifics of this domain.

Neural Cloth Simulation

Meng Zhang, Duygu Ceylan, Niloy J. Mitra

Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the previous state of the garment as well as its relative position with respect to the underlying body. Technically, we model garment dynamics, driven using the input character motion, by predicting per-frame local displacements in a canonical state of the garment that is enriched with frame-dependent skinning weights to bring the garment to the global space. We resolve any remaining per-frame collisions by predicting residual local displacements. The resultant garment geometry is used as history to enable iterative roll-out prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.

Motion Guided Deep Dynamic 3D Garments

Ty Trusty, Danny M. Kaufman, David I. W. Levin

We propose and explore a new method for the implicit time integration of elastica. Key to our approach is the use of a mixed variational principle. In turn, its finite element discretization leads to an efficient and accurate sequential quadratic programming solver with a superset of the desirable properties of many previous integration strategies. This framework fits a range of elastic constitutive models and remains stable across a wide span of time step sizes and material parameters (including problems that are approximately rigid). Our method exhibits convergence on par with full Newton type solvers and also generates visually plausible results in just a few iterations comparable to recent fast simulation methods that do not converge. These properties make it suitable for both offline accurate simulation and performant applications with expressive physics. We demonstrate the efficacy of our approach on a number of simulated examples.

Mixed Variational Finite Elements for Implicit Simulation of Deformables

Jiayi Eris Zhang, Jérémie Dumas, Yun (Raymond) Fei, Alec Jacobson, Doug L. James, Danny M. Kaufman

The trade-off between speed and fidelity in cloth simulation is a fundamental computational problem in computer graphics and computational design. Coarse cloth models provide the interactive performance required by designers, but they can not be simulated at higher resolutions (“up-resed”) without introducing simulation artifacts and/or unpredicted outcomes, such as different folds, wrinkles and drapes. But how can a coarse simulation predict the result of an unconstrained, high-resolution simulation that has not yet been run? We propose Progressive Cloth Simulation (PCS), a new forward simulation method for efficient preview of cloth quasistatics on exceedingly coarse triangle meshes with consistent and progressive improvement over a hierarchy of increasingly higher-resolution models. PCS provides an efficient coarse previewing simulation method that predicts the coarse-scale folds and wrinkles that will be generated by a corresponding converged, high-fidelity C-IPC simulation of the cloth drape’s equilibrium. For each preview PCS can generate an increasing-resolution sequence of consistent models that progress towards this converged solution. This successive improvement can then be interrupted at any point, for example, whenever design parameters are updated. PCS then ensures feasibility at all resolutions, so that predicted solutions remain intersection-free and capture the complex folding and buckling behaviors of frictionally contacting cloth.

Progressive Simulation for Cloth Quasistatics

Seung-wook Kim, JungHyun Han

This paper presents a novel method for simulating inducible rigid magnets efficiently and stably. In the proposed method, inducible magnets are magnetized by a modified magnetization dynamics, so that the magnetic equilibrium can be obtained in a computationally efficient manner. Furthermore, our model of magnetic forces takes magnetization change into account to produce stable motions of inducible magnets. The experiments show that the proposed method enables a large-scale simulation involving a huge number of inducible magnets.

Fast Stabilization of Inducible Magnet Simulation

Huancheng Lin, Floyd M. Chitalu, Taku Komura

Isotropic As-Rigid-As-Possible (ARAP) energy has been popular for shape editing, mesh parametrisation and soft-body simulation for almost two decades. However, a formulation using Cauchy-Green (CG) invariants has always been unclear, due to a rotation-polluted trace term that cannot be directly expressed using these invariants. We show how this incongruent trace term can be understood via an implicit relationship to the CG invariants. Our analysis reveals this relationship to be a polynomial where the roots equate to the trace term, and where the derivatives also give rise to closed-form expressions of the Hessian to guarantee positive semi-definiteness for a fast and concise Newton-type implicit time integration. A consequence of this analysis is a novel approach to determine rotations and singular values of deformation-gradient tensors without explicit/numerical factorization which is significant, resulting in up-to 3.5× speedup and benefits energy function evaluation for reducing solver time. We validate our energy formulation by experiments and comparison, demonstrating that our resulting eigendecomposition using the CG invariants is equivalent to existing ARAP formulations. We thus reveal isotropic ARAP energy to be a member of the “Cauchy-Green club”, meaning that it can indeed be defined using CG invariants and therefore that the closed-form expressions of the resulting Hessian are shared with other energies written in their terms.

Isotropic ARAP energy using Cauchy-Green invariants

Julian Panetta, Haleh Mohammadian, Emiliano Luci, Vahid Babaei

Objects with different shapes can dissolve in significantly different ways inside a solution. Predicting different shapes’ dissolution dynamics is an important problem especially in pharmaceutics. More important and challenging, however, is controlling the dissolution via shape, \ie, designing shapes that lead to a desired release behavior of materials in a solvent over a specific time. Here, we tackle this challenge by introducing a computational inverse design pipeline. We begin by introducing a simple, physically-inspired differentiable forward model of dissolution. % that is both efficient and amenable to differentiation. We then formulate our inverse design as a PDE-constrained topology optimization that has access to analytical derivatives obtained via sensitivity analysis. Furthermore, we incorporate fabricability terms in the optimization objective that enable physically realizing our designs. We thoroughly analyze our approach on a diverse set of examples via both simulation and fabrication.

Shape from Release: Inverse Design and Fabrication of Controlled Release Structures

Mianlun Zheng*, Bohan Wang*, Jingtao Huang, Jernej Barbič (*joint first authors)

Precision modeling of the hand internal musculoskeletal anatomy has been largely limited to individual poses, and has not been connected into continuous volumetric motion of the hand anatomy actuating across the hand’s entire range of motion. This is for a good reason, as hand anatomy and its motion are extremely complex and cannot be predicted merely from the anatomy in a single pose. We give a method to simulate the volumetric shape of hand’s musculoskeletal organs to any pose in the hand’s range of motion, producing external hand shapes and internal organ shapes that match ground truth optical scans and medical images (MRI) in multiple scanned poses. We achieve this by combining MRI images in multiple hand poses with FEM multibody nonlinear elastoplastic simulation. Our system models bones, muscles, tendons, joint ligaments and fat as separate volumetric organs that mechanically interact through contact and attachments, and whose shape matches medical images (MRI) in the MRI-scanned hand poses. The match to MRI is achieved by incorporating pose-space deformation and plastic strains into the simulation. We show how to do this in a non-intrusive manner that still retains all the simulation benefits, namely the ability to prescribe realistic material properties, generalize to arbitrary poses, preserve volume and obey contacts and attachments. We use our method to produce volumetric renders of the internal anatomy of the human hand in motion, and to compute and render highly realistic hand surface shapes. We evaluate our method by comparing it to optical scans, and demonstrate that we qualitatively and quantitatively substantially decrease the error compared to previous work. We test our method on five complex hand sequences, generated either using keyframe animation or performance animation using modern hand tracking techniques.

Simulation of Hand Anatomy Using Medical Imaging