Direct Position‐Based Solver for Stiff Rods

In this paper, we present a novel direct solver for the efficient simulation of stiff, inextensible elastic rods within the position‐based dynamics (PBD) framework. It is based on the XPBD algorithm, which extends PBD to simulate elastic objects with physically meaningful material parameters. XPBD approximates an implicit Euler integration and solves the system of non‐linear equations using a non‐linear Gauss–Seidel solver. However, this solver requires many iterations to converge for complex models and if convergence is not reached, the material becomes too soft. In contrast, we use Newton iterations in combination with our direct solver to solve the non‐linear equations which significantly improves convergence by solving all constraints of an acyclic structure (tree), simultaneously. Our solver only requires a few Newton iterations to achieve high stiffness and inextensibility. We model inextensible rods and trees using rigid segments connected by constraints. Bending and twisting constraints are derived from the well‐established Cosserat model. The high performance of our solver is demonstrated in highly realistic simulations of rods consisting of multiple 10 000 segments. In summary, our method allows the efficient simulation of stiff rods in the PBD framework with a speedup of two orders of magnitude compared to the original XPBD approach.     

Physically‐based forehead animation including wrinkles

Physically‐based animation techniques enable more realistic and accurate animation to be created. We present a fully physically‐based approach for efficiently producing realistic‐looking animations of facial movement, including animation of expressive wrinkles. This involves simulation of detailed voxel‐based models using a graphics processing unit‐based total Lagrangian explicit dynamic finite element solver with an anatomical muscle contraction model, and advanced boundary conditions that can model the sliding of soft tissue over the skull. The flexibility of our approach enables detailed animations of gross and fine‐scale soft‐tissue movement to be easily produced with different muscle structures and material parameters, for example, to animate different aged skins. Although we focus on the forehead, our approach can be used to animate any multi‐layered soft body. © 2014 The Authors. Computer Animation and Virtual Worlds published by John Wiley & Sons, Ltd.     

Proximity Based Automatic Data Annotation for Autonomous Driving

The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding.Today,most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging(LIDAR)and HD maps with high level annotations.In this paper,we propose a scalable and affordable data collection and annotation framework image-to-map annotation proximity(I2MAP),for affordance learning in autonomous driving applications.We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map(OSM).Our benchmark consists of 40000 images with more than40 affordance labels under various day time and weather even with very challenging heavy snow.We implemented sample advanced driver-assistance systems(ADAS)functions by training our data with neural networks(NN)and cross-validate the results on benchmarks like KITTI and BDD100K,which indicate the effectiveness of our framework and training models.     

Real-Time Finite-Element Simulation of Linear Viscoelastic Tissue Behavior Based on Experimental Data

We propose an end-to-end solution to real-time and realistic finite-element modeling and simulation of viscoelastic soft tissue behavior. We provide an efficient numerical scheme for solving a linear viscoelastic FEM model derived from the generalized Maxwell solid, and present methods for measuring and integrating experimental data on the viscoelastic material properties of soft tissues into the model for realistic display of visual deformations and interaction forces. Our precomputation scheme and multilayer computational architecture enable the model's real-time execution with visual and haptic feedback to the user. Our approach includes time- and rate-dependent effects, which requires considering a node's loading history in our displacement computations at each cycle of the simulation     

Urban Walkability Design Using Virtual Population Simulation

We present a system to generate a procedural environment that produces a desired crowd behaviour. Instead of altering the behavioural parameters of the crowd itself, we automatically alter the environment to yield such desired crowd behaviour. This novel inverse approach is useful both to crowd simulation in virtual environments and to urban crowd planning applications. Our approach tightly integrates and extends a space discretization crowd simulator with inverse procedural modelling. We extend crowd simulation by goal exploration (i.e. agents are initially unaware of the goal locations), variable‐appealing sign usage and several acceleration schemes. We use Markov chain Monte Carlo to quickly explore the solution space and yield interactive design. We have applied our method to a variety of virtual and real‐world locations, yielding one order of magnitude faster crowd simulation performance over related methods and several fold improvement of crowd indicators.     

A Practical Method for Animating Anisotropic Elastoplastic Materials

This paper introduces a simple method for simulating highly anisotropic elastoplastic material behaviors like the dissolution of fibrous phenomena (splintering wood, shredding bales of hay) and materials composed of large numbers of irregularly-shaped bodies (piles of twigs, pencils, or cards). We introduce a simple transformation of the anisotropic problem into an equivalent isotropic one, and we solve this new “fictitious” isotropic problem using an existing simulator based on the material point method. Our approach results in minimal changes to existing simulators, and it allows us to re-use popular isotropic plasticity models like the Drucker-Prager yield criterion instead of inventing new anisotropic plasticity models for every phenomenon we wish to simulate.     

Real‐time haptic manipulation and cutting of hybrid soft tissue models by extended position‐based dynamics

This paper systematically describes an interactive dissection approach for hybrid soft tissue models governed by extended position‐based dynamics. Our framework makes use of a hybrid geometric model comprising both surface and volumetric meshes. The fine surface triangular mesh with high‐precision geometric structure and texture at the detailed level is employed to represent the exterior structure of soft tissue models. Meanwhile, the interior structure of soft tissues is constructed by coarser tetrahedral mesh, which is also employed as physical model participating in dynamic simulation. The less details of interior structure can effectively reduce the computational cost during simulation. For physical deformation, we design and implement an extended position‐based dynamics approach that supports topology modification and material heterogeneities of soft tissue. Besides stretching and volume conservation constraints, it enforces the energy preserving constraints, which take the different spring stiffness of material into account and improve the visual performance of soft tissue deformation. Furthermore, we develop mechanical modeling of dissection behavior and analyze the system stability. The experimental results have shown that our approach affords real‐time and robust cutting without sacrificing realistic visual performance. Our novel dissection technique has already been integrated into a virtual reality‐based laparoscopic surgery simulator. Copyright © 2015 John Wiley & Sons, Ltd.     

Position‐Based Skinning for Soft Articulated Characters

In this paper, we introduce a two‐layered approach addressing the problem of creating believable mesh‐based skin deformation. For each frame, the skin is first deformed with a classic linear blend skinning approach, which usually leads to unsightly artefacts such as the well‐known candy‐wrapper effect and volume loss. Then we enforce some geometric constraints which displace the positions of the vertices to mimic the behaviour of the skin and achieve effects like volume preservation and jiggling. We allow the artist to control the amount of jiggling and the area of the skin affected by it. The geometric constraints are solved using a position‐based dynamics (PBDs) schema. We employ a graph colouring algorithm for parallelizing the computation of the constraints. Being based on PBDs guarantees efficiency and real‐time performances while enduring robustness and unconditional stability. We demonstrate the visual quality and the performance of our approach with a variety of skeleton‐driven soft body characters.     

A Semi-Procedural Convolutional Material Prior

Lightweight material capture methods require a material prior, defining the subspace of plausible textures within the large space of unconstrained texel grids. Previous work has either used deep neural networks (trained on large synthetic material datasets) or procedural node graphs (constructed by expert artists) as such priors. In this paper, we propose a semi-procedural differentiable material prior that represents materials as a set of (typically procedural) grayscale noises and patterns that are processed by a sequence of lightweight learnable convolutional filter operations. We demonstrate that the restricted structure of this architecture acts as an inductive bias on the space of material appearances, allowing us to optimize the weights of the convolutions per-material, with no need for pre-training on a large dataset. Combined with a differentiable rendering step and a perceptual loss, we enable single-image tileable material capture comparable with state of the art. Our approach does not target the pixel-perfect recovery of the material, but rather uses noises and patterns as input to match the target appearance. To achieve this, it does not require complex procedural graphs, and has a much lower complexity, computational cost and storage cost. We also enable control over the results, through changing the provided patterns and using guide maps to push the material properties towards a user-driven objective.     

Detection and Synthesis of Full-Body Environment Interactions for Virtual Humans

We present a new methodology for enabling virtual humans to autonomously detect and perform complex full-body interactions with their environments. Given a parameterized walking controller and a set of motion-captured example interactions, our method is able to detect when interactions can occur and to coordinate the detected upper-body interaction with the walking controller in order to achieve full-body mobile interactions in similar situations. Our approach is based on learning spatial coordination features from the example motions and on associating body-environment proximity information to the body configurations of each performed action. Body configurations become the input to a regression system, which in turn is able to generate new interactions for different situations in similar environments. The regression model is capable of selecting, encoding and replicating key spatial strategies with respect to body coordination and management of environment constraints as well as determining the correct moment in time and space for starting an interaction. As a result, we obtain an interactive controller able to detect and synthesize coordinated full-body motions for a variety of complex interactions requiring body mobility. Our results achieve complex interactions, such as opening doors and drawing in a wide whiteboard. The presented approach introduces the concept of learning interaction coordination models that can be applied on top of any given walking controller. The obtained method is simple and flexible, it handles the detection of possible interactions and is suitable for real-time applications.     

Online Motion Capture Marker Labeling for Multiple Interacting Articulated Targets

In this paper, we propose an online motion capture marker labeling approach for multiple interacting articulated targets. Given hundreds of unlabeled motion capture markers from multiple articulated targets that are interacting each other, our approach automatically labels these markers frame by frame, by fitting rigid bodies and exploiting trained structure and motion models. Advantages of our approach include: 1) our method is an online algorithm, which requires no user interaction once the algorithm starts. 2) Our method is more robust than traditional the closest point-based approaches by automatically imposing the structure and motion models. 3) Due to the use of the structure model which encodes the rigidity of each articulated body of captured targets, our method can recover missing markers robustly. Our approach is efficient and particularly suited for online computer animation and video game applications.     

Collision‐Aware and Online Compression of Rigid Body Simulations via Integrated Error Minimization

Methods to compress simulation data are invaluable as they facilitate efficient transmission along the visual effects pipeline, fast and efficient replay of simulations for visualization and enable storage of scientific data. However, all current approaches to compressing simulation data require access to the entire dynamic simulation, leading to large memory requirements and additional computational burden. In this paper we perform compression of contact‐dominated, rigid body simulations in an online, error‐bounded fashion. This has the advantage of requiring access to only a narrow window of simulation data at a time while still achieving good agreement with the original simulation. Our approach is simulator agnostic allowing us to compress data from a variety of sources. We demonstrate the efficacy of our algorithm by compressing contact‐dominated rigid body simulations from a number of sources, achieving compression rates of up to 360 times over raw data size.     

Primal/Dual Descent Methods for Dynamics

We examine the relationship between primal, or force-based, and dual, or constraint-based formulations of dynamics. Variational frameworks such as Projective Dynamics have proved popular for deformable simulation, however they have not been adopted for contact-rich scenarios such as rigid body simulation. We propose a new preconditioned frictional contact solver that is compatible with existing primal optimization methods, and competitive with complementarity-based approaches. Our relaxed primal model generates improved contact force distributions when compared to dual methods, and has the advantage of being differentiable, making it well-suited for trajectory optimization. We derive both primal and dual methods from a common variational point of view, and present a comprehensive numerical analysis of both methods with respect to conditioning. We demonstrate our method on scenarios including rigid body contact, deformable simulation, and robotic manipulation.     

Real‐time horse gait synthesis

Horse locomotion exhibits rich variations in gaits and styles. Although there have been many approaches proposed for animating quadrupeds, there is not much research on synthesizing horse locomotion. In this paper, we present a horse locomotion synthesis approach. A user can arbitrarily change a horse's moving speed and direction, and our system would automatically adjust the horse's motion to fulfill the user's commands. At preprocessing, we manually capture horse locomotion data from Eadweard Muybridge's famous photographs of animal locomotion and expand the captured motion database to various speeds for each gait. At runtime, our approach automatically changes gaits based on speed, synthesizes the horse's root trajectory, and adjusts its body orientation based on the horse's turning direction. We propose an asynchronous time warping approach to handle gait transition, which is critical for generating realistic and controllable horse locomotion. Our experiments demonstrate that our system can produce smooth, rich, and controllable horse locomotion in real time. Copyright © 2012 John Wiley & Sons, Ltd.     

Physics-Informed Neural Corrector for Deformation-based Fluid Control

Controlling fluid simulations is notoriously difficult due to its high computational cost and the fact that user control inputs can cause unphysical motion. We present an interactive method for deformation-based fluid control. Our method aims at balancing the direct deformations of fluid fields and the preservation of physical characteristics. We train convolutional neural networks with physics-inspired loss functions together with a differentiable fluid simulator, and provide an efficient workflow for flow manipulations at test time. We demonstrate diverse test cases to analyze our carefully designed objectives and show that they lead to physical and eventually visually appealing modifications on edited fluid data.     

Walk the talk: coordinating gesture with locomotion for conversational characters

Communicative behaviors are a very important aspect of human behavior and deserve special attention when simulating groups and crowds of virtual pedestrians. Previous approaches have tended to focus on generating believable gestures for individual characters and talker‐listener behaviors for static groups. In this paper, we consider the problem of creating rich and varied conversational behaviors for data‐driven animation of walking and jogging characters. We captured ground truth data of participants conversing in pairs while walking and jogging. Our stylized splicing method takes as input a motion captured standing gesture performance and a set of looped full body locomotion clips. Guided by the ground truth metrics, we perform stylized splicing and synchronization of gesture with locomotion to produce natural conversations of characters in motion. Copyright © 2016 John Wiley & Sons, Ltd.     

Reconstruction of tokamak density profiles using feedforward networks

The tokamak is currently the principal magnetic confinement system for controlled fusion research. In seeking to understand the physics of the high temperature plasma inside the tokamak, it is important to have detailed information on the spatial distribution of electron density. One technique for density measurement uses laser interferometry, which gives line-integral information along chords through the plasma. This requires an inversion procedure to extract spatially local density information. In this paper we make use of feedforward networks to extract local density profiles from the line-integral data obtained from the multichannel interferometer on the JET (Joint European Torus) tokamak. An important feature of our approach is the use of profile data from a second high resolution diagnostic system, called LIDAR, to train the network. The LIDAR system provides data at high spatial resolution but with a low repetition rate, and therefore has a complementary rôle to interferometry which operates at a high sampling rate but with much lower spatial resolution. Results show that the neural network is able to extract significantly more detailed profile information than the conventional Abel inversion method currently used on JET.     

Optimized keyframe extraction for 3D character animations

In this paper, we propose a new method to automatically extract keyframes from animation sequences. Our method can be applied equally to both skeletal and mesh animations. It uses animation saliency computed on the original data to help select the group of keyframes that can reconstruct the input animation with less perception error. For computational efficiency, we perform nonlinear dimension reduction using locally linear embedding and then carry out the optimal search in much lower‐dimensional space. With this approach, reconstruction of the animation from the extracted keyframes shows much better results as compared with earlier approaches. Copyright © 2012 John Wiley & Sons, Ltd.     

用于动态序列合成的运动纹理模型

提出了一种新的用于动态序列合成的分层统计模型——运动纹理模型．该模型使用统计方法自动分析动态序列，可以合成与原始样本数据统计特性相同的新的动态序列．运动纹理模型是一个两层模型．其中包含的两层分别为基元层和基元分布层．模型中用线性动态系统来表示单个基元．而对基元的统计分布则由相关转移矩阵来描述．该文详细地讨论了如何通过最大似然准则来学习运动纹理模型的方法，并描述了如何用运动纹理模型自动合成动态序列的过程．该文通过使用运动纹理模型合成舞蹈动作和视频序列的一些实验验证了该模型的有效性．     

Detailed Rigid Body Simulation with Extended Position Based Dynamics

We present a rigid body simulation method that can resolve small temporal and spatial details by using a quasi explicit integration scheme that is unconditionally stable. Traditional rigid body simulators linearize constraints because they operate on the velocity level or solve the equations of motion implicitly thereby freezing the constraint directions for multiple iterations. Our method always works with the most recent constraint directions. This allows us to trace high speed motion of objects colliding against curved geometry, to reduce the number of constraints, to increase the robustness of the simulation, and to simplify the formulation of the solver. In this paper we provide all the details to implement a fully fledged rigid body solver that handles contacts, a variety of joint types and the interaction with soft objects.