Locomotion Skills for Insects with Sample‐based Controller

Natural‐looking insect animation is very difficult to simulate. The fast movement and small scale of insects often challenge the standard motion capture techniques. As for the manual key‐framing or physics‐driven methods, significant amounts of time and efforts are necessary due to the delicate structure of the insect, which prevents practical applications. In this paper, we address this challenge by presenting a two‐level control framework to efficiently automate the modeling and authoring of insects’ locomotion. On the top level, we design a Triangle Placement Engine to automatically determine the location and orientation of insects’ foot contacts, given the user‐defined trajectory and settings, including speed, load, path and terrain etc. On the low‐level, we relate the Central Pattern Generator to the triangle profiles with the assistance of a Controller Look‐Up Table to fast simulate the physically‐based movement of insects. With our approach, animators can directly author insects’ behavior among a wide range of locomotion repertoire, including walking along a specified path or on an uneven terrain, dynamically adjusting to external perturbations and collectively transporting prey back to the nest.     

Improving Sampling‐based Motion Control

We address several limitations of the sampling‐based motion control method of Liu et at. [ LYvdP* 10 ]. The key insight is to learn from the past control reconstruction trials through sample distribution adaptation. Coupled with a sliding window scheme for better performance and an averaging method for noise reduction, the improved algorithm can efficiently construct open‐loop controls for long and challenging reference motions in good quality. Our ideas are intuitive and the implementations are simple. We compare the improved algorithm with the original algorithm both qualitatively and quantitatively, and demonstrate the effectiveness of the improved algorithm with a variety of motions ranging from stylized walking and dancing to gymnastic and Martial Arts routines.     

Multi‐layer Lattice Model for Real‐Time Dynamic Character Deformation

Due to the recent advancement of computer graphics hardware and software algorithms, deformable characters have become more and more popular in real‐time applications such as computer games. While there are mature techniques to generate primary deformation from skeletal movement, simulating realistic and stable secondary deformation such as jiggling of fats remains challenging. On one hand, traditional volumetric approaches such as the finite element method require higher computational cost and are infeasible for limited hardware such as game consoles. On the other hand, while shape matching based simulations can produce plausible deformation in real‐time, they suffer from a stiffness problem in which particles either show unrealistic deformation due to high gains, or cannot catch up with the body movement. In this paper, we propose a unified multi‐layer lattice model to simulate the primary and secondary deformation of skeleton‐driven characters. The core idea is to voxelize the input character mesh into multiple anatomical layers including the bone, muscle, fat and skin. Primary deformation is applied on the bone voxels with lattice‐based skinning. The movement of these voxels is propagated to other voxel layers using lattice shape matching simulation, creating a natural secondary deformation. Our multi‐layer lattice framework can produce simulation quality comparable to those from other volumetric approaches with a significantly smaller computational cost. It is best to be applied in real‐time applications such as console games or interactive animation creation.     

A Data‐Driven Framework for Visual Crowd Analysis

We present a novel approach for analyzing the quality of multi‐agent crowd simulation algorithms. Our approach is data‐driven, taking as input a set of user‐defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real‐time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely‐used methods, including a simulation from a commercial game.     

A Dimension‐reduced Pressure Solver for Liquid Simulations

This work presents a method for efficiently simplifying the pressure projection step in a liquid simulation. We first devise a straightforward dimension reduction technique that dramatically reduces the cost of solving the pressure projection. Next, we introduce a novel change of basis that satisfies free‐surface boundary conditions exactly, regardless of the accuracy of the pressure solve. When combined, these ideas greatly reduce the computational complexity of the pressure solve without compromising free surface boundary conditions at the highest level of detail. Our techniques are easy to parallelize, and they effectively eliminate the computational bottleneck for large liquid simulations.     

Environment‐Adaptive Contact Poses for Virtual Characters

We present a novel method to generate a virtual character's multi‐contact poses adaptive to the various shapes of the environment. Given the user‐specified center of mass (CoM) position and direction as inputs, our method finds the potential contacts for the character in the surrounding geometry of the environment and generates a set of stable poses that are contact‐rich. Major contributions of the work are in efficiently finding admissible support points for the target environment by precomputing candidate support points from a human pose database, and in automatically generating interactive poses that can maintain stable equilibrium. We develop the concept of support complexity to scale the set of precomputed support points by the geometric complexity of the environment. We demonstrate the effectiveness of our method by creating contact poses for various test cases of environments.     

Simulation‐Ready Hair Capture

Physical simulation has long been the approach of choice for generating realistic hair animations in CG. A constant drawback of simulation, however, is the necessity to manually set the physical parameters of the simulation model in order to get the desired dynamic behavior. To alleviate this, researchers have begun to explore methods for reconstructing hair from the real world and even to estimate the corresponding simulation parameters through the process of inversion. So far, however, these methods have had limited applicability, because dynamic hair capture can only be played back without the ability to edit, and solving for simulation parameters can only be accomplished for static hairstyles, ignoring the dynamic behavior. We present the first method for capturing dynamic hair and automatically determining the physical properties for simulating the observed hairstyle in motion. Since our dynamic inversion is agnostic to the simulation model, the proposed method applies to virtually any hair simulation technique, which we demonstrate using two state‐of‐the‐art hair simulation models. The output of our method is a fully simulation‐ready hairstyle, consisting of both the static hair geometry as well as its physical properties. The hairstyle can be easily edited by adding additional external forces, changing the head motion, or re‐simulating in completely different environments, all while remaining faithful to the captured hairstyle.     

A Practical Method for High‐Resolution Embedded Liquid Surfaces

Combining high‐resolution level set surface tracking with lower resolution physics is an inexpensive method for achieving highly detailed liquid animations. Unfortunately, the inherent resolution mismatch introduces several types of disturbing visual artifacts. We identify the primary sources of these artifacts and present simple, efficient, and practical solutions to address them. First, we propose an unconditionally stable filtering method that selectively removes sub‐grid surface artifacts not seen by the fluid physics, while preserving fine detail in dynamic splashing regions. It provides comparable results to recent error‐correction techniques at lower cost, without substepping, and with better scaling behavior. Second, we show how a modified narrow‐band scheme can ensure accurate free surface boundary conditions in the presence of large resolution mismatches. Our scheme preserves the efficiency of the narrow‐band methodology, while eliminating objectionable stairstep artifacts observed in prior work. Third, we demonstrate that the use of linear interpolation of velocity during advection of the high‐resolution level set surface is responsible for visible grid‐aligned kinks; we therefore advocate higher‐order velocity interpolation, and show that it dramatically reduces this artifact. While these three contributions are orthogonal, our results demonstrate that taken together they efficiently address the dominant sources of visual artifacts arising with high‐resolution embedded liquid surfaces; the proposed approach offers improved visual quality, a straightforward implementation, and substantially greater scalability than competing methods.     

Modeling and Estimation of Energy‐Based Hyperelastic Objects

In this paper, we present a method to model hyperelasticity that is well suited for representing the nonlinearity of real‐world objects, as well as for estimating it from deformation examples. Previous approaches suffer several limitations, such as lack of integrability of elastic forces, failure to enforce energy convexity, lack of robustness of parameter estimation, or difficulty to model cross‐modal effects. Our method avoids these problems by relying on a general energy‐based definition of elastic properties. The accuracy of the resulting elastic model is maximized by defining an additive model of separable energy terms, which allow progressive parameter estimation. In addition, our method supports efficient modeling of extreme nonlinearities thanks to energy‐limiting constraints. We combine our energy‐based model with an optimization method to estimate model parameters from force‐deformation examples, and we show successful modeling of diverse deformable objects, including cloth, human finger skin, and internal human anatomy in a medical imaging application.     

On‐line real‐time physics‐based predictive motion control with balance recovery

In this paper, we present an on‐line real‐time physics‐based approach to motion control with contact repositioning based on a low‐dimensional dynamics model using example motion data. Our approach first generates a reference motion in run time according to an on‐line user request by transforming an example motion extracted from a motion library. Guided by the reference motion, it repeatedly generates an optimal control policy for a small time window one at a time for a sequence of partially overlapping windows, each covering a couple of footsteps of the reference motion, which supports an on‐line performance. On top of this, our system dynamics and problem formulation allow to derive closed‐form derivative functions by exploiting the low‐dimensional dynamics model together with example motion data. These derivative functions and their sparse structures facilitate a real‐time performance. Our approach also allows contact foot repositioning so as to robustly respond to an external perturbation or an environmental change as well as to perform locomotion tasks such as stepping on stones effectively.     

Performance‐Based Biped Control using a Consumer Depth Camera

We present a technique for controlling physically simulated characters using user inputs from an off‐the‐shelf depth camera. Our controller takes a real‐time stream of user poses as input, and simulates a stream of target poses of a biped based on it. The simulated biped mimics the user's actions while moving forward at a modest speed and maintaining balance. The controller is parameterized over a set of modulated reference motions that aims to cover the range of possible user actions. For real‐time simulation, the best set of control parameters for the current input pose is chosen from the parameterized sets of pre‐computed control parameters via a regression method. By applying the chosen parameters at each moment, the simulated biped can imitate a range of user actions while walking in various interactive scenarios.     

Data‐guided Model Predictive Control Based on Smoothed Contact Dynamics

In this paper, we propose an efficient data‐guided method based on Model Predictive Control (MPC) to synthesize a full‐body motion. Guided by a reference motion, our method repeatedly plans the full‐body motion to produce an optimal control policy for predictive control while sliding the fixed‐span window along the time axis. Based on this policy, the method computes the joint torques of a character at every time step. Together with contact forces and external perturbations if there are any, the joint torques are used to update the state of the character. Without including the contact forces in the control vector, our formulation of the trajectory optimization problem enables automatic adjustment of contact timings and positions for balancing in response to environmental changes and external perturbations. For efficiency, we adopt derivative‐based trajectory optimization on top of state‐of‐the‐art smoothed contact dynamics. Use of derivatives enables our method to run much faster than the existing sampling‐based methods. In order to further accelerate the performance of MPC, we propose efficient numerical differentiation of the system dynamics of a full‐body character based on two schemes: data reuse and data interpolation. The former scheme exploits data dependency to reuse physical quantities of the system dynamics at near‐by time points. The latter scheme allows the use of derivatives at sparse sample points to interpolate those at other time points in the window. We further accelerate evaluation of the system dynamics by exploiting the sparsity of physical quantities such as Jacobian matrix resulting from the tree‐like structure of the articulated body. Through experiments, we show that the proposed method efficiently can synthesize realistic motions such as locomotion, dancing, gymnastic motions, and martial arts at interactive rates using moderate computing resources.     

IISPH‐FLIP for incompressible fluids

We propose to use Implicit Incompressible Smoothed Particle Hydrodynamics (IISPH) for pressure projection and boundary handling in Fluid‐Implicit‐Particle (FLIP) solvers for the simulation of incompressible fluids. This novel combination addresses two issues of existing SPH and FLIP solvers, namely mass preservation in FLIP and efficiency and memory consumption in SPH. First, the SPH component enables the simulation of incompressible fluids with perfect mass preservation. Second, the FLIP component efficiently enriches the SPH component with detail that is comparable to a standard SPH simulation with the same number of particles, while improving the performance by a factor of 7 and significantly reducing the memory consumption. We demonstrate that the proposed IISPH‐FLIP solver can simulate incompressible fluids with a quantifiable, imperceptible density deviation below 0.1%. We show large‐scale scenarios with up to 160 million particles that have been processed on a single desktop PC using only 15GB of memory. One‐ and two‐way coupled solids are illustrated.     

Sparse high‐degree polynomials for wide‐angle lenses

Rendering with accurate camera models greatly increases realism and improves the match of synthetic imagery to real‐life footage. Photographic lenses can be simulated by ray tracing, but the performance depends on the complexity of the lens system, and some operations required for modern algorithms, such as deterministic connections, can be difficult to achieve. We generalise the approach of polynomial optics, i.e. expressing the light field transformation from the sensor to the outer pupil using a polynomial, to work with extreme wide angle (fisheye) lenses and aspherical elements. We also show how sparse polynomials can be constructed from the large space of high‐degree terms (we tested up to degree 15). We achieve this using a variant of orthogonal matching pursuit instead of a Taylor series when computing the polynomials. We show two applications: photorealistic rendering using Monte Carlo methods, where we introduce a new aperture sampling technique that is suitable for light tracing, and an interactive preview method suitable for rendering with deep images.     

Rich‐VPLs for Improving the Versatility of Many‐Light Methods

Many‐light methods approximate the light transport in a scene by computing the direct illumination from many virtual point light sources (VPLs), and render low‐noise images covering a wide range of performance and quality goals. However, they are very inefficient at representing glossy light transport. This is because a VPL on a glossy surface illuminates a small fraction of the scene only, and a tremendous number of VPLs might be necessary to render acceptable images. In this paper, we introduce Rich‐VPLs which, in contrast to standard VPLs, represent a multitude of light paths and thus have a more widespread emission profile on glossy surfaces and in scenes with multiple primary light sources. By this, a single Rich‐VPL contributes to larger portions of a scene with negligible additional shading cost. Our second contribution is a placement strategy for (Rich‐)VPLs proportional to sensor importance times radiance. Although both Rich‐VPLs and improved placement can be used individually, they complement each other ideally and share interim computation. Furthermore, both complement existing many‐light methods, e.g. Lightcuts or the Virtual Spherical Lights method, and can improve their efficiency as well as their application for scenes with glossy materials and many primary light sources.     

Narrow Band FLIP for Liquid Simulations

The Fluid Implicit Particle method (FLIP) for liquid simulations uses particles to reduce numerical dissipation and provide important visual cues for events like complex splashes and small‐scale features near the liquid surface. Unfortunately, FLIP simulations can be computationally expensive, because they require a dense sampling of particles to fill the entire liquid volume. Furthermore, the vast majority of these FLIP particles contribute nothing to the fluid's visual appearance, especially for larger volumes of liquid. We present a method that only uses FLIP particles within a narrow band of the liquid surface, while efficiently representing the remaining inner volume on a regular grid. We show that a naïve realization of this idea introduces unstable and uncontrollable energy fluctuations, and we propose a novel coupling scheme between FLIP particles and regular grid which overcomes this problem. Our method drastically reduces the particle count and simulation times while yielding results that are nearly indistinguishable from regular FLIP simulations. Our approach is easy to integrate into any existing FLIP implementation.     

Portal‐Masked Environment Map Sampling

We present a technique to efficiently importance sample distant, all‐frequency illumination in indoor scenes. Standard environment sampling is inefficient in such cases since the distant lighting is typically only visible through small openings (e.g. windows). This visibility is often addressed by manually placing a portal around each window to direct samples towards the openings; however, uniformly sampling the portal (its area or solid angle) disregards the possibly high frequency environment map. We propose a new portal importance sampling technique which takes into account both the environment map and its visibility through the portal, drawing samples proportional to the product of the two. To make this practical, we propose a novel, portal‐rectified reparametrization of the environment map with the key property that the visible region induced by a rectangular portal projects to an axis‐aligned rectangle. This allows us to sample according to the desired product distribution at an arbitrary shading location using a single (precomputed) summed‐area table per portal. Our technique is unbiased, relevant to many renderers, and can also be applied to rectangular light sources with directional emission profiles, enabling efficient rendering of non‐diffuse light sources with soft shadows.     

Gradient‐based steering for vision‐based crowd simulation algorithms

Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic simulation of the way humans navigate according to their perception of the surrounding environment. In this paper, we propose a new perception/motion loop to steering agents along collision free trajectories that significantly improves the quality of vision‐based crowd simulators. In contrast with solutions where agents avoid collisions in a purely reactive (binary) way, we suggest exploring the full range of possible adaptations and retaining the locally optimal one. To this end, we introduce a cost function, based on perceptual variables, which estimates an agent's situation considering both the risks of future collision and a desired destination. We then compute the partial derivatives of that function with respect to all possible motion adaptations. The agent then adapts its motion by following the gradient. This paper has thus two main contributions: the definition of a general purpose control scheme for steering synthetic vision‐based agents; and the proposition of cost functions for evaluating the perceived danger of the current situation. We demonstrate improvements in several cases.