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Efficient Synthesis of Physically Valid Human Motion

Anthony C. Fang and Nancy S. Pollard

Abstract

Optimization is a promising way to generate new animations from a minimal amount of input data. Physically based optimization techniques, however, are difficult to scale to complex animated characters, in part because evaluating and differentiating physical quantities becomes prohibitively slow. Traditional approaches often require optimizing or constraining parameters involving joint torques; obtaining first derivatives for these parameters is generally an O(D2) process, where D is the number of degrees of freedom of the character. In this paper, we describe a set of objective functions and constraints that lead to linear time analytical first derivatives. The surprising finding is that this set includes constraints on physical validity, such as ground contact constraints. Considering only constraints and objective functions that lead to linear time first derivatives results in fast per-iteration computation times and an optimization problem that appears to scale well to more complex characters. We show that qualities such as squash-and-stretch that are expected from physically based optimization result from our approach. Our animation system is particularly useful for synthesizing highly dynamic motions, and we show examples of swinging and leaping motions for characters having from 7 to 22 degrees of freedom.

Citation

Anthony C. Fang and Nancy S. Pollard. Efficient synthesis of physically valid human motion. ACM Transactions on Graphics (SIGGRAPH 2003), 22(3):417–426, July 2003. [BiBTeX]

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Estimating Cloth Simulation Parameters from Video

Kiran S. Bhat, Christopher D. Twigg, Jessica K. Hodgins, Pradeep K. Khosla, Zoran Popović, and Steven M. Seitz

Abstract

Cloth simulations are notoriously difficult to tune due to the many parameters that must be adjusted to achieve the look of a particular fabric. In this paper, we present an algorithm for estimating the parameters of a cloth simulation from video data of real fabric. A perceptually motivated metric based on matching between folds is used to compare video of real cloth with simulation. This metric compares two video sequences of cloth and returns a number that measures the differences in their folds. Simulated annealing is used to minimize the frame by frame error between the metric for a given simulation and the real-world footage. To estimate all the cloth parameters, we identify simple static and dynamic calibration experiments that use small swatches of the fabric. To demonstrate the power of this approach, we use our algorithm to find the parameters for four different fabrics. We show the match between the video footage and simulated motion on the calibration experiments, on new video sequences for the swatches, and on a simulation of a full skirt.

Citation

Kiran S. Bhat, Christopher D. Twigg, Jessica K. Hodgins, Pradeep K. Khosla, Zoran Popović, and Steven M. Seitz. Estimating cloth simulation parameters from video. In 2003 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, pages 37–51, July 2003. [BiBTeX]

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Keyframe Control of Smoke Simulations

Adrien Treuille, Antoine McNamara, Zoran Popović, and Jos Stam

Abstract

We describe a method for controlling smoke simulations through user-specified keyframes. To achieve the desired behavior, a continuous quasi-Newton optimization solves for appropriate "wind" forces to be applied to the underlying velocity field throughout the simulation. The cornerstone of our approach is a method to efficiently compute exact derivatives through the steps of a fluid simulation. We formulate an objective function corresponding to how well a simulation matches the user's keyframes, and use the derivatives to solve for force parameters that minimize this function. For animations with several keyframes, we present a novel multiple-shooting approach. By splitting large problems into smaller overlapping subproblems, we greatly speed up the optimization process while avoiding certain local minima.

Citation

Adrien Treuille, Antoine McNamara, Zoran Popović, and Jos Stam. Keyframe control of smoke simulations. ACM Transactions on Graphics (SIGGRAPH 2003), 22(3):716–723, July 2003. [BiBTeX]

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Perceptual Metrics for Character Animation: Sensitivity to Errors in Ballistic Motion

Paul S. A. Reitsma and Nancy S. Pollard

Abstract

Motion capture data and techniques for blending, editing, and sequencing that data can produce rich, realistic character animation; however, the output of these motion processing techniques sometimes appears unnatural. For example, the motion may violate physical laws or reflect unreasonable forces from the character or the environment. While problems such as these can be fixed, doing so is not yet feasible in real time environments. We are interested in developing ways to estimate perceived error in animated human motion so that the output quality of motion processing techniques can be better controlled to meet user goals. This paper presents results of a study of user sensitivity to errors in animated human motion. Errors were systematically added to human jumping motion, and the ability of subjects to detect these errors was measured. We found that users were able to detect motion with errors, and noted some interesting trends: errors in horizontal velocity were easier to detect than errors in vertical velocity, and added accelerations were easier to detect than added decelerations. On the basis of our results, we propose a perceptually based metric for measuring errors in ballistic human motion.

Citation

Paul S. A. Reitsma and Nancy S. Pollard. Perceptual metrics for character animation: Sensitivity to errors in ballistic motion. ACM Transactions on Graphics (SIGGRAPH 2003), 22(3):537–542, July 2003. [BiBTeX]

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Precomputing Interactive Dynamic Deformable Scenes

Doug L. James and Kayvon Fatahalian

Abstract

We present an approach for precomputing data-driven models of interactive physically based deformable scenes. The method permits real-time hardware synthesis of nonlinear deformation dynamics, including self-contact and global illumination effects, and supports real-time user interaction. We use data-driven tabulation of the system's deterministic state space dynamics, and model reduction to build efficient low-rank parameterizations of the deformed shapes. To support runtime interaction, we also tabulate impulse response functions for a palette of external excitations. Although our approach simulates particular systems under very particular interaction conditions, it has several advantages. First, parameterizing all possible scene deformations enables us to precompute novel reduced coparameterizations of global scene illumination for low-frequency lighting conditions. Second, because the deformation dynamics are precomputed and parameterized as a whole, collisions are resolved within the scene during precomputation so that runtime self-collision handling is implicit. Optionally, the data-driven models can be synthesized on programmable graphics hardware, leaving only the low-dimensional state space dynamics and appearance data models to be computed by the main CPU.

Citation

Doug L. James and Kayvon Fatahalian. Precomputing interactive dynamic deformable scenes. ACM Transactions on Graphics (SIGGRAPH 2003), 22(3):879–887, July 2003. [BiBTeX]

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Vision-based Control of 3D Facial Animation

Jinxiang Chai, Jing Xiao, and Jessica K. Hodgins

Abstract

Controlling and animating the facial expression of a computer-generated 3D character is a difficult problem because the face has many degrees of freedom while most available input devices have few. In this paper, we show that a rich set of lifelike facial actions can be created from a preprocessed motion capture database and that a user can control these actions by acting out the desired motions in front of a video camera. We develop a real-time facial tracking system to extract a small set of animation control parameters from video. Because of the nature of video data, these parameters may be noisy, low-resolution, and contain errors. The system uses the knowledge embedded in motion capture data to translate these low-quality 2D animation control signals into high-quality 3D facial expressions. To adapt the synthesized motion to a new character model, we introduce an efficient expression retargeting technique whose run-time computation is constant independent of the complexity of the character model. We demonstrate the power of this approach through two users who control and animate a wide range of 3D facial expressions of different avatars.

Citation

Jinxiang Chai, Jing Xiao, and Jessica K. Hodgins. Vision-based control of 3d facial animation. In 2003 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, jul 2003. [BiBTeX]

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