Philippe Beaudoin

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We present a control strategy for physically-simulated walking motions that generalizes well across gait parameters, motion styles, character proportions, and a variety of skills. The control is realtime, requires no character-specific or motion-specific tuning, is robust to disturbances, and is simple to compute. The method works by integrating tracking,(More)
We present a new particle-based method for viscoelastic fluid simulation. We achieve realistic small-scale behavior of substances such as paint or mud as they splash on moving objects. Incompressibility and particle anti-clustering are enforced with a double density relaxation procedure which updates particle positions according to two opposing pressure(More)
We introduce a set of techniques that are used together to produce realistic-looking animations of burning objects. These include a new method for simulating spreading on polygonal meshes. A key component of our approach consists in using individual flames as primitives to animate and render the fire. This simplification enables rapid computation and gives(More)
Modeling the large space of possible human motions requires scalable techniques. Generalizing from example motions or example controllers is one way to provide the required scalability. We present techniques for generalizing a controller for physics-based walking to significantly different tasks, such as climbing a large step up, or pushing a heavy object.(More)
We present a method for precomputing robust task-based control policies for physically simulated characters. This allows for characters that can demonstrate skill and purpose in completing a given task, such as walking to a target location, while physically interacting with the environment in significant ways. As input, the method assumes an abstract action(More)
Simulated characters in simulated worlds require simulated skills. We develop control strategies that enable physically-simulated characters to dynamically navigate environments with significant stepping constraints, such as sequences of gaps. We present a synthesis-analysis-synthesis framework for this type of problem. First, an offline optimization method(More)
We present a technique to automatically distill a motion-motif graph from an arbitrary collection of motion capture data. Motion motifs represent clusters of similar motions and together with their encompassing motion graph they lend understandable structure to the contents and connectivity of large motion datasets. They can be used in support of motion(More)
Motion capture data is an effective way of synthesizing human motion for many interactive applications, including games and simulations. A compact, easy-to-decode representation is needed for the motion data in order to support the real-time motion of a large number of characters with minimal memory and minimal computational overheads. We present a(More)
Motion capture data often requires substantial processing before it becomes useful. We propose a technique that automatically distills a compact motion graph from an arbitrary collection of motion capture data. At its heart, the process identifies clusters of similar motions which we call “motion bundles”. Motion bundles and their encompassing motion graph(More)
Today’s hardware graphics accelerators incorporate techniques to antialias edges and minimize geometry-related sampling artifacts. Two such techniques, brute force supersampling and multisampling, increase the sampling rate by rasterizing the triangles in a larger antialiasing buffer that is then filtered down to the size of the framebuffer. The sampling(More)