Technical Papers
Data-Driven Animation
Tuesday, 23 July 3:45 PM - 5:35 PM
Session Chair: Jinxiang Chai, Texas A&M University
Tuesday, 23 July 3:45 PM - 5:35 PM
Session Chair: Jinxiang Chai, Texas A&M University
This paper presents an efficient extension of Galerkin projection to a large class of non-polynomial functions and demonstrates the broad applicability of the approach by applying it to two strikingly different problems: fluid simulation and radiosity rendering.
Matt Stanton
Carnegie Mellon University
Yu Sheng
Carnegie Mellon University
Martin Wicke
Otherlab
Federico Perazzi
Carnegie Mellon University
Amos Yuen
Carnegie Mellon University
Srinivasa Narasimhan
Carnegie Mellon University
Adrien Treuille
Carnegie Mellon University
Using over several thousand hours to perform a massive exploration of the space of secondary clothing effects on a character animated through a large motion graph, this method successfully samples the complex dynamical space to low visual error and compresses the resulting dataset to enable real-time animation.
Doyub Kim
Carnegie Mellon University
Woojong Koh
University of California, Berkeley
Rahul Narain
University of California, Berkeley
Kayvon Fatahalian
Carnegie Mellon University
Adrien Treuille
Carnegie Mellon University
James O'Brien
University of California, Berkeley
Data-driven models to improve the simulation of cloth-body interactions from three perspectives: collision, friction, and air pressure. The resulting system allows efficient and accurate simulation of deformable objects with a three-layer structure such as pillows, comforters, down jackets, and stuffed toys.
Zhili Chen
The Ohio State University
Huamin Wang
The Ohio State University
Renguo Feng
The Ohio State University
A novel algorithm to reconstruct and display continuous virtual traffic flows based on discrete spatio-temporal traffic-sensor data for a dynamic virtual environment.
David Wilkie
University of North Carolina at Chapel Hill
Jason Sewall
Intel Corporation
Ming Lin
University of North Carolina at Chapel Hill
Dynamic-element textures describe phenomena that are repetitive in both shapes and motions, such as particles, threads, and sheets. They are often hard to control during authoring. This method facilitates controls at coarse scales through spatial-temporal constraints and fine scales through input exemplars. It combines constrained optimization with data-driven computation.
Chongyang Ma
The University of British Columbia, Tsinghua University
Li-Yi Wei
The University of Hong Kong, Microsoft Research
Sylvain Lefebvre
INRIA
Xin Tong
Microsoft Research Asia