Abstract

We present an algorithm for creating realistic animations of characters that are swimming through fluids. Our approach combines dynamic simulation with data-driven kinematic motions (motion capture data) to produce realistic animation in a fluid. The interaction of the articulated body with the fluid is performed by incorporating joint constraints with rigid animation and by extending a solid/fluid coupling method to handle articulated chains. Our solver takes as input the current state of the simulation and calculates the angular and linear accelerations of the connected bodies needed to match a particular motion sequence for the articulated body. These accelerations are used to estimate the forces and torques that are then applied to each joint. Based on this approach, we demonstrate simulated swimming results for a variety of different strokes, including crawl, backstroke, breaststroke and butterfly. The ability to have articulated bodies interact with fluids also allows us to generate simulations of simple water creatures that are driven by simple controllers.

]]> http://prof.irfanessa.com/2009/06/10/swimmer/feed/ 0 http://prof.irfanessa.com/2009/03/01/paper-2009-acm-symposium-on-interactive-human-video-textures/ http://prof.irfanessa.com/2009/03/01/paper-2009-acm-symposium-on-interactive-human-video-textures/#comments Sun, 01 Mar 2009 19:43:45 +0000 Irfan Essa http://academics.irfanessa.com/?p=473

Matthew Flagg, Atsushi Nakazawa, Qiushuang Zhang, Sing Bing Kang, Young Kee Ryu, Irfan Essa, James M. Rehg (2009), Human Video Textures In Proceedings of the ACM Symposium on Interactive 3D Graphics and Games 2009 (I3D ’09), Boston, MA, February 27-March 1 (Fri-Sun), 2009 [PDF (see Copyright) | Video in DiVx | Website ]

Abstract

This paper describes a data-driven approach for generating photorealistic animations of human motion. Each animation sequence follows a user-choreographed path and plays continuously by seamlessly transitioning between different segments of the captured data. To produce these animations, we capitalize on the complementary characteristics of motion capture data and video. We customize our capture system to record motion capture data that are synchronized with our video source. Candidate transition points in video clips are identified using a new similarity metric based on 3-D marker trajectories and their 2-D projections into video. Once the transitions have been identified, a video-based motion graph is constructed. We further exploit hybrid motion and video data to ensure that the transitions are seamless when generating animations. Motion capture marker projections serve as control points for segmentation of layers and nonrigid transformation of regions. This allows warping and blending to generate seamless in-between frames for animation. We show a series of choreographed animations of walks and martial arts scenes as validation of our approach.

Human Video Textures (Output Rendered as a Collage!)