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Paper (2009) In IEEE Transactions on Visualization and CG “Fluid Simulation with Articulated Bodies”

June 10th, 2009 Irfan Essa Posted in Greg Turk, Modeling and Animation, Nipun Kwatra No Comments »

Nipun Kwatra, Chris Wojtan, Mark Carlson, Irfan A. Essa, Peter J. Mucha, Greg Turk (2009), “Fluid Simulation with Articulated Bodies“, IEEE Transactions on Visualization and Computer Graphics, 10 Jun. 2009. IEEE computer Society Digital Library. IEEE Computer Society. [DOI | PDF (see copyright) | Video | Website]

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.

teaser

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Paper: ACM SIGGRAPH (2005) “Texture optimization for example-based synthesis”

July 25th, 2005 Irfan Essa Posted in Aaron Bobick, ACM SIGGRAPH, Computational Photography and Video, Nipun Kwatra, Papers, Research, Vivek Kwatra No Comments »

Vivek Kwatra, Irfan Essa, Aaron Bobick, and Nipun Kwatra (2005), “Texture optimization for example-based synthesis” In ACM Transactions on Graphics (TOG) Volume 24 , Issue 3 (July 2005) Proceedings of ACM SIGGRAPH 2005, Pages: 795 – 802, ISSN:0730-0301 (DOI|PDF|Project Site|Video|Talk)

ABSTRACT

TextureOptimizationWe present a novel technique for texture synthesis using optimization. We define a Markov Random Field (MRF)-based similarity metric for measuring the quality of synthesized texture with respect to a given input sample. This allows us to formulate the synthesis problem as minimization of an energy function, which is optimized using an Expectation Maximization (EM)-like algorithm. In contrast to most example-based techniques that do region-growing, ours is a joint optimization approach that progressively refines the entire texture. Additionally, our approach is ideally suited to allow for controllable synthesis of textures. Specifically, we demonstrate controllability by animating image textures using flow fields. We allow for general two-dimensional flow fields that may dynamically change over time. Applications of this technique include dynamic texturing of fluid animations and texture-based flow visualization.

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