Dagstuhl Workshop 2015: “Modeling and Simulation of Sport Games, Sport Movements, and Adaptations to Training”

September 13th, 2015 Irfan Essa Posted in Activity Recognition, Behavioral Imaging, Computer Vision, Human Factors, Modeling and Animation, Presentations No Comments »

Participated in the Dagstuhl Workshop on “Modeling and Simulation of Sport Games, Sport Movements, and Adaptations to Training” at the Dagstuhl Castle, September 13  – 16, 2015.


Computational modeling and simulation are essential to analyze human motion and interaction in sports science. Applications range from game analysis, issues in training science like training load-adaptation relationship, motor control & learning, to biomechanical analysis. The motivation of this seminar is to enable an interdisciplinary exchange between sports and computer scientists to advance modeling and simulation technologies in selected fields of applications: sport games, sport movements and adaptations to training. In addition, contributions to the epistemic basics of modeling and simulation are welcome.

Source: Schloss Dagstuhl : Seminar Homepage

Past Seminars on this topic include

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Kihwan Kim’s Thesis Defense (2011): “Spatio-temporal Data Interpolation for Dynamic Scene Analysis”

December 6th, 2011 Irfan Essa Posted in Computational Photography and Video, Kihwan Kim, Modeling and Animation, Multimedia, PhD, Security, Visual Surviellance, WWW No Comments »

Spatio-temporal Data Interpolation for Dynamic Scene Analysis

Kihwan Kim, PhD Candidate

School of Interactive Computing, College of Computing, Georgia Institute of Technology

Date: Tuesday, December 6, 2011

Time: 1:00 pm – 3:00 pm EST

Location: Technology Square Research Building (TSRB) Room 223


Analysis and visualization of dynamic scenes is often constrained by the amount of spatio-temporal information available from the environment. In most scenarios, we have to account for incomplete information and sparse motion data, requiring us to employ interpolation and approximation methods to fill for the missing information. Scattered data interpolation and approximation techniques have been widely used for solving the problem of completing surfaces and images with incomplete input data. We introduce approaches for such data interpolation and approximation from limited sensors, into the domain of analyzing and visualizing dynamic scenes. Data from dynamic scenes is subject to constraints due to the spatial layout of the scene and/or the configurations of video cameras in use. Such constraints include: (1) sparsely available cameras observing the scene, (2) limited field of view provided by the cameras in use, (3) incomplete motion at a specific moment, and (4) varying frame rates due to different exposures and resolutions.

In this thesis, we establish these forms of incompleteness in the scene, as spatio- temporal uncertainties, and propose solutions for resolving the uncertainties by applying scattered data approximation into a spatio-temporal domain.

The main contributions of this research are as follows: First, we provide an effi- cient framework to visualize large-scale dynamic scenes from distributed static videos. Second, we adopt Radial Basis Function (RBF) interpolation to the spatio-temporal domain to generate global motion tendency. The tendency, represented by a dense flow field, is used to optimally pan and tilt a video camera. Third, we propose a method to represent motion trajectories using stochastic vector fields. Gaussian Pro- cess Regression (GPR) is used to generate a dense vector field and the certainty of each vector in the field. The generated stochastic fields are used for recognizing motion patterns under varying frame-rate and incompleteness of the input videos. Fourth, we also show that the stochastic representation of vector field can also be used for modeling global tendency to detect the region of interests in dynamic scenes with camera motion. We evaluate and demonstrate our approaches in several applications for visualizing virtual cities, automating sports broadcasting, and recognizing traffic patterns in surveillance videos.


  • Prof. Irfan Essa (Advisor, School of Interactive Computing, Georgia Institute of Technology)
  • Prof. James M. Rehg (School of Interactive Computing, Georgia Institute of Technology)
  • Prof. Thad Starner (School of Interactive Computing, Georgia Institute of Technology)
  • Prof. Greg Turk (School of Interactive Computing, Georgia Institute of Technology)
  • Prof. Jessica K. Hodgins (Robotics Institute, Carnegie Mellon University, and Disney Research Pittsburgh)
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Paper ISMAR 2009: “Augmenting Aerial Earth Maps with Dynamic Information”

October 20th, 2009 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Kihwan Kim, Modeling and Animation, Papers No Comments »

Augmenting Aerial Earth Maps with Dynamic Information

  • K. Kim, S. Oh, J. Lee, and I. Essa (2009), “Augmenting Aerial Earth Maps with Dynamic Information,” in Proceedings of IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2009. [PDF] [WEBSITE] [VIDEO] [DOI] [BLOG] [BIBTEX]
    @InProceedings{    2009-Kim-AAEMWDI,
      author  = {K. Kim and S. Oh and J. Lee and I. Essa},
      blog    = {http://prof.irfanessa.com/2009/10/20/augearth-ismar2009/},
      booktitle  = {Proceedings of IEEE International Symposium on
          Mixed and Augmented Reality (ISMAR)},
      doi    = {10.1109/ISMAR.2009.5336505},
      month    = {October},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2009-Kim-AAEMWDI.pdf},
      title    = {Augmenting Aerial Earth Maps with Dynamic
      url    = {http://www.cc.gatech.edu/cpl/projects/augearth/},
      video    = {http://www.youtube.com/watch?v=TPk88soc2qw},
      year    = {2009}


We introduce methods for augmenting aerial visualizations of Earth (from tools such as Google Earth or Microsoft Virtual Earth) with dynamic information obtained from videos. Our goal is to make Augmented Earth Maps that visualize the live broadcast of dynamic sceneries within a city. We propose different approaches to analyze videos of pedestrians and cars, under differing conditions and then augment Aerial Earth Maps (AEMs) with live and dynamic information. We also analyze natural phenomenon (clouds) and project information from these to the AEMs to add the visual reality.

For Journal Version of this paper, please see http://prof.irfanessa.com/2011/02/02/vr-2011/

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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]


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.


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Paper (2009) In ACM Symposium on Interactive 3D Graphics “Human Video Textures”

March 1st, 2009 Irfan Essa Posted in ACM SIGGRAPH, Atsushi Nakazawa, Computational Photography and Video, James Rehg, Matt Flagg, Modeling and Animation, Papers, Sing Bing Kang No Comments »


Matthew FlaggAtsushi Nakazawa, Qiushuang Zhang, Sing Bing Kang, Young Kee Ryu, Irfan EssaJames 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 ]


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.

Example Image from Project

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