Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of linear manifolds spanned by a set of base configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.

Note: Joint work with John Lin

finger articulation (1.8M, mpg)	entire hand motion (2.8M, mpg)

1. Ying Wu, John Lin and Thomas S. Huang, "Analyzing and Capturing Articulated Hand Motion in Image Sequences", IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.27, No.12, pp.1910-1922, Dec., 2005.  [PDF]

 
2. John Lin, Ying Wu, and Thomas S. Huang, ``Articulate Hand Motion Capturing Based on a Monte Carlo Simplex Tracker", in Proc. 17th Int'l Conf. on Pattern Recognition (ICPR04), Cambridge, UK, 2004.  [PDF]

 
3. John Lin, Ying Wu, and Thomas S. Huang, "3D Model-Based Hand Tracking Using Stochastic Direct Search Method", in Proc. IEEE Int'l Conf. on Automatic Face and Gesture Recognition (FG04), Seoul, Korea, 2004.  [PDF]

 
4. Ying Wu, John Lin and Thomas S. Huang, "Capturing Natural Hand Articulation",  in Proc. IEE Int'l Conf. on Computer Vision (ICCV'2001), Vancouver, July, 2001.  [PDF]

 
5. Ying Wu and Thomas S. Huang, "Capturing Articulated Hand Motion: A Divide-and-Conquer Approach", In Proc. IEEE Int'l Conf. on Computer Vision (ICCV'99), pp.606-611, Greece, Sept., 1999.  [PDF]

 
6. John Lin, Ying Wu and Thomas S. Huang, "Modeling Human Hand Constraints", In Proc. Workshop on Human Motion, Austin, TX, Dec., 2000.  [PDF]

 
7. Ying Wu and Thomas S. Huang, "Human Hand Modeling, Analysis and Animation in the Context of HCI", In Proc. IEEE Int'l Conf. on Image Processing (ICIP'99), Kobe, Japan, 1999.   [PDF]