LeveragingtheTalentofHandAnimators toCreateThree-DimensionalAnimation

The skills required to create compelling three-dimensional animation using computer software are quite different from those required to create compelling hand animation with pencil and paper. The three-dimensional medium has several advantages over the traditional medium—it is easy to relight the scene, render it from different viewpoints, and add physical simulations. In this work, we propose a method to leverage the talent of traditionally trained hand animators to create three-dimensional animation of human motion, while allowing them to work in the medium that is familiar to them. The input to our algorithm is a set of hand-animated frames. Our key insight is to use motion capture data as a source of domain knowledge and ‘lift’ the two-dimensional animation to three dimensions, while maintaining the unique style of the input animation. A motion capture clip is projected to two dimensions. First, time alignment is done to match the timing of the hand-drawn frames and then, the limbs are aligned to better match the pose in the hand-drawn frames. Finally the motion is reconstructed in three dimensions. We demonstrate our algorithm on a variety of hand animated motion sequences on different characters, including ballet, a stylized sneaky walk, and a sequence of jumping jacks.

[1]  Christoph Bregler,et al.  Turning to the masters: motion capturing cartoons , 2002, ACM Trans. Graph..

[2]  Jessica K. Hodgins,et al.  Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces , 2004, SIGGRAPH 2004.

[3]  Michael J. Black,et al.  Learning the Statistics of People in Images and Video , 2003, International Journal of Computer Vision.

[4]  Takeo Kanade,et al.  Recovery of the Three-Dimensional Shape of an Object from a Single View , 1981, Artif. Intell..

[5]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[6]  David J. Fleet,et al.  Temporal motion models for monocular and multiview 3D human body tracking , 2006, Comput. Vis. Image Underst..

[7]  James M. Rehg,et al.  Reconstruction of 3-D Figure Motion from 2-D Correspondences , 2001, CVPR 2001.

[8]  Michael J. Black,et al.  Implicit Probabilistic Models of Human Motion for Synthesis and Tracking , 2002, ECCV.

[9]  Michael J. Black,et al.  Detailed Human Shape and Pose from Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Ankur Agarwal,et al.  Recovering 3D human pose from monocular images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Michael J. Black,et al.  Learning image statistics for Bayesian tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Harry Shum,et al.  Stylizing motion with drawings , 2003, SCA '03.

[13]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[14]  Cristian Sminchisescu,et al.  Estimating Articulated Human Motion with Covariance Scaled Sampling , 2003, Int. J. Robotics Res..

[15]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[16]  Ankur Agarwal,et al.  Learning to track 3D human motion from silhouettes , 2004, ICML.

[17]  F. Thomas,et al.  The illusion of life : Disney animation , 1981 .

[18]  Hans-Peter Seidel,et al.  Free-viewpoint video of human actors , 2003, ACM Trans. Graph..

[19]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[20]  Matthew Brand,et al.  Shadow puppetry , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  ten Josephus Berge,et al.  Review of: J.C. Gower & G.B. Dijksterhuis: Procrustes Problems, Oxford University Press. , 2004 .

[22]  Camillo J. Taylor,et al.  Reconstruction of articulated objects from point correspondences in a single uncalibrated image , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[23]  Michiel van de Panne,et al.  Motion doodles: an interface for sketching character motion , 2004, SIGGRAPH 2004.

[24]  Hsi-Jian Lee,et al.  Determination of 3D human body postures from a single view , 1985, Comput. Vis. Graph. Image Process..

[25]  David Salesin,et al.  A sketching interface for articulated figure animation , 2006, SIGGRAPH 2006.

[26]  Yaser Sheikh,et al.  Leveraging the talent of hand animators to create three-dimensional animation , 2009, SCA '09.

[27]  John Lasseter Tricks to animating characters with a computer , 2001, COMG.

[28]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[29]  Jitendra Malik,et al.  Tracking people with twists and exponential maps , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[30]  William T. Freeman,et al.  Bayesian Reconstruction of 3D Human Motion from Single-Camera Video , 1999, NIPS.