Estimating Human Shape and Pose from a Single Image

1. Estimating Human Shape and Pose from a Single Image Peng Guan Alex Weiss Alexandru Balan Michael J. Black Brown University Department of Computer Science ICCV’ 2009

2. Body shape and pose from 1 image?

3. Introduction What we do • Estimating both 3D shape and pose in uncalibrated monocular imagery • Use additional monocular cues including smooth shading • Use GrabCut to produce foreground region • Make height variation concentrated along one shape basis vector, which allows “height constrained fitting” What others do • Estimating 3D human pose in uncalibrated monocular imagery • Use silhouette in multi-camera setting to recover 3D body shape • Most work assumes the existence of a known background to extract foreground silhouette • In previous body models, height is correlated with other shape variations

4. Previous Work 3D pose and shape estimation from multiple, calibrated, cameras Balan, A., Sigal, L., Black, M. J., Davis, J., Haussecker, H, “Detailed human shape and pose from images”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, Minneapolis, June 2007

5. SCAPE Body Model D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, and J. Davis. SCAPE: Shape completion and animation of people. SIGGRAPH, 24(3):408–416, 2005. Pose Training Set

6. Body shape/pose from 1 image: Problems • High dimensional body model (shape and pose) – initialization problem. • Background unknown • Single, monocular image • poorly constrained • Shape/Pose ambiguities • Silhouette insufficient

7. Solution 1: Pose Initialization Better Shape: initialized to mean body shape.

8. Solution 2: Segmentation C. Rother, V. Kolmogorov, and A. Blake. “GrabCut”: Interactive foreground extraction using iterated graph cuts. SIGGRAPH, 23(3):309–314, 2004.

9. Problem: Pose/Shape ambiguities Body shape and pose fitted to a single camera view

10. Solution 3: Height Preserving Shape Space

11. Shape space without height preserving

12. Problem: Silhouette not sufficient

13. Solution 4: Edge Cues

14. Problem: Shape not well constrained

15. Solution 5: Parametric Shape from Shading M. de la Gorce, N. Paragios and David Fleet. Model-Based Hand Tracking with Texture, Shading and Self-occlusions. IEEE Conference in Computer Vision and Pattern Recognition (CVPR), Anchorage 2008.

16. Shading/Overall Cost function Shading cost function: Overall cost function:

17. Experiment: Lab Images

18. Experiment: Lab Images

19. Quantitative Comparison

20. Experiment: Internet Images

21. Experiment: Paintings

23. Conclusions Contributions • Solution to a new problem: Human pose and shape estimation from a single image • Parametric shape from shading for estimating human shape from complex images and paintings • Attribute-constrained body model Limitations • Single point light assumption and simplified model of surface reflection • User assistance for pose initialization • Minimal clothing for shading

24. Acknowledgement • Financial support: NSF IIS-0812364 and the RI Economic Development Corp. • Peng Guan, Alexander Weiss, Alexandru Balan, Michael Black, “Estimating Human Shape and Pose from a Single Image”, Int. Conf. on Computer Vision, ICCV, Kyoto, Japan, Sept. 2009 • Alexander Weiss: GrabCut 3D pose initialization • Alexandru Balan: Height preserving shape space • David Hirshberg: Projection of model edge

25. Thank you!