Abstract
We propose a method that extracts sparse and spatially localized deformation modes from an animated mesh sequence. To this end, we propose a new way to extend the theory of sparse matrix decompositions to 3D mesh sequence processing, and further contribute with an automatic way to ensure spatial locality of the decomposition in a new optimization framework. The extracted dimensions often have an intuitive and clear interpretable meaning. Our method optionally accepts user-constraints to guide the process of discovering the underlying latent deformation space. The capabilities of our efficient, versatile, and easy-to-implement method are extensively demonstrated on a variety of data sets and application contexts. We demonstrate its power for user friendly intuitive editing of captured mesh animations, such as faces, full body motion, cloth animations, and muscle deformations. We further show its benefit for statistical geometry processing and biomechanically meaningful animation editing. It is further shown qualitatively and quantitatively that our method outperforms other unsupervised decomposition methods and other animation parameterization approaches in the above use cases.
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- Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., and Davis, J. 2005. SCAPE: shape completion and animation of people. ACM Trans. Graph. (Proc. SIGGRAPH) 24, 3. Google ScholarDigital Library
- Bach, F. R., Jenatton, R., Mairal, J., and Obozinski, G. 2012. Optimization with sparsity-inducing penalties. Found. Trends Mach. Learn. 4, 1. Google ScholarDigital Library
- Beeler, T., Hahn, F., Bradley, D., Bickel, B., Beardsley, P., Gotsman, C., Sumner, R. W., and Gross, M. 2011. High-quality passive facial performance capture using anchor frames. ACM Trans. Graph. (Proc. SIGGRAPH) 30. Google ScholarDigital Library
- Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1. Google ScholarDigital Library
- Cao, Y., Shapiro, A., Faloutsos, P., and Pighin, F. 2007. Motion editing with independent component analysis. Visual Computer.Google Scholar
- Cashman, T. J., and Hormann, K. 2012. A continuous, editable representation for deforming mesh sequences with separate signals for time, pose and shape. Comp. Graph. Forum (Proc. EG) 31, 2. Google ScholarDigital Library
- Crane, K., Weischedel, C., and Wardetzky, M. 2013. Geodesics in heat: A new approach to computing distance based on heat flow. ACM Trans. Graph., to appear. Google ScholarDigital Library
- de Aguiar, E., Theobalt, C., Thrun, S., and Seidel, H.-P. 2008. Automatic conversion of mesh animations into skeleton-based animations. Comp. Graph. Forum (Proc. EG) 27, 2.Google ScholarCross Ref
- de Aguiar, E., Sigal, L., Treuille, A., and Hodgins, J. K. 2009. Stable spaces for real-time clothing. ACM Trans. Graph. (Proc. SIGGRAPH) 29, 3. Google ScholarDigital Library
- Deng, B., Bouaziz, S., Deuss, M., Zhang, J., Schwartzburg, Y., and Pauly, M. 2013. Exploring Local Modifications for Constrained Meshes. Comp. Graph. Forum (Proc. EG) 32, 2.Google Scholar
- Feng, W.-W., Kim, B.-U., and Yu, Y. 2008. Real-time data driven deformation using kernel canonical correlation analysis. ACM Trans. Graph. (Proc. SIGGRAPH) 27, 3. Google ScholarDigital Library
- Fröhlich, S., and Botsch, M. 2011. Example-driven deformations based on discrete shells. Comp. Graph. Forum 30, 8.Google ScholarCross Ref
- Guan, P., Reiss, L., Hirshberg, D., Weiss, A., and Black, M. J. 2012. DRAPE: DRessing Any PErson. ACM Trans. on Graphics (Proc. SIGGRAPH) 31, 4. Google ScholarDigital Library
- Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., and Seidel, H.-P. 2009. A statistical model of human pose and body shape. Comp. Graph. Forum (Proc. EG) 2, 28.Google Scholar
- Hasler, N., Thormählen, T., Rosenhahn, B., and Seidel, H.-P. 2010. Learning skeletons for shape and pose. In Proc. of I3D. Google ScholarDigital Library
- Havaldar, P. 2006. Performance driven facial animation. In ACM SIGGRAPH 2006 Course 30 Notes.Google Scholar
- Hyvärinen, A., Karhunen, J., and Oja, E. 2001. Independent component analysis. John Wiley & Sons.Google Scholar
- Jenatton, R. 2011. Structured Sparsity-Inducing Norms: Statistical and Algorithmic Properties with Applications to Neuroimaging. PhD thesis, École Normale Supérieure Cachan.Google Scholar
- Jolliffe, I. T., Trendafilov, N. T., and Uddin, M. 2003. A modified principal component technique based on the LASSO. J. Comp. Graph. Stat. 12, 3.Google ScholarCross Ref
- Kavan, L., Sloan, P.-P., and O'Sullivan, C. 2010. Fast and efficient skinning of animated meshes. Comp. Graph. Forum (Proc. EG) 29, 2.Google ScholarCross Ref
- Kavan, L., Gerszewski, D., Bargteil, A., and Sloan, P.- P. 2011. Physics-inspired upsampling for cloth simulation in games. ACM Trans. Graph. 30, 4. Google ScholarDigital Library
- Kim, D., Koh, W., Narain, R., Fatahalian, K., Treuille, A., and O'Brien, J. F. 2013. Near-exhaustive precomputation of secondary cloth effects. ACM Trans. Graph. (Proc. SIGGRAPH) 32, 4. Google ScholarDigital Library
- Kircher, S., and Garland, M. 2009. Free-form motion processing. ACM Trans. Graph. 27, 2. Google ScholarDigital Library
- Kry, P. G., James, D. L., and Pai, D. K. 2000. EigenSkin: Real time large deformation character skinning in hardware. In Proc. SCA. Google ScholarDigital Library
- Lau, M., Chai, J., Xu, Y.-Q., and Shum, H.-Y. 2009. Face poser: Interactive modeling of 3D facial expressions using facial priors. ACM Trans. Graph. 29, 1. Google ScholarDigital Library
- Le, B., and Deng, Z. 2012. Smooth skinning decomposition with rigid bones. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 31, 6. Google ScholarDigital Library
- Lee, D. D., and Seung, H. S. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401.Google Scholar
- Levine, S., Wang, J. M., Haraux, A., Popovíc, Z., and Koltun, V. 2012. Continuous character control with low-dimensional embeddings. ACM Trans. Graph. (Proc. SIGGRAPH) 31, 4. Google ScholarDigital Library
- Lewis, J. P., and Anjyo, K. 2010. Direct-manipulation blendshapes. IEEE CGAA 30, 4. Google ScholarDigital Library
- Li, H., Weise, T., and Pauly, M. 2010. Example-based facial rigging. ACM Trans. Graph. (Proc. SIGGRAPH) 29, 4. Google ScholarDigital Library
- Mackey, L. 2009. Deflation methods for sparse pca. In Adv. NIPS.Google Scholar
- Mairal, J., Bach, F., Ponce, J., and Sapiro, G. 2009. Online dictionary learning for sparse coding. In Proc. ICML. Google ScholarDigital Library
- Meyer, M., and Anderson, J. 2007. Key point subspace acceleration and soft caching. ACM Trans. Graph. (Proc. SIGGRAPH)26, 3. Google ScholarDigital Library
- Miguel, E., Bradley, D., Thomaszewski, B., Bickel, B., Matusik, W., Otaduy, M. A., and Marschner, S. 2012. Data-driven estimation of cloth simulation models. Comp. Graph. Forum (Proc. EG) 31, 2. Google ScholarDigital Library
- Mohr, A., and Gleicher, M. 2003. Building efficient, accurate character skins from examples. ACM Trans. Graph. (Proc. SIGGRAPH). Google ScholarDigital Library
- Neumann, T., Varanasi, K., Hasler, N., Wacker, M., Magnor, M., and Theobalt, C. 2013. Capture and Statistical Modeling of Arm-Muscle Deformations. Comp. Graph. Forum (Proc. EG) 32, 2.Google ScholarCross Ref
- Olshausen, B., and Field, D. J. 1997. Sparse coding with an overcomplete basis set: a strategy employed by v1? Vision Research 37, 23.Google ScholarCross Ref
- Osipa, J. 2003. Stop Staring: Facial modeling and animation done right, second ed. Sybex. Google ScholarDigital Library
- Pokrass, J., Bronstein, A. M., Bronstein, M. M., Sprechmann, P., and Sapiro, G. 2013. Sparse Modeling of Intrinsic Correspondences. Comp. Graph. Forum (Proc. EG) 32, 2.Google Scholar
- Schumacher, C., Thomaszewski, B., Coros, S., Martin, S., Sumner, R., and Gross, M. 2012. Efficient simulation of example-based materials. In Proc. SCA. Google ScholarDigital Library
- Seo, J., Irving, G., Lewis, J. P., and Noh, J. 2011. Compression and direct manipulation of complex blendshape models. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 30, 6. Google ScholarDigital Library
- Sifakis, E., Neverov, I., and Fedkiw, R. 2005. Automatic determination of facial muscle activations from sparse motion capture marker data. ACM Trans. Graph. (Proc. SIGGRAPH) 24, 3. Google ScholarDigital Library
- Sumner, R. W., Schmid, J., and Pauly, M. 2007. Embedded deformation for shape manipulation. ACM Trans. Graph. (Proc. SIGGRAPH) 26, 3. Google ScholarDigital Library
- Tena, J. R., De la Torre, F., and Matthews, I. 2011. Interactive region-based linear 3D face models. ACM Trans. Graph. (Proc. SIGGRAPH) 30, 4. Google ScholarDigital Library
- Theobalt, C., de Aguiar, E., Stoll, C., Seidel, H.-P., and Thrun, S. 2010. Image and geometry processing for 3D Cinematography. Springer, ch. Performance capture from multi-view video.Google Scholar
- Tournier, M., and Reveret, L. 2012. Principal Geodesic Dynamics. In Proc. SCA. Google ScholarDigital Library
- Valgaerts, L., Wu, C., Bruhn, A., Seidel, H.-P., and Theobalt, C. 2012. Lightweight binocular facial performance capture under uncontrolled lighting. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 31, 6. Google ScholarDigital Library
- Vlasic, D., Brand, M., Pfister, H., and Popovíc, J. 2005. Face transfer with multilinear models. ACM Trans. Graph. (Proc. SIGGRAPH) 24, 3. Google ScholarDigital Library
- Weber, O., Sorkine, O., Lipman, Y., and Gotsman, C. 2007. Context-aware skeletal shape deformation. Comp. Graph. Forum (Proc. EG) 26, 3.Google ScholarCross Ref
- Wright, S., Nowak, R., and Figueiredo, M. 2009. Sparse reconstruction by separable approximation. Signal Processing, IEEE Transactions on 57, 7. Google ScholarDigital Library
- Wu, C., Varanasi, K., Liu, Y., Seidel, H.-P., and Theobalt, C. 2011. Shading-based dynamic shape refinement from multi-view video under general illumination. In Proc. ICCV. Google ScholarDigital Library
- Zhang, L., Snavely, N., Curless, B., and Seitz, S. 2004. Spacetime faces: High-resolution capture for modeling and animation. ACM Trans. Graph. (Proc. SIGGRAPH). Google ScholarDigital Library
- Zou, H., Hastie, T., and Tibshirani, R. 2006. Sparse principal component analysis. J. Comp. Graph. Stat. 15, 2.Google ScholarCross Ref
Index Terms
- Sparse localized deformation components
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