#Body poser skin#

Liu, L., Zheng, Y., Tang, D., Yuan, Y., Fan, C., Zhou, K.: NeuroSkinning: automatic skin binding for production characters with deep graph networks.

In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. Lewis, J.P., Cordner, M., Fong, N.: Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation. In: Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. Kurihara, T., Miyata, N.: Modeling deformable human hands from medical images. In: Proceedings of the 2002 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. Kry, P.G., James, D.L., Pai, D.K.: Eigenskin: real time large deformation character skinning in hardware. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. Kocabas, M., Athanasiou, N., Black, M.J.: VIBE: video inference for human body pose and shape estimation. Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. Jacobson, A., Baran, I., Kavan, L., Popović, J., Sorkine, O.: Fast automatic skinning transformations. Huang, Y., Kaufmann, M., Aksan, E., Black, M.J., Hilliges, O., Pons-Moll, G.: Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time. In: International Conference on 3D Vision (3DV), pp. Huang, Y., et al.: Towards accurate marker-less human shape and pose estimation over time. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. Hirshberg, D.A., Loper, M., Rachlin, E., Black, M.J.: Coregistration: simultaneous alignment and modeling of articulated 3D shape. Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., Seidel, H.P.: A statistical model of human pose and body shape. 105–112 (2013)įreifeld, O., Black, M.J.: Lie bodies: a manifold representation of 3D human shape.

SIGGRAPH) 22(3), 587–594 (2003)Īnguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J.: SCAPE: shape completion and animation of PEople. SIGGRAPH) 21(3), 612–619 (2002)Īllen, B., Curless, B., Popović, Z.: The space of human body shapes: reconstruction and parameterization from range scans. 98–109 (2018)Īllen, B., Curless, B., Popović, Z.: Articulated body deformation from range scan data. Īlldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Detailed human avatars from monocular video. STAR is publicly available for research purposes at.

STAR is compact, generalizes better to new bodies and is a drop-in replacement for SMPL. We address this by training STAR with an additional 10,000 scans of male and female subjects, and show that this results in better model generalization. Third, we show that the shape space of SMPL is not rich enough to capture the variation in the human population. Consequently, we learn shape-dependent pose-corrective blend shapes that depend on both body pose and BMI. Second, SMPL factors pose-dependent deformations from body shape while, in reality, people with different shapes deform differently. When trained on the same data as SMPL, STAR generalizes better despite having many fewer parameters. This sparse formulation results in more realistic deformations and significantly reduces the number of model parameters to 20% of SMPL. To address this, we define per-joint pose correctives and learn the subset of mesh vertices that are influenced by each joint movement. These dense pose-corrective offsets relate every vertex on the mesh to all the joints in the kinematic tree, capturing spurious long-range correlations. First, SMPL has a huge number of parameters resulting from its use of global blend shapes. While popular, we show that SMPL has several limitations and introduce STAR, which is quantitatively and qualitatively superior to SMPL. The SMPL body model is widely used for the estimation, synthesis, and analysis of 3D human pose and shape.

Body poser