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2018


A machine from machines
A machine from machines

Fischer, P.

Nature Physics, 14, pages: 1072–1073, July 2018 (misc)

Abstract
Building spinning microrotors that self-assemble and synchronize to form a gear sounds like an impossible feat. However, it has now been achieved using only a single type of building block -- a colloid that self-propels.

pf

link (url) DOI [BibTex]

2018


link (url) DOI [BibTex]


Co-Registration -- Simultaneous Alignment and Modeling of Articulated {3D} Shapes
Co-Registration – Simultaneous Alignment and Modeling of Articulated 3D Shapes

Black, M., Hirshberg, D., Loper, M., Rachlin, E., Weiss, A.

Febuary 2018, U.S.~Patent 9,898,848 (misc)

Abstract
Present application refers to a method, a model generation unit and a computer program (product) for generating trained models (M) of moving persons, based on physically measured person scan data (S). The approach is based on a common template (T) for the respective person and on the measured person scan data (S) in different shapes and different poses. Scan data are measured with a 3D laser scanner. A generic personal model is used for co-registering a set of person scan data (S) aligning the template (T) to the set of person scans (S) while simultaneously training the generic personal model to become a trained person model (M) by constraining the generic person model to be scan-specific, person-specific and pose-specific and providing the trained model (M), based on the co registering of the measured object scan data (S).

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text [BibTex]


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Emission and propagation of multi-dimensional spin waves in anisotropic spin textures

Sluka, V., Schneider, T., Gallardo, R. A., Kakay, A., Weigand, M., Warnatz, T., Mattheis, R., Roldan-Molina, A., Landeros, P., Tiberkevich, V., Slavin, A., Schütz, G., Erbe, A., Deac, A., Lindner, J., Raabe, J., Fassbender, J., Wintz, S.

2018 (misc)

mms

link (url) [BibTex]

link (url) [BibTex]


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Thermal skyrmion diffusion applied in probabilistic computing

Zázvorka, J., Jakobs, F., Heinze, D., Keil, N., Kromin, S., Jaiswal, S., Litzius, K., Jakob, G., Virnau, P., Pinna, D., Everschor-Sitte, K., Donges, A., Nowak, U., Kläui, M.

2018 (misc)

mms

link (url) [BibTex]

link (url) [BibTex]

2017


Crowdshaping Realistic {3D} Avatars with Words
Crowdshaping Realistic 3D Avatars with Words

Streuber, S., Ramirez, M. Q., Black, M., Zuffi, S., O’Toole, A., Hill, M. Q., Hahn, C. A.

August 2017, Application PCT/EP2017/051954 (misc)

Abstract
A method for generating a body shape, comprising the steps: - receiving one or more linguistic descriptors related to the body shape; - retrieving an association between the one or more linguistic descriptors and a body shape; and - generating the body shape, based on the association.

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Google Patents [BibTex]

2017


Google Patents [BibTex]

2016


Skinned multi-person linear model
Skinned multi-person linear model

Black, M.J., Loper, M., Mahmood, N., Pons-Moll, G., Romero, J.

December 2016, Application PCT/EP2016/064610 (misc)

Abstract
The invention comprises a learned model of human body shape and pose dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity- dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. The invention quantitatively evaluates variants of SMPL using linear or dual- quaternion blend skinning and show that both are more accurate than a Blend SCAPE model trained on the same data. In a further embodiment, the invention realistically models dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

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Google Patents [BibTex]

2016


Google Patents [BibTex]


Perceiving Systems (2011-2015)
Perceiving Systems (2011-2015)
Scientific Advisory Board Report, 2016 (misc)

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pdf [BibTex]

pdf [BibTex]