Fetching the paper…
Reading the bibliography…
Human motion taxonomies serve as high-level hierarchical abstractions that classify how humans move and interact with their environment.
Stochastic processes in several dimensions
Whittle, P · 1963
Earlier work this paper cites.
Handbook of mathematical functions with formulas, graphs, and mathematical tables , volume 55
Abramowitz, M. and Stegun, I. A · 1964
Earlier work this paper cites.
Special functions and their applications
Lebedev, N. N., Silverman, R. A., and Livhtenberg, D · 1965
Earlier work this paper cites.
An upper bound to the spectrum of Δ \Delta on a manifold of negative curvature
McKean, H. P · 1970
Earlier work this paper cites.
Eigenvalues in Riemannian geometry
Chavel, I · 1984
Earlier work this paper cites.
On grasp choice, grasp models, and the design of hands for manufacturing tasks
Cutkosky, M. R · 1989
Earlier work this paper cites.
The heat kernel on hyperbolic space
Grigoryan, A. and Noguchi, M · 1998
Earlier work this paper cites.
Gaussian process latent variable models for visualisation of high dimensional data
Lawrence, N. D · 2003
Earlier work this paper cites.
Local distance preservation in the GP-LVM through back constraints
Lawrence, N. D. and Quiñonero Candela, J · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Rasmussen, C. E. and Williams, C. K · 2006
Earlier work this paper cites.
Optimization Algorithms on Matrix Manifolds
Absil, P.-A., Mahony, R., and Sepulchre, R · 2007
Earlier work this paper cites.
Context and observation driven latent variable model for human pose estimation
Gupta, A., Chen, T., Chen, F., Kimber, D., and Davis, L. S · 2008
Earlier work this paper cites.
Topologically-constrained latent variable models
Urtasun, R., Fleet, D. J., Geiger, A., Popović, J., Darrell, T. J., and Lawrence, N. D · 2008
Earlier work this paper cites.
Variational Learning of Inducing Variables in Sparse Gaussian Processes
Titsias, M. K · 2009
Earlier work this paper cites.
Kick-starting GPLVM optimization via a connection to metric mds
Bitzer, S. and Williams, C. K. I · 2010
Earlier work this paper cites.
Hyperbolic geometry of complex networks
Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., and Boguñá, M · 2010
Earlier work this paper cites.
Spatio-temporal modeling of grasping actions
Romero, J., Feix, T., Kjellström, H., and Kragic, D · 2010
Earlier work this paper cites.
Bayesian Gaussian process latent variable model
Titsias, M. K. and Lawrence, N. D · 2010
Earlier work this paper cites.
Learning GP-BayesFilters via Gaussian process latent variable models
Ko, J. and Fox, D · 2011
Earlier work this paper cites.
A hand-centric classification of human and robot dexterous manipulation
Bullock, I. M., Ma, R. R., and Dollar, A. M · 2012
Earlier work this paper cites.
Stationary Gaussian random fields on hyperbolic spaces and on Euclidean spheres
Cohen, S. and Lifshits, M · 2012
Earlier work this paper cites.
Multilayer joint gait-pose manifolds for human gait motion modeling
Ding, M. and Fan, G · 2014
Earlier work this paper cites.
Metrics for probabilistic geometries
Tosi, A., Hauberg, S., Vellido, A., and Lawrence, N. D · 2014
Earlier work this paper cites.
On the analysis of movement smoothness
Balasubramanian, S., Melendez-Calderon, A., Roby-Brami, A., and Burdet, E · 2015
Cited alongside, same era.
The GRASP taxonomy of human grasp types
Feix, T., Romero, J., Schmiedmayer, H.-B., Dollar, A. M., and Kragic, D · 2015
Cited alongside, same era.
Scalable variational Gaussian process classification
Hensman, J., Matthews, A., and Ghahramani, Z · 2015
Cited alongside, same era.
Robot grasp planning based on demonstrated grasp strategies
Lin, Y. and Sun, Y · 2015
Cited alongside, same era.
Grasp taxonomy based on force distribution
Abbasi, B., Noohi, E., Parastegari, S., and Žefran, M · 2016
Cited alongside, same era.
Using language models to generate whole-body multi-contact motions
Mandery, C., Borràs, J., Jöchner, M., and Asfour, T · 2016
Cited alongside, same era.
Matérn Gaussian processes on Riemannian manifolds
Borovitskiy, V., Terenin, A., Mostowsky, P., and Deisenroth, M. P · 2020
Later among the works it cites.
Latent variable modelling with hyperbolic normalizing flows
Bose, J., Smofsky, A., Liao, R., Panangaden, P., and Hamilton, W · 2020
Later among the works it cites.
From trees to continuous embeddings and back: Hyperbolic hierarchical clustering
Chami, I., Gu, A., Chatziafratis, V., and Ré, C · 2020
Later among the works it cites.
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Jensen, K., Kao, T.-C., Tripodi, M., and Hennequin, G · 2020
Later among the works it cites.
Geoopt: Riemannian optimization in PyTorch
Kochurov, M., Karimov, R., and Kozlukov, S · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Unifying representations and large-scale whole-body motion databases for studying human motion
Mandery, C., Terlemez, O., Do, M., Vahrenkamp, N., and Asfour, T · 2016
Cited alongside, same era.
Springer Handbook of Robotics
Siciliano, B. and Khatib, O · 2016
Cited alongside, same era.
A whole-body support pose taxonomy for multi-contact humanoid robot motions
Borràs, J., Mandery, C., and Asfour, T · 2017
Cited alongside, same era.
Poincaré embeddings for learning hierarchical representations
Nickel, M. and Kiela, D · 2017
Cited alongside, same era.
The latent geometry of the human protein interaction network
Alanis-Lobato, G., Mier, P., and Andrade-Navarro, M · 2018
Cited alongside, same era.
Introduction to Riemannian Manifolds
Lee, J · 2018
Cited alongside, same era.
Langenstein, A · 2020
Later among the works it cites.
A motion taxonomy for manipulation embedding
Paulius, D., Eales, N., and Sun, Y · 2020
Later among the works it cites.
Mixed-curvature variational autoencoders
Skopek, O., Ganea, O.-E., and Bécigneul, G · 2020
Later among the works it cites.
Matérn Gaussian processes on graphs
Borovitskiy, V., Azangulov, I., Terenin, A., Mostowsky, P., Deisenroth, M., and Durrande, N · 2021
Later among the works it cites.
Neural distance embeddings for biological sequences
Corso, G., Ying, Z., Pándy, M., Veličković, P., Leskovec, J., and Liò, P · 2021
Later among the works it cites.
Computationally tractable Riemannian manifolds for graph embeddings
Cruceru, C., Bécigneul, G., and Ganea, O.-E · 2021
Later among the works it cites.
Geometry-aware Bayesian optimization in robotics using Riemannian Matérn kernels
Jaquier, N., Borovitskiy, V., Smolensky, A., Terenin, A., Asfour, T., and Rozo, L · 2021
Later among the works it cites.
Isometric Gaussian process latent variable model for dissimilarity data
Jørgensen, M. and Hauberg, S · 2021
Later among the works it cites.
Hyperbolic deep neural networks: A survey
Peng, W., Varanka, T., Mostafa, A., Shi, H., and Zhao, G · 2021
Later among the works it cites.
Teach: Temporal action compositions for 3d humans
Athanasiou, N., Petrovich, M., Black, M. J., and Varol, G · 2022
Closest in time.
Adversarial parametric pose prior
Davydov, A., Remizova, A., Constantin, V., Honari, S., Salzmann, M., and Fua, P · 2022
Closest in time.
Heterogeneous manifolds for curvature-aware graph embedding
Giovanni, F. D., Luise, G., and Bronstein, M. M · 2022
Closest in time.
A bimanual manipulation taxonomy
Krebs, F. and Asfour, T · 2022
Closest in time.
Rethinking the compositionality of point clouds through regularization in the hyperbolic space
Montanaro, A., Valsesia, D., and Magli, E · 2022
Closest in time.
An introduction to optimization on smooth manifolds
Boumal, N · 2023
Closest in time.
Fidelity of hyperbolic space for bayesian phylogenetic inference
Macaulay, M., Darling, A., and Fourment, M · 2023
Closest in time.
Human motion diffusion as a generative prior
Shafir, Y., Tevet, G., Kapon, R., and Bermano, A. H · 2023
Closest in time.