Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds Article Swipe
Noémie Jaquier
,
Leonel Rozo
,
Miguel González-Duque
,
Viacheslav Borovitskiy
,
Tamim Asfour
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.01672
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.01672
Human motion taxonomies serve as high-level hierarchical abstractions that classify how humans move and interact with their environment. They have proven useful to analyse grasps, manipulation skills, and whole-body support poses. Despite substantial efforts devoted to design their hierarchy and underlying categories, their use remains limited. This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories. To overcome this problem, we propose to model taxonomy data via hyperbolic embeddings that capture the associated hierarchical structure. We achieve this by formulating a novel Gaussian process hyperbolic latent variable model that incorporates the taxonomy structure through graph-based priors on the latent space and distance-preserving back constraints. We validate our model on three different human motion taxonomies to learn hyperbolic embeddings that faithfully preserve the original graph structure. We show that our model properly encodes unseen data from existing or new taxonomy categories, and outperforms its Euclidean and VAE-based counterparts. Finally, through proof-of-concept experiments, we show that our model may be used to generate realistic trajectories between the learned embeddings.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.01672
- https://arxiv.org/pdf/2210.01672
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4302307229
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4302307229Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.01672Digital Object Identifier
- Title
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Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifoldsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-10-04Full publication date if available
- Authors
-
Noémie Jaquier, Leonel Rozo, Miguel González-Duque, Viacheslav Borovitskiy, Tamim AsfourList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.01672Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2210.01672Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2210.01672Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Taxonomy (biology), Hierarchy, Hyperbolic space, Graph, Theoretical computer science, Machine learning, Euclidean geometry, Hierarchical database model, Data mining, Mathematics, Economics, Botany, Geometry, Biology, Market economy, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.different | 131 |
| abstract_inverted_index.hierarchy | 38 |
| abstract_inverted_index.realistic | 182 |
| abstract_inverted_index.structure | 64, 113 |
| abstract_inverted_index.associated | 73, 93 |
| abstract_inverted_index.attributed | 49 |
| abstract_inverted_index.embeddings | 89, 138 |
| abstract_inverted_index.faithfully | 140 |
| abstract_inverted_index.high-level | 5 |
| abstract_inverted_index.hyperbolic | 88, 105, 137 |
| abstract_inverted_index.structure. | 95, 145 |
| abstract_inverted_index.taxonomies | 2, 134 |
| abstract_inverted_index.underlying | 40 |
| abstract_inverted_index.whole-body | 28 |
| abstract_inverted_index.categories, | 41, 160 |
| abstract_inverted_index.categories. | 76 |
| abstract_inverted_index.embeddings. | 187 |
| abstract_inverted_index.formulating | 100 |
| abstract_inverted_index.graph-based | 115 |
| abstract_inverted_index.outperforms | 162 |
| abstract_inverted_index.substantial | 32 |
| abstract_inverted_index.abstractions | 7 |
| abstract_inverted_index.constraints. | 124 |
| abstract_inverted_index.environment. | 17 |
| abstract_inverted_index.experiments, | 171 |
| abstract_inverted_index.hierarchical | 6, 63, 94 |
| abstract_inverted_index.incorporates | 110 |
| abstract_inverted_index.manipulation | 25 |
| abstract_inverted_index.trajectories | 183 |
| abstract_inverted_index.computational | 54 |
| abstract_inverted_index.counterparts. | 167 |
| abstract_inverted_index.heterogeneous | 71 |
| abstract_inverted_index.high-dimensional | 70 |
| abstract_inverted_index.proof-of-concept | 170 |
| abstract_inverted_index.distance-preserving | 122 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |