Matches in SemOpenAlex for { <https://semopenalex.org/work/W4361764560> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W4361764560 endingPage "254" @default.
- W4361764560 startingPage "243" @default.
- W4361764560 abstract "Intelligent choreographic from music is a popular field of study currently. Many works use fragment splicing to generate new motions, which lacks motion diversity. When the input is only music, the frame-by-frame generation methods lead to similar motions generated by the same music. Some works improve this problem by adding motions as one of the inputs, but requires a high number of frames. In this paper, a new transformer-based neural network, TG-dance, is proposed for predicting high-quality 3D dance motions that follow the musical rhythms. We propose a new idea of multi-level expansion of motion sequences and design a new motion encoder, using a multi-level transformer-upsampling layer. The multi-head attention in the transformer allows better access to contextual information. The upsampling can greatly reduce motion frames input, and is memory friendly. We use generative adversarial network to effectively improve the quality of generated motions. We designed experiments on the publicly available large dataset AIST++. The experimental results show that TG-dance network outperforms the latest models in quantitative and qualitative. Our model inputs fewer frames of motion sequences and audio features to predict high-quality 3D dance motion sequences that follow the rhythm of the music." @default.
- W4361764560 created "2023-04-04" @default.
- W4361764560 creator A5010581787 @default.
- W4361764560 creator A5010776860 @default.
- W4361764560 creator A5059575071 @default.
- W4361764560 creator A5088458293 @default.
- W4361764560 date "2023-01-01" @default.
- W4361764560 modified "2023-10-18" @default.
- W4361764560 title "TG-Dance: TransGAN-Based Intelligent Dance Generation with Music" @default.
- W4361764560 cites W2191779130 @default.
- W4361764560 cites W2336236730 @default.
- W4361764560 cites W2606517404 @default.
- W4361764560 cites W2963165299 @default.
- W4361764560 cites W3010049297 @default.
- W4361764560 cites W3010876998 @default.
- W4361764560 cites W3036644940 @default.
- W4361764560 cites W3085795245 @default.
- W4361764560 cites W3093390323 @default.
- W4361764560 cites W3101151469 @default.
- W4361764560 cites W3105160031 @default.
- W4361764560 cites W3108240585 @default.
- W4361764560 cites W3136525061 @default.
- W4361764560 cites W3181695292 @default.
- W4361764560 cites W3204221554 @default.
- W4361764560 cites W4205471895 @default.
- W4361764560 cites W4206861281 @default.
- W4361764560 cites W4239352679 @default.
- W4361764560 doi "https://doi.org/10.1007/978-3-031-27077-2_19" @default.
- W4361764560 hasPublicationYear "2023" @default.
- W4361764560 type Work @default.
- W4361764560 citedByCount "0" @default.
- W4361764560 crossrefType "book-chapter" @default.
- W4361764560 hasAuthorship W4361764560A5010581787 @default.
- W4361764560 hasAuthorship W4361764560A5010776860 @default.
- W4361764560 hasAuthorship W4361764560A5059575071 @default.
- W4361764560 hasAuthorship W4361764560A5088458293 @default.
- W4361764560 hasConcept C110384440 @default.
- W4361764560 hasConcept C115961682 @default.
- W4361764560 hasConcept C121332964 @default.
- W4361764560 hasConcept C124952713 @default.
- W4361764560 hasConcept C142362112 @default.
- W4361764560 hasConcept C147446459 @default.
- W4361764560 hasConcept C154945302 @default.
- W4361764560 hasConcept C165801399 @default.
- W4361764560 hasConcept C28490314 @default.
- W4361764560 hasConcept C31972630 @default.
- W4361764560 hasConcept C41008148 @default.
- W4361764560 hasConcept C50644808 @default.
- W4361764560 hasConcept C62520636 @default.
- W4361764560 hasConcept C66322947 @default.
- W4361764560 hasConceptScore W4361764560C110384440 @default.
- W4361764560 hasConceptScore W4361764560C115961682 @default.
- W4361764560 hasConceptScore W4361764560C121332964 @default.
- W4361764560 hasConceptScore W4361764560C124952713 @default.
- W4361764560 hasConceptScore W4361764560C142362112 @default.
- W4361764560 hasConceptScore W4361764560C147446459 @default.
- W4361764560 hasConceptScore W4361764560C154945302 @default.
- W4361764560 hasConceptScore W4361764560C165801399 @default.
- W4361764560 hasConceptScore W4361764560C28490314 @default.
- W4361764560 hasConceptScore W4361764560C31972630 @default.
- W4361764560 hasConceptScore W4361764560C41008148 @default.
- W4361764560 hasConceptScore W4361764560C50644808 @default.
- W4361764560 hasConceptScore W4361764560C62520636 @default.
- W4361764560 hasConceptScore W4361764560C66322947 @default.
- W4361764560 hasLocation W43617645601 @default.
- W4361764560 hasOpenAccess W4361764560 @default.
- W4361764560 hasPrimaryLocation W43617645601 @default.
- W4361764560 hasRelatedWork W1891287906 @default.
- W4361764560 hasRelatedWork W1969923398 @default.
- W4361764560 hasRelatedWork W1998618079 @default.
- W4361764560 hasRelatedWork W2090346739 @default.
- W4361764560 hasRelatedWork W2181461482 @default.
- W4361764560 hasRelatedWork W2301388240 @default.
- W4361764560 hasRelatedWork W2787555990 @default.
- W4361764560 hasRelatedWork W2990559186 @default.
- W4361764560 hasRelatedWork W3175382666 @default.
- W4361764560 hasRelatedWork W4360962690 @default.
- W4361764560 isParatext "false" @default.
- W4361764560 isRetracted "false" @default.
- W4361764560 workType "book-chapter" @default.