Generating dance motion using musical features
Notice bibliographique
Résumé
In a computer animation workflow, an animator has to plot keyframes and adjust the in-between frames to create or edit a motion, and motion is represented by keyframes and in-between frames that connect the keyframes.The sequence of significant poses, represented by keyframes, determine the general motion in animations.In-between frames are generated by interpolation strategies using keyframes, such as linear interpolation or parametric curve equation, where the starting and ending point of the parametric curve set to the two adjacent selected keyframes.In-between frames determine the trajectory and the speed an animated object moves from one keyframe to the next.Since in-between frames are reconstructed from adjacent keyframes, the selection of keyframes influences the reconstruction distance of reconstructed in-betweens, and a great amount of research has focused on strategies to select appropriate keyframes.Dance motion and dance music are often closely related to each other.Choreography is generally designed to synchronize with the rhythm of the music, and various deep learning research topics have been proposed to choreograph dance motion from dance music.In this thesis, inspired by previous research studying the close relation between dance and music, I use musical features of dance music to select keyframes and examine the reconstruction distance of Abstract ii the generated dance motion based on the AIST++ dataset.I hypothesize that the close relation between dance and music may improve reconstruction of dance motions.Three experiments are designed to evaluate the effect of musical features when reconstructing dance motions.The first experiment is used as a baseline and does not use musical features in both keyframe selection and in-between reconstructions.Keyframes are selected evenly over the measure.The List of Tables xiii 4.2 Evaluate F ID k with three, five, and nine keyframes. . . . . . . . . . . . . . . .4.3 Evaluate M SE with three, five, and nine keyframes. . . . . . . . . . . . . . . . .4.4 Evaluated results of dance reconstructed by selecting nine keyframes and reconstruct in-between frames without musical features. . . . . . . . . . . . . . .4.5 Evaluated results of dance reconstructed by selecting three keyframes and reconstruct in-between frames without musical features. . . . . . . . . . . . . . .4.6 Results of selecting nine keyframes with keyframe selection strategies using musical features, and reconstruct in-between frames using strategies independent of musical features. . . . . . . . . . . . . . . . . . . . . . . . . . . .4.7 Results of selecting three keyframes with keyframe selection strategies using musical features, and reconstruct in-between frames using strategies independent of musical features. . . . . . . . . . . . . . . . . . . . . . . . . . . .4.8 The evaluated result of dances reconstructed by evenly selecting nine keyframes and deriving in-between frames using musical features. . . . . . . . . . . . . . .4.9 The evaluated result of dances reconstructed by evenly selecting three keyframes and deriving in-between frames using musical features. . . . . . . . . . . . . . .4.10 Evaluated results of dance reconstructed by selecting nine keyframes and reconstruct in-between frames with/without musical features. . . . . . . . . . . .4.11 Evaluated results of dance reconstructed by selecting three keyframes and reconstruct in-between frames with/without musical features. . . . . . . . . . . .List of Tables xiv 4.12 Evaluated results of dance reconstructed by selecting nine keyframes and reconstruct in-between frames with/without musical features. . . . . . . . . . . .83 4.13 Evaluated results of dance reconstructed by selecting three keyframes and reconstruct in-between frames with/without musical features. . . . . . . . . . . .
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».