TeMuDance enables text-based semantic control over music-conditioned dance generation by using motion as a bridge to align existing unpaired datasets and training a lightweight text branch on a frozen diffusion backbone with noise-filtered supervision.
arXiv preprint arXiv:2008.08171 (2020)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
STREAM decouples text (via AdaLN) from music (via energy-based BEAM attention) to generate editable, musically aligned dance motions with a new annotated dataset and editability metric.
DanceDuo applies diffusion models for music-synchronized dance generation and pose estimation for user-AI performance comparison, with a user study reporting positive feedback on usability.
citing papers explorer
-
TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation
TeMuDance enables text-based semantic control over music-conditioned dance generation by using motion as a bridge to align existing unpaired datasets and training a lightweight text branch on a frozen diffusion backbone with noise-filtered supervision.
-
Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
STREAM decouples text (via AdaLN) from music (via energy-based BEAM attention) to generate editable, musically aligned dance motions with a new annotated dataset and editability metric.
-
DanceDuo: Bridging Human Movement and AI Choreography
DanceDuo applies diffusion models for music-synchronized dance generation and pose estimation for user-AI performance comparison, with a user study reporting positive feedback on usability.