What is edge-dance.github.io?
EDGE represents a breakthrough in AI-driven dance generation, combining transformer-based diffusion models with Jukebox's music feature extraction capabilities to create physically plausible and musically synchronized choreographies. The system excels in producing realistic dance movements while maintaining precise control over temporal and spatial constraints.
The technology supports various creative applications, including full-body dance generation, joint-wise choreography creation, motion in-betweening, and dance continuation. What sets EDGE apart is its ability to handle complex foot-ground contact scenarios and maintain physical realism through its innovative Contact Consistency Loss feature.
Features
- Music-Driven Generation: Creates dances from any input music using Jukebox embeddings
- Physical Plausibility: Maintains realistic foot-ground contact and movement physics
- Temporal Consistency: Generates arbitrary-length dances through sequence stitching
- Joint-wise Control: Supports separate upper and lower body movement generation
- Motion In-betweening: Creates smooth transitions between specified start and end poses
- Dance Continuation: Extends existing choreographies seamlessly
Use Cases
- Choreography Creation
- Dance Education
- Motion Design
- Animation Production
- Performance Art
- Interactive Dance Applications
FAQs
-
How does EDGE handle foot sliding in dance movements?
EDGE uses a Contact Consistency Loss feature that helps maintain physical realism while distinguishing between intentional and unintentional foot sliding during dance movements. -
What is the minimum length of dance sequences EDGE can generate?
EDGE is trained on 5-second dance clips, but can generate dances of any length by imposing temporal constraints on batches of sequences. -
Can EDGE work with any type of music?
Yes, EDGE can work with in-the-wild music samples using music embeddings from the Jukebox model to create high-quality dances.
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