Edge DanceMusic choreographies AI Tool

The conditional diffusion model in EDGE works by learning to map the music embedding into a series of 5-second dance clips. This learning process is triggered once the input music is encoded into embeddings by the Jukebox model.

EDGE applies temporal constraints on batches of multiple clips during inference. These constraints enforce temporal consistency before the clips are stitched into an arbitrary-length full video. Temporal constraints are also used in EDGE to generate dances of any length by imposing temporal continuity between batches of multiple sequences.

EDGE can support arbitrary spatial and temporal constraints. These can be used to support applications such as arbitrarily long dances by enforcing temporal continuity between batches of multiple sequences, dances subject to joint-wise constraints like lower body generation given upper body motion, or vice versa, In-Betweening motions, and dances that start with a prespecified motion.

The Contact Consistency Loss in EDGE is a new feature that improves physical realism in the generated dances. It learns when feet should and shouldn't slide. This feature significantly improves physical realism while keeping intentional foot-ground sliding intact, thereby avoiding unintentional foot sliding.

EDGE ensures physical realism of the generated dances with its new Contact Consistency Loss that learns when feet should and shouldn't slide. This significantly improves physical realism while keeping intentional foot-ground contact sliding intact. The tool has been trained with physical realism in mind.

EDGE can be used in various applications including creating arbitrarily long dances, creating dances subject to joint-wise constraints, for motion in-betweening, and for dance continuation. These applications can be useful in various industries like entertainment and arts.

Joint-wise constraints in EDGE allow for lower body generation given the motion of the upper body, or vice versa. This can be used for creating dances that require specific movements of either the upper body or the lower body.

Yes, EDGE can generate dances for any length of music. It does this by imposing temporal constraints on batches of sequences, allowing it to generate dances of any length.

Human raters have shown a strong preference for dances generated by EDGE over those of previous work, indicating an improved performance by EDGE compared to previous methodologies.

EDGE is trained on 5-second dance clips. These clips are used to form the temporal sequences that the conditional diffusion model then uses during the process of learning to map the music embedding.

EDGE handles foot-sliding in dance motions using its new Contact Consistency Loss that learns when feet should and shouldn't slide. It avoids unintentional foot sliding, thereby improving the physical realism of the dances while maintaining intentional foot-ground contact sliding.

Motion in-betweening, in the context of EDGE, refers to the creation of dances that start and end with prespecified motions. It's one of the temporal constraints that can be implemented using EDGE.

The source code for EDGE can be accessed from the Github repository through the link: https://github.com/Stanford-TML/EDGE

EDGE handles dance continuation by allowing for dances that start with a prespecified motion. This is achieved through its support for temporal constraints which can be tailored to suit the requirements of dance continuation.

EDGE's approach towards generating upper body from lower body motion and vice versa is accomplished through joint-wise constraints. These constraints allow the generation of either lower body given the upper body motion or vice versa.

Yes, human raters can determine the quality of dances generated by EDGE. They have shown a strong preference for dances generated by EDGE over those of previous work, confirming its ability to generate high-quality dance choreographies.

EDGE can create choreography for a broad range of music. It leverages music embeddings from the powerful Jukebox model, which allows it to understand and create dances even for in-the-wild music samples.