Summary of Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns, by Ronghui Li et al.
Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns
by Ronghui Li, Hongwen Zhang, Yachao Zhang, Yuxiang Zhang, Youliang Zhang, Jie Guo, Yan Zhang, Xiu Li, Yebin Liu
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new framework called Lodge++ for generating high-quality, long-sequence, and vivid dances given the music and desired genre. The approach involves a two-stage strategy to produce dances from coarse to fine. In the first stage, a global choreography network is designed to generate dance primitives that capture complex global patterns. In the second stage, a primitive-based dance diffusion model is used to further generate high-quality dances in parallel, adhering to the complex patterns. The framework also employs modules for penetration guidance, foot refinement, and multi-genre discriminator to improve physical plausibility and genre consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lodge++ is a new way to create long and beautiful dance sequences given music and a desired style of dance. It uses two steps: first, it generates simple movements that capture the overall pattern of the dance, then it builds on those movements to create a longer sequence. The framework also makes sure the dancers don’t overlap or trip over each other, and that the dance stays true to its original genre. |
Keywords
* Artificial intelligence * Diffusion model
