Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning

• URL: http://arxiv.org/abs/2006.06119v8
• Date: Sat, 9 Sep 2023 13:31:44 GMT
• Title: Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
• Authors: Ruozi Huang, Huang Hu, Wei Wu, Kei Sawada, Mi Zhang and Daxin Jiang
• Abstract summary: We formalize the music-conditioned dance generation as a sequence-to-sequence learning problem. We propose a novel curriculum learning strategy to alleviate error accumulation of autoregressive models in long motion sequence generation. Our approach significantly outperforms the existing state-of-the-arts on automatic metrics and human evaluation.
• Score: 55.854205371307884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dancing to music is one of human's innate abilities since ancient times. In machine learning research, however, synthesizing dance movements from music is a challenging problem. Recently, researchers synthesize human motion sequences through autoregressive models like recurrent neural network (RNN). Such an approach often generates short sequences due to an accumulation of prediction errors that are fed back into the neural network. This problem becomes even more severe in the long motion sequence generation. Besides, the consistency between dance and music in terms of style, rhythm and beat is yet to be taken into account during modeling. In this paper, we formalize the music-conditioned dance generation as a sequence-to-sequence learning problem and devise a novel seq2seq architecture to efficiently process long sequences of music features and capture the fine-grained correspondence between music and dance. Furthermore, we propose a novel curriculum learning strategy to alleviate error accumulation of autoregressive models in long motion sequence generation, which gently changes the training process from a fully guided teacher-forcing scheme using the previous ground-truth movements, towards a less guided autoregressive scheme mostly using the generated movements instead. Extensive experiments show that our approach significantly outperforms the existing state-of-the-arts on automatic metrics and human evaluation. We also make a demo video to demonstrate the superior performance of our proposed approach at https://www.youtube.com/watch?v=lmE20MEheZ8.

Related papers

• LongDanceDiff: Long-term Dance Generation with Conditional Diffusion Model [3.036230795326545]
  LongDanceDiff is a conditional diffusion model for sequence-to-sequence long-term dance generation. It addresses the challenges of temporal coherency and spatial constraint. We also address common visual quality issues in dance generation, such as foot sliding and unsmooth motion.
  arXiv  Detail & Related papers  (2023-08-23T06:37:41Z)
• DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation [89.50310360658791]
  We present a novel cascaded motion diffusion model, DiffDance, designed for high-resolution, long-form dance generation. This model comprises a music-to-dance diffusion model and a sequence super-resolution diffusion model. We demonstrate that DiffDance is capable of generating realistic dance sequences that align effectively with the input music.
  arXiv  Detail & Related papers  (2023-08-05T16:18:57Z)
• BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis [123.73677487809418]
  We introduce a new dataset aiming to challenge common assumptions in dance motion synthesis. We focus on breakdancing which features acrobatic moves and tangled postures. Our efforts produced the BRACE dataset, which contains over 3 hours and 30 minutes of densely annotated poses.
  arXiv  Detail & Related papers  (2022-07-20T18:03:54Z)
• Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory [92.81383016482813]
  We propose a novel music-to-dance framework, Bailando, for driving 3D characters to dance following a piece of music. We introduce an actor-critic Generative Pre-trained Transformer (GPT) that composes units to a fluent dance coherent to the music. Our proposed framework achieves state-of-the-art performance both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-03-24T13:06:43Z)
• Music-to-Dance Generation with Optimal Transport [48.92483627635586]
  We propose a Music-to-Dance with Optimal Transport Network (MDOT-Net) for learning to generate 3D dance choreographs from music. We introduce an optimal transport distance for evaluating the authenticity of the generated dance distribution and a Gromov-Wasserstein distance to measure the correspondence between the dance distribution and the input music.
  arXiv  Detail & Related papers  (2021-12-03T09:37:26Z)
• Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio [7.612064511889756]
  Learning to move naturally from music, i.e., to dance, is one of the more complex motions humans often perform effortlessly. In this paper, we design a novel method based on graph convolutional networks to tackle the problem of automatic dance generation from audio information. Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions preserving the key movements of different music styles.
  arXiv  Detail & Related papers  (2020-11-25T19:53:53Z)
• Learning to Generate Diverse Dance Motions with Transformer [67.43270523386185]
  We introduce a complete system for dance motion synthesis. A massive dance motion data set is created from YouTube videos. A novel two-stream motion transformer generative model can generate motion sequences with high flexibility.
  arXiv  Detail & Related papers  (2020-08-18T22:29:40Z)
• Music2Dance: DanceNet for Music-driven Dance Generation [11.73506542921528]
  We propose a novel autoregressive generative model, DanceNet, to take the style, rhythm and melody of music as the control signals. We capture several synchronized music-dance pairs by professional dancers, and build a high-quality music-dance pair dataset.
  arXiv  Detail & Related papers  (2020-02-02T17:18:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.