ITGPT: A Transformer Based Architecture for the Generation of Dance Dance Revolution and In the Groove Charts

• URL: http://arxiv.org/abs/2607.14148v1
• Date: Tue, 14 Jul 2026 13:05:53 GMT
• Title: ITGPT: A Transformer Based Architecture for the Generation of Dance Dance Revolution and In the Groove Charts
• Abstract summary: Dance Dance Revolution and In the Groove are rhythm games consisting of songs and accompanying choreography, referred to as charts.<n>We propose ITGPT, a new transformer based architecture for the generation of DDR/ITG charts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dance Dance Revolution and In the Groove are rhythm games consisting of songs and accompanying choreography, referred to as charts. Players press arrows on a device referred to as a dance pad in time with steps determined by the song's chart. The process of manual chart generation is timestaking and difficult, motivating interest in automation. We propose ITGPT, a new transformer based architecture for the generation of DDR/ITG charts, and demonstrate significant improvements to generation accuracy and computational cost in comparison to predecessor work.

Related papers

• Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model [8.542693293312935]
  We propose emphMambaDance, a new dance generation approach that leverages a Mamba-based diffusion model.<n>Mamba, well-suited to handling long and autoregressive sequences, is integrated into our two-stage diffusion architecture, substituting off-the-shelf Transformer.<n>We show that our proposed method effectively generates plausible dance movements while reflecting essential characteristics, consistently from short to long dances, compared to the previous methods.
  arXiv  Detail & Related papers  (2026-03-09T06:59:03Z)
• Dance Dance ConvLSTM [0.0]
  textitDance Dance Revolution is a rhythm game consisting of songs and accompanying choreography, referred to as charts.<n>We introduce Dance Dance ConvLSTM, a new method for the automatic generation of DDR charts using a ConvLSTM based model.
  arXiv  Detail & Related papers  (2025-07-02T12:17:33Z)
• Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation [22.729568599120846]
  We propose Danceba, a novel framework that leverages gating mechanism to enhance rhythm-aware feature representation.<n>Phase-Based Rhythm Extraction (PRE) to precisely extract rhythmic information from musical phase data.<n>Temporal-Gated Causal Attention (TGCA) to focus on global rhythmic features.<n> Parallel Mamba Motion Modeling (PMMM) architecture to separately model upper and lower body motions.
  arXiv  Detail & Related papers  (2025-03-21T17:42:50Z)
• Controllable Dance Generation with Style-Guided Motion Diffusion [49.35282418951445]
  Dance plays an important role as an artistic form and expression in human culture, yet the creation of dance remains a challenging task.<n>Most dance generation methods rely solely on music, seldom taking into consideration intrinsic attributes such as music style or genre.<n>In this work, we introduce Flexible Dance Generation with Style Description Prompts (DGSDP), a diffusion-based framework suitable for diversified tasks of dance generation.
  arXiv  Detail & Related papers  (2024-06-12T04:55:14Z)
• Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment [87.20240797625648]
  We introduce a novel task within the field of 3D dance generation, termed dance accompaniment. It requires the generation of responsive movements from a dance partner, the "follower", synchronized with the lead dancer's movements and the underlying musical rhythm. We propose a GPT-based model, Duolando, which autoregressively predicts the subsequent tokenized motion conditioned on the coordinated information of the music, the leader's and the follower's movements.
  arXiv  Detail & Related papers  (2024-03-27T17:57:02Z)
• TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration [75.37311932218773]
  We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities. Our approach can generate realistic and coherent dance movements conditioned on both text and music while maintaining comparable performance with the two single modalities.
  arXiv  Detail & Related papers  (2023-04-05T12:58:33Z)
• 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)
• TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games [1.590611306750623]
  Patterning is a key identifier of high quality rhythm game content, seen as a necessary component in human rankings. We establish a new approach for chart generation that produces charts with more congruent, human-like patterning than seen in prior work.
  arXiv  Detail & Related papers  (2021-07-26T22:55:57Z)
• 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)
• Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning [55.854205371307884]
  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.
  arXiv  Detail & Related papers  (2020-06-11T00:08:25Z)

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.