Bharatanatyam, an Indian Classical Dance form, represents the rich cultural heritage of India. Analysis and recognition of such dance forms are critical for the preservation of cultural heritage. Like in most dance forms, a Bharatanatyam dancer performs in synchronization with structured rhythmic music, called Sollukattu, which comprises instrumental beats and vocalized utterances (bols) to create a rhythmic music structure. Computer analysis of Bharatanatyam, therefore, requires a structural analysis of Sollukattus. In this paper, we use speech processing techniques to recognize bols. Exploiting the predefined structures of Sollukattus and the detected bols, we recognize the Sollukattu. We estimate the tempo period by two methods. Finally, we generate a complete annotation of the audio signal by beat marking. For this, we also use the information of beats detected from the onset envelope of a Sollukattu signal. For training and test, we create a data set for Sollukattus and annotate them. We achieve 85% accuracy in bol recognition, 95% in Sollukattu recognition, 96% in tempo period estimation, and over 90% in beat marking. This is the maiden attempt to fully structurally analyze the music of an Indian Classical Dance form and the use of speech processing techniques for beat marking.
Beat-It: Beat-Synchronized Multi-Condition 3D Dance Generation
South China University of Technology（华南理工大学）；Guangdong Engineering Center for Large Model and GenAI Technology（广东省大模型与生成式人工智能技术工程中心）；Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information（广东省计算智能与网络空间信息重点实验室）；The Hong Kong Polytechnic University（香港理工大学）；Singapore Management University（新加坡管理大学）
Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model
Ulsan National Institute of Science and Technology, South Korea（乌山国立科学技术研究院，韩国）；Daegu Gyeongbuk Institute of Science and Technology, South Korea（大邱庆北科学技术研究院，韩国）
Waseda University（早稻田大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Honda Research Institute Japan（日本本田研究所）
Synthesizing human's movements such as dancing is a flourishing research field which has several applications in computer graphics. Recent studies have demonstrated the advantages of deep neural networks (DNNs) for achieving remarkable performance in motion and music tasks with little effort for feature pre-processing. However, applying DNNs for generating dance to a piece of music is nevertheless challenging, because of 1) DNNs need to generate large sequences while mapping the music input, 2) the DNN needs to constraint the motion beat to the music, and 3) DNNs require a considerable amount …
Tempo as the Stable Cue: Hierarchical Mixture of Tempo and Beat Experts for Music to 3D Dance Generation
School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Hohai university（河海大学）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Institute for Infocomm Research (I 2 R), A*STAR（新加坡科技研究局信息与通信研究所）
Driving 3D characters to dance following a piece of music is highly challenging due to the spatial constraints applied to poses by choreography norms. In addition, the generated dance sequence also needs to maintain temporal coherency with different music genres. To tackle these challenges, we propose a novel music-to-dance framework, Bailando, with two powerful components: 1) a choreographic memory that learns to summarize meaningful dancing units from 3D pose sequence to a quantized codebook, 2) an actor-critic Generative Pre-trained Transformer (GPT) that composes these units to a fluent da…
Dance is an important human art form, but creating new dances can be difficult and time-consuming. In this work, we introduce Editable Dance GEneration (EDGE), a state-of-the-art method for editable dance generation that is capable of creating realistic, physically-plausible dances while remaining faithful to the input music. EDGE uses a transformer-based diffusion model paired with Jukebox, a strong music feature extractor, and confers powerful editing capabilities well-suited to dance, including joint-wise conditioning, and in-betweening. We introduce a new metric for physical plausibility, …
Illinois Institute of Technology（伊利诺伊理工学院）；Snap Inc.（Snap公司）；Princeton University（普林斯顿大学）
We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos. Our proposed framework takes dance video frames and human body motions as input, and learns to generate music samples that plausibly accompany the corresponding input. Unlike most existing conditional music generation works that generate specific types of mono-instrumental sounds using symbolic audio representations (e.g., MIDI), and that usually rely on pre-defined musical synthesizers, in this work we generate dance music in complex styles (e.g.,…
Eindhoven University of Technology（埃因霍温理工大学）；University of Florence（佛罗伦萨大学）；University of Basel（巴塞尔大学）；Conservatorio G.F. Ghedini di Cuneo（库尼奥G.F.盖迪尼音乐学院）；Musica Combinatoria（组合音乐机构）
We describe a system based on deep learning that generates drum patterns in the electronic dance music domain. Experimental results reveal that generated patterns can be employed to produce musically sound and creative transitions between different genres, and that the process of generation is of interest to practitioners in the field.
Dance-to-Music Generation with Encoder-based Textual Inversion
Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；University of Chinese Academy of Sciences（中国科学院大学）；Kuaishou Technology（快手科技）；University of Konstanz（康斯坦茨大学）；National Cheng-Kung University（成功大学）
Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；State Key Laboratory of Space-Ground Integrated Information Technology（空间地面一体化信息技术创新实验室）
GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation
School of Computer Science and Electronic Engineering, University of Surrey（萨里大学计算机科学与电子工程学院）；Department of Music and Media, University of Surrey（萨里大学音乐与媒体系）；Department of Informatics, University of Oslo（奥斯陆大学信息学系）；Centre for Vision, Speech and Signal Processing, University of Surrey（萨里大学视觉、语音与信号处理中心）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）
