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）
Sangjune Park, Inhyeok Choi, Donghyeon Soon, Youngwoo Jeon, Kyungdon Joo
Dance is a form of human motion characterized by emotional expression and communication, playing a role in various fields such as music, virtual reality, and content creation. Existing methods for dance generation often fail to adequately capture the inherently sequential, rhythmical, and music-synchronized characteristics of dance. In this paper, we propose \emph{MambaDance}, a new dance generation approach that leverages a Mamba-based diffusion model. Mamba, well-suited to handling long and autoregressive sequences, is integrated into our two-stage diffusion architecture, substituting off-the-shelf Transformer. Additionally, considering the critical role of musical beats in dance choreography, we propose a Gaussian-based beat representation to explicitly guide the decoding of dance sequences. Experiments on AIST++ and FineDance datasets for each sequence length 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. Additional qualitative results and demo videos are available at \small{https://vision3d-lab.github.io/mambadance}.
Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机科学学院，北京大学）；UCAS-Terminus AI Lab, University of Chinese Academy of Sciences（中国科学院大学Terminus AI实验室，中国科学院大学）；School of Intelligent Science and Technology, Nanjing University（智能科学与技术学院，南京大学）；Department of Information Technology and Electrical Engineering, ETH Zurich（信息科技与电气工程系，苏黎世联邦理工学院）；Department of Electrical Engineering, KU Leuven（电气工程系，鲁汶大学）；INSAIT, Sofia Un.（INSAIT，索菲亚大学）；Department of Information Engineering and Computer Science (DISI), University of Trento（信息工程与计算机科学系（DISI），特伦托大学）
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）
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, …
University of California, Merced（加利福尼亚大学默塞德分校）；NVIDIA（英伟达公司）
Dancing to music is an instinctive move by humans. Learning to model the music-to-dance generation process is, however, a challenging problem. It requires significant efforts to measure the correlation between music and dance as one needs to simultaneously consider multiple aspects, such as style and beat of both music and dance. Additionally, dance is inherently multimodal and various following movements of a pose at any moment are equally likely. In this paper, we propose a synthesis-by-analysis learning framework to generate dance from music. In the analysis phase, we decompose a dance into…
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…
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）
We present a learning-based approach with pose perceptual loss for automatic music video generation. Our method can produce a realistic dance video that conforms to the beats and rhymes of almost any given music. To achieve this, we firstly generate a human skeleton sequence from music and then apply the learned pose-to-appearance mapping to generate the final video. In the stage of generating skeleton sequences, we utilize two discriminators to capture different aspects of the sequence and propose a novel pose perceptual loss to produce natural dances. Besides, we also provide a new cross-mod…
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）
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.,…
Close your eyes and listen to music, one can easily imagine an actor dancing rhythmically along with the music. These dance movements are usually made up of dance movements you have seen before. In this paper, we propose to reproduce such an inherent capability of the human-being within a computer vision system. The proposed system consists of three modules. To explore the relationship between music and dance movements, we propose a cross-modal alignment module that focuses on dancing video clips, accompanied on pre-designed music, to learn a system that can judge the consistency between the v…
EgoMusic-driven Human Dance Motion Estimation with Skeleton Mamba
FPT Software AI Center（FPT软件AI中心）；The University of Western Australia（西澳大学）；TU Wien（维也纳技术大学）；Meta；University of Arkansas（阿肯色大学）；National University of Singapore（新加坡国立大学）；University of Liverpool（利物浦大学）
BeatDance: Generating Beat-Consistent 3D Dance with Hierarchical Spatial-Temporal Modeling
College of Software, Jilin University（吉林大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ReLER, AAII, University of Technology Sydney（悉尼科技大学AAII ReLER实验室）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；School of Information and Communication Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）；School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University（深圳大学医学部生物医学工程学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）
ReactionMamba: Generating Short & Long Human Reaction Sequences
Univ. Lille, CNRS, Centrale Lille, Institut Mines-Télécom, UMR 9189 CRIStAL（里尔大学）；IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, Centre for Digital Systems（IMT北欧，矿电信学院，里尔大学，数字系统中心）；da/sec – Biometrics and Security Research Group, Hochschule Darmstadt（da/sec生物识别与安全研究组，达姆施塔特大学）；Peking University（北京大学）
