Hasil untuk "Dancing"

N. McCarty, K. Poole, H. Rosenthal

J. Huizinga

K. Ericsson, A. Lehmann

Sarah Thornton

B. Repp

D. Harvey, F. Lerdahl, Ray Jackendoff

P. Senge, A. Kleiner, C. Roberts et al.

Valerie J. Janesick

R. Morse

T. Seeley

Matt Brzycki

B. Calvo-Merino, J. Grèzes, D. Glaser et al.

Susan M Johnson

Kuniaki Saito, Donghyun Kim, S. Sclaroff et al.

Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap between the two domains. While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori. We propose a more universally applicable domain adaptation approach that can handle arbitrary category shift, called Domain Adaptative Neighborhood Clustering via Entropy optimization (DANCE). DANCE combines two novel ideas: First, as we cannot fully rely on source categories to learn features discriminative for the target, we propose a novel neighborhood clustering technique to learn the structure of the target domain in a self-supervised way. Second, we use entropy-based feature alignment and rejection to align target features with the source, or reject them as unknown categories based on their entropy. We show through extensive experiments that DANCE outperforms baselines across open-set, open-partial and partial domain adaptation settings.

Wenlin Zhuang, Congyi Wang, Jinxiang Chai et al.

Synthesize human motions from music (i.e., music to dance) is appealing and has attracted lots of research interests in recent years. It is challenging because of the requirement for realistic and complex human motions for dance, but more importantly, the synthesized motions should be consistent with the style, rhythm, and melody of the music. In this article, we propose a novel autoregressive generative model, DanceNet, to take the style, rhythm, and melody of music as the control signals to generate 3D dance motions with high realism and diversity. Due to the high long-term spatio-temporal complexity of dance, we propose the dilated convolution to improve the receptive field, and adopt the gated activation unit as well as separable convolution to enhance the fusion of motion features and control signals. To boost the performance of our proposed model, we capture several synchronized music-dance pairs by professional dancers and build a high-quality music-dance pair dataset. Experiments have demonstrated that the proposed method can achieve state-of-the-art results.

Kehong Gong, Dongze Lian, Heng Chang et al.

We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities. Unlike existing works that generate dance movements using a single modality such as music, our goal is to produce richer dance movements guided by the instructive information provided by the text. However, the lack of paired motion data with both music and text modalities limits the ability to generate dance movements that integrate both. To alleviate this challenge, we propose to utilize a 3D human motion VQ-VAE to project the motions of the two datasets into a latent space consisting of quantized vectors, which effectively mix the motion tokens from the two datasets with different distributions for training. Additionally, we propose a cross-modal transformer to integrate text instructions into motion generation architecture for generating 3D dance movements without degrading the performance of music-conditioned dance generation. To better evaluate the quality of the generated motion, we introduce two novel metrics, namely Motion Prediction Distance (MPD) and Freezing Score (FS), to measure the coherence and freezing percentage of the generated motion. Extensive experiments show that our approach can generate realistic and coherent dance movements conditioned on both text and music while maintaining comparable performance with the two single modalities. Code is available at https://garfield-kh.github.io/TM2D/.

Solomon Gwerevende, Z. Mthombeni

ABSTRACT Like other forms of Intangible Cultural Heritage (ICH), Indigenous music and dance cultures have been adversely affected by significant social, economic, technological, and ecological modifications. The resultant transformations in cultural contexts, function, modes of transmission, and performance have endangered the sustainability of several music and dance traditions and their transmission languages. Moreover, efforts to actively support the vitality of jeopardised cultural heritage are being developed and implemented in the emerging fields of applied ethnomusicology, ethnochoreology and linguistics. The area of Indigenous language safeguarding has theoretical, epistemological, and practical models comparable to safeguarding Indigenous music and dance traditions. This similarity is essential to developing interdisciplinary models, policies, and strategies to support the transmission of Indigenous choreomusical and linguistic heritage. Therefore, this article demonstrates how Indigenous music, dance, and language are integral to African cultural heritage and argues for an interdisciplinary community-based model to safeguard them as part of the same cultural ecosystem.

Ronghui Li, Junfan Zhao, Yachao Zhang et al.

Generating full-body and multi-genre dance sequences from given music is a challenging task, due to the limitations of existing datasets and the inherent complexity of the fine-grained hand motion and dance genres. To address these problems, we propose FineDance, which contains 14.6 hours of music-dance paired data, with fine-grained hand motions, fine-grained genres (22 dance genres), and accurate posture. To the best of our knowledge, FineDance is the largest music-dance paired dataset with the most dance genres. Additionally, to address monotonous and unnatural hand movements existing in previous methods, we propose a full-body dance generation network, which utilizes the diverse generation capabilities of the diffusion model to solve monotonous problems, and use expert nets to solve unreal problems. To further enhance the genre-matching and long-term stability of generated dances, we propose a Genre&Coherent aware Retrieval Module. Besides, we propose a novel metric named Genre Matching Score to evaluate the genre-matching degree between dance and music. Quantitative and qualitative experiments demonstrate the quality of FineDance, and the state-of-the-art performance of FineNet. The FineDance Dataset and more qualitative samples can be found at website.

Motoyoshi Kobayashi, Takuya Sugimoto, Ryoichi Ishibashi et al.

Concentrated suspensions exhibit intriguing behaviors under external forces, including vibration and shear. While previous studies have focused primarily on cornstarch suspensions, this paper reports a novel observation that colloidal silica suspensions also exhibit dancing, bouncing, solidification, and melting under vertical vibration. Unlike cornstarch, silica particles offer high stability, controlled size distribution, and tunable surface properties, making them an ideal system for investigating these phenomena. The 70 wt.% aqueous suspensions of spherical silica particles with a diameter of 0.55 μm were subjected to controlled vertical vibration (60–100 Hz, 100–500 m/s²). High-speed video analysis revealed dynamic transitions, including melting, fingering, squirming, fragmentation, and jumping. The solidified suspension retained its shape after vibration ceased but melted upon weak vibration. This study demonstrates that such dynamic state transitions are not exclusive to starch-based suspensions but can also occur in well-defined colloidal suspensions. Our findings provide a new platform for investigating shear-thickening, jamming, and vibrational solidification in suspensions with controllable parameters. Further work is required to elucidate the underlying mechanisms.

Mingyu YAN, Fei WANG

ObjectiveUrban parks play a vital role in enhancing residents’ physical and mental well-being and offering leisure opportunities. Their vitality has become a crucial indicator of urban spatial quality and public welfare. Rapid urbanization has further intensified the imbalance in the allocation of public service resources. Existing research, which primarily relies on heat maps, mobile signaling data, or ground-based camera monitoring, can reveal macroscopic trends but fail to capture the dynamic spatiotemporal characteristics of crowd distribution at the micro scale. Meanwhile, aerial photography obtained through unmanned aerial vehicle (UAV) offers high spatial resolution and flexible data acquisition capabilities, while the advancement of object detection algorithms based on deep learning presents new technological opportunities for crowd recognition in complex urban environments. This research aims to develop and validate a micro-scale vitality measurement method for urban parks based on aerial time-series imagery and an improved object detection model. The method seeks to reveal the spatiotemporal patterns of crowd distribution, identify high-frequency vitality nodes and their driving mechanisms, and provide data support and strategic insights for optimizing the spatial layout, facility allocation, and refined management of parks. Taking Xi’an Xingfu Linear Park as an example, the research focuses on analyzing vitality intensity, fluctuation, and spatial balance at a fine spatiotemporal scale.MethodsBetween March 27 and 30, 2025, continuous UAV-based aerial photography was conducted at a fixed altitude of 75 m during six standard time periods (08:00, 10:00, 12:00, 14:00, 16:00, 18:00), yielding over 2,300 high-resolution images. A manually annotated dataset of 2,000 sub-images with 12,340 pedestrian instances is constructed for model training. To address challenges of small-scale targets and complex occlusions in aerial imagery, an enhanced YOLO11m-CBAM model is developed by embedding a convolutional block attention module (CBAM) into YOLO11m. The improved model achieves notable performance gains: mAP50 increases from 77.1% to 81.3%, mAP50–95 from 45.6% to 51.7%, with precision and recall reaching 86.4% and 72.0% respectively, demonstrating enhanced robustness under medium and low occlusion conditions. Detection outputs are orthorectified to geographic coordinates to construct a structured spatiotemporal dataset. Spatial analysis employs kernel density estimation, coefficient of variation (CV), spatial Gini coefficient, and the “latitude-population” curve to characterize multidimensional vitality patterns.ResultsThe temporal analysis results indicate that the overall utilization of Xingfu Forest Belt exhibits a distinct “dual-peak” pattern. On rest days, the number of visitors reaches 2,112 at 10:00 and 3,641 at 16:00, reflecting typical peaks of family and leisure activities. The daily coefficient of variation (CV = 38.67%) is relatively low, suggesting stable visiting patterns with activity concentrated in leisure hours. In contrast, on working days, vitality peaks occur at 10:00 and 18:00, corresponding to post-commuting and after-work relaxation periods, respectively. The higher daily visiting (CV = 55.34%) indicates a more uneven temporal distribution of activities. Notably, 12:00 represents the lowest point of visiting (the minimum number of visitors is only 595, and the average number is 883), implying underutilization of space during midday and suggesting potential opportunities for future facility optimization or time-specific programming. The spatial equilibrium analysis further reveals that during peak hours (14:00 and 16:00), the spatial Gini coefficient reaches 0.44 – 0.48, indicating a strong concentration of vitality in specific functional zones and a pronounced spatial polarization effect. In contrast, the Gini coefficient drops to 0.24 during off-peak periods (08:00 and 12:00), reflecting a more dispersed and evenly distributed use of space. At 18:00, the Gini coefficient remains between 0.38 and 0.41, suggesting a moderate level of aggregation in the evening. Overall, the vitality of Xingfu Forest Belt demonstrates a dynamic pattern of “daytime polarization with evening recovery”. In terms of spatial distribution, vitality hotspots are primarily concentrated along the central and northern segments of the belt, forming localized peaks. The emergence of these core areas is driven by two main factors: 1) the attraction of fixed functional facilities such as children’s play areas, fitness zones, and square-dancing spaces; and 2) the temporal aggregation generated by periodic activities, including weekend family events and morning exercise. At the macro scale, the concentration of residential and educational land uses, high accessibility to bus stops, and the scarcity of comparable recreational facilities jointly reinforce the sustained vitality of the central children’s play area. Maintaining consistently high footfall and strong spatial spillover effects across multiple time periods, this area serves as a key vitality hub within the overall spatial structure of Xingfu Forest Belt.ConclusionThe research demonstrates that the proposed UAV-based and YOLO-based vitality measurement framework provides high spatiotemporal resolution at the micro-park scale, enabling accurate identification of vitality hotspots, temporal fluctuations, and spatial imbalances. This approach offers an operational, quantitative basis for optimizing facility layouts, designing flexible spaces, and implementing differentiated management strategies. Methodological limitations are also discussed: The approach performs reliably in spring, autumn, and winter with low to moderate vegetation coverage, but may encounter partial omissions under dense canopy or multi-layer pergola structures in summer. To enhance applicability, future improvements include multi-drone and multi-view data acquisition, infrared thermal imaging to mitigate occlusion, air-ground data fusion, inter-frame trajectory matching to distinguish stay/pass behaviors, and fine-grained activity recognition. Overall, the proposed method provides a replicable technical pathway and empirical reference for refined park governance and smart park development. The findings contribute to advancing quantitative urban vitality assessment and provide methodological insights for integrating AI and spatial analysis in urban landscape research.