Hasil untuk "Dancing"

A. Yerkes

Andreas Wieland

Most of the theories that have dominated supply chain management (SCM) take a reductionist and static view on the supply chain and its management, promoting a global hunt for cheap labor and resources. As a result, supply chains tend to be operated without much concern for their broader contextual environment. This perspective overlooks that supply chains have become both vulnerable and harmful systems. Recent and ongoing crises have emphasized that the structures and processes of supply chains are fluid and interwoven with political‐economic and planetary phenomena. Building on panarchy theory, this article reinterprets the supply chain as a social–ecological system and leaves behind a modernist view of SCM, replacing it with a more contemporary vision of “dancing the supply chain.” A panarchy is a structure of adaptive cycles that are linked across different levels on scales of time, space, and meaning. It represents the world’s complexities more effectively than reductionist and static theories ever could, providing the basis for transformative SCM.

Leanne R. Simpson, Leanne R. Simpson, Gaawiin Nda-gajsii

Mark Franko

Introduction 1. The Invention of Modern Dance 2. Bodies of Radical Will 3. Emotivist Movement and Histories of Modernism: The Case of Martha Graham 4. Expressivism and Chance Procedure: The Future of an Emotion 5. Where He Danced Appendix: Left-Wing Dance Theory: Articles on dance from New Theatre, New Masses, and Daily Worker Notes Bibliography Index

J. M. Serrano, Orestis Papakyriakopoulos, Simon Hegelich

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world’s second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.

Peizhuo Li, Sebastian Starke, Yuting Ye et al.

Ballroom dancing is a structured yet expressive motion category. Its highly diverse movement and complex interactions between leader and follower dancers make the understanding and synthesis challenging. We demonstrate that the three-point trajectory available from a virtual reality (VR) device can effectively serve as a dancer's motion descriptor, simplifying the modeling and synthesis of interplay between dancers' full-body motions down to sparse trajectories. Thanks to the low dimensionality, we can employ an efficient MLP network to predict the follower's three-point trajectory directly from the leader's three-point input for certain types of ballroom dancing, addressing the challenge of modeling high-dimensional full-body interaction. It also prevents our method from overfitting thanks to its compact yet explicit representation. By leveraging the inherent structure of the movements and carefully planning the autoregressive procedure, we show a deterministic neural network is able to translate three-point trajectories into a virtual embodied avatar, which is typically considered under-constrained and requires generative models for common motions. In addition, we demonstrate this deterministic approach generalizes beyond small, structured datasets like ballroom dancing, and performs robustly on larger, more diverse datasets such as LaFAN. Our method provides a computationally- and data-efficient solution, opening new possibilities for immersive paired dancing applications. Code and pre-trained models for this paper are available at https://peizhuoli.github.io/dancing-points.

Éden Peretta

The research at the heart of this essay began in 2018 and gradually gained depth as the creative processes underlying it were updated in various pedagogical and artistic contexts. The methodological proposal for choreographic-dramaturgical creation that was arrived at is based on a critical and negative use of images, i.e. on the destabilisation of their forms and the deconstruction of their “figurability”, rather than on their affirmation in a mimetic perspective. The research was initially inspired by the poetics and procedures used by the creator of butoh dance, Tatsumi Hijikata, and their potential intersections with the universe of image philosophy, in particular with the contributions of philosopher Georges Didi-Huberman and his dialogue with the works of Aby Warburg and Georges Bataille. The aim of this article is, therefore, to present some of the conceptual principles of this methodology, as well as to report on some practical creative experiences in which what I have defined as the “choreo-dramaturgical atlas” has been applied and developed in more complex forms in recent years, both in the undergraduate and postgraduate courses I have taught and in the creative process of the university artistic research group I coordinate.

David Sinclair, Ademyemi Ademola, Babis Koniaris et al.

DanceGraph is an architecture for synchronized online dancing overcoming the latency of networked body pose sharing. We break down this challenge by developing a real-time bandwidth-efficient architecture to minimize lag and reduce the timeframe of required motion prediction for synchronization with the music's rhythm. In addition, we show an interactive method for the parameterized stylization of dance motions for rhythmic dance using online dance correctives.

Miguel O'Malley

\textit{Dance Dance Revolution} is a rhythm game 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. In 2017, the authors of Dance Dance Convolution (DDC) developed an algorithm for the automatic generation of \textit{Dance Dance Revolution} charts, utilizing a CNN-LSTM architecture. We introduce Dance Dance ConvLSTM (DDCL), a new method for the automatic generation of DDR charts using a ConvLSTM based model, which improves upon the DDC methodology and substantially increases the accuracy of chart generation.

Caterina Feletti, Paola Flocchini, Debasish Pattanayak et al.

The Dancing problem requires a swarm of $n$ autonomous mobile robots to form a sequence of patterns, aka perform a choreography. Existing work has proven that some crucial restrictions on choreographies and initial configurations (e.g., on repetitions of patterns, periodicity, symmetries, contractions/expansions) must hold so that the Dancing problem can be solved under certain robot models. Here, we prove that these necessary constraints can be dropped by considering the LUMI model (i.e., where robots are endowed with a light whose color can be chosen from a constant-size palette) under the quite unexplored sequential scheduler. We formalize the class of Universal Dancing problems which require a swarm of $n$ robots starting from any initial configuration to perform a (periodic or finite) sequence of arbitrary patterns, only provided that each pattern consists of $n$ vertices (including multiplicities). However, we prove that, to be solvable under LUMI, the length of the feasible choreographies is bounded by the compositions of $n$ into the number of colors available to the robots. We provide an algorithm solving the Universal Dancing problem by exploiting the peculiar capability of sequential robots to implement a distributed counter mechanism. Even assuming non-rigid movements, our algorithm ensures spatial homogeneity of the performed choreography.

Ryo Watanabe, Chenhao Li, Marco Hutter

As entertainment robots gain popularity, the demand for natural and expressive motion, particularly in dancing, continues to rise. Traditionally, dancing motions have been manually designed by artists, a process that is both labor-intensive and restricted to simple motion playback, lacking the flexibility to incorporate additional tasks such as locomotion or gaze control during dancing. To overcome these challenges, we introduce Deep Fourier Mimic (DFM), a novel method that combines advanced motion representation with Reinforcement Learning (RL) to enable smooth transitions between motions while concurrently managing auxiliary tasks during dance sequences. While previous frequency domain based motion representations have successfully encoded dance motions into latent parameters, they often impose overly rigid periodic assumptions at the local level, resulting in reduced tracking accuracy and motion expressiveness, which is a critical aspect for entertainment robots. By relaxing these locally periodic constraints, our approach not only enhances tracking precision but also facilitates smooth transitions between different motions. Furthermore, the learned RL policy that supports simultaneous base activities, such as locomotion and gaze control, allows entertainment robots to engage more dynamically and interactively with users rather than merely replaying static, pre-designed dance routines.

Carolina Are

ABSTRACT This paper contributes to the social media moderation research space by examining the still under-researched “shadowban”, a form of light and secret censorship targeting what Instagram defines as borderline content, particularly affecting posts depicting women’s bodies, nudity and sexuality. “Shadowban” is a user-generated term given to the platform’s “vaguely inappropriate content” policy, which hides users’ posts from its Explore page, dramatically reducing their visibility. While research has already focused on algorithmic bias and on social media moderation, there are not, at present, studies on how Instagram’s shadowban works. This autoethnographic exploration of the shadowban provides insights into how it manifests from a user’s perspective, applying a risk society framework to Instagram’s moderation of pole dancing content to show how the platform’s preventive measures are affecting user rights.

Vongani Maluleke, Lea Müller, Jathushan Rajasegaran et al.

This paper asks to what extent social interaction influences one's behavior. We study this in the setting of two dancers dancing as a couple. We first consider a baseline in which we predict a dancer's future moves conditioned only on their past motion without regard to their partner. We then investigate the advantage of taking social information into account by conditioning also on the motion of their dancing partner. We focus our analysis on Swing, a dance genre with tight physical coupling for which we present an in-the-wild video dataset. We demonstrate that single-person future motion prediction in this context is challenging. Instead, we observe that prediction greatly benefits from considering the interaction partners' behavior, resulting in surprisingly compelling couple dance synthesis results (see supp. video). Our contributions are a demonstration of the advantages of socially conditioned future motion prediction and an in-the-wild, couple dance video dataset to enable future research in this direction. Video results are available on the project website: https://von31.github.io/synNsync

Hyemin Ahn

We hypothesize dance as a motion that forms a visual rhythm from music, where the visual rhythm can be perceived from an optical flow. If an agent can recognize the relationship between visual rhythm and music, it will be able to dance by generating a motion to create a visual rhythm that matches the music. Based on this, we propose a framework for any kind of non-humanoid agents to learn how to dance from human videos. Our framework works in two processes: (1) training a reward model which perceives the relationship between optical flow (visual rhythm) and music from human dance videos, (2) training the non-humanoid dancer based on that reward model, and reinforcement learning. Our reward model consists of two feature encoders for optical flow and music. They are trained based on contrastive learning which makes the higher similarity between concurrent optical flow and music features. With this reward model, the agent learns dancing by getting a higher reward when its action creates an optical flow whose feature has a higher similarity with the given music feature. Experiment results show that generated dance motion can align with the music beat properly, and user study result indicates that our framework is more preferred by humans compared to the baselines. To the best of our knowledge, our work of non-humanoid agents which learn dance from human videos is unprecedented. An example video can be found at https://youtu.be/dOUPvo-O3QY.

Dadong Liu, Lai Chen, Li-Gang Wang

Vortex dynamics, which encompass the motion, evolution, and propagation of vortices, elicit both fascination and challenges across various domains such as fluid dynamics, atmospheric science, and physics. This study focuses on fundamental dynamics of vortex-pair fields, specifically known as vortex-pair beams (VPBs) in optics. VPBs have gained increasing attention due to their unique properties, including vortex attraction and repulsion. Here, we explore the dynamics of pure-phase VPBs (PPVPBs) and observe intriguing helical and intertwined behaviors of vortices, resembling a vortex-pair dance. We uncover the oscillation property of the intervortex distance for PPVPBs in free space. The observed dancing and oscillation phenomena are intricately tied to the initial intervortex distance and can be explained well in the hydrodynamic picture. Notably, the vortex dancing and oscillation alter the process of vortex-pair annihilation, extending the survival range for opposite vortices. This discovery enhances our understanding of vortex interactions and sheds light on the intricate dynamics of both vortex-vortex and vortex-antivortex interactions.

T. Simeonova, Y. Yankov, N. Nikolova

The teaching of folk dances and dances in the 4th grade is included in the compulsory area of educational content in the Bulgarian school. After conducting an innovative program in the teaching of folk dances, the physical capacity of students were determined by accomplishing seven tests. The study found that dancing exerts a positive effect on the development of children's physical qualities. The correlations between the indicators of physical capacity of students proved the effectiveness of the proposed program.

Bhuvaneswari Sarupuri, R. Kulpa, A. Aristidou et al.

Virtual reality (VR) has recently seen significant development in interaction with computers and the visualization of information. More and more people are using virtual and immersive technologies in their daily lives, especially for entertainment, fitness, and socializing purposes. This paper presents a qualitative evaluation of a large sample of users using a VR platform for dancing ( $$N=292$$ N = 292 ); we study the users’ motivations, experiences, and requirements for using VR as an inclusive platform for dancing, mainly as a social or physical activity. We used an artificial intelligence platform (OpenAI) to extract categories or clusters of responses automatically. We organized the data into six user motivation categories: fun, fitness, social activity, pandemic, escape from reality, and professional activities. Our results indicate that dancing in virtual reality is a different experience than in the real world, and there is a clear distinction in the user’s motivations for using VR platforms for dancing. Our survey results suggest that VR is a tool that can positively impact physical and mental well-being through dancing. These findings complement the related work, help in identifying the use cases, and can be used to assist future improvements of VR dance applications.

Jinwoo Kim, Heeseok Oh, S. Kim et al.

When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres. Therefore, they tend to return certain patterns of the dance genres that they are familiar with. What if artificial intelligence could be used to help choreographers blend dance genres by suggesting various dances, and one that matches their choreographic style? Numerous task-specific variants of autoregressive networks have been developed for dance generation. Yet, a serious limitation remains that all existing algorithms can return repeated patterns for a given initial pose sequence, which may be inferior. To mitigate this issue, we propose MNET, a novel and scalable approach that can perform music-conditioned pluralistic dance generation synthesized by multiple dance genres using only a single model. Here, we learn a dancegenre aware latent representation by training a conditional generative adversarial network leveraging Transformer architecture. We conduct extensive experiments on AIST++ along with user studies. Compared to the state-of-the-art methods, our method synthesizes plausible and diverse outputs according to multiple dance genres as well as generates outperforming dance sequences qualitatively and quantitatively.

Tomoya Sasaki, Narin Okazaki, Takatoshi Yoshida et al.

We propose SolefulTap for a novel tap dancing experience. It allows users to feel as if they are tap dancing or appreciate a tap dancing performance using the sensations of their own feet. SolefulTap uses a method called Step Augmentation that provides audio-haptic feedback to users, generating impacts in response to users' simple step motions. Our prototype uses a floor-type impact display consisting of pressure sensors, which detect users' steps, and solenoids, which generate feedback through impact. Through a preliminary user study, we confirmed that the system can provide untrained users with the experience of tap dancing. This study serves as a case study that provides insight into how a reactive environment can affect the human capabilities of physical expression and the sensation experienced.

Gil Bor, Luis Hernández Lamoneda

A pair of planar polygons is "dancing" if one is inscribed in the other and they satisfy a certain cross-ratio relation at each vertex of the circumscribing polygon. Non-degenerate dancing pairs of closed $n$-gons exist for all $n\geq 6$. Dancing pairs correspond to trajectories of a non-holonomic mechanical system, consisting of a ball rolling, without slipping and twisting, along a polygon drawn on the surface of a ball 3 times larger than the rolling ball. The correspondence stems from reformulating both systems as piecewise rigid curves of a certain remarkable rank 2 non-integrable distribution defined on a 5-dimensional quadric in $\mathbb{RP}^6$, introduced by É. Cartan and F. Engel in 1893 in order to define the simple Lie group $\mathrm{G}_2$.