DFM: Deep Fourier Mimic for Expressive Dance Motion Learning
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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.new traditional dance must be a new direction emerged in the 21st Century. In the history of dance, artistic dance has seen new expression methods unfold according to the times. It''s meaningful that a new type of artistic dance emerged at this moment when more than 100 years have passed since the Korean dance circle had started the artistic dance. Apart from this, new traditional dance can make traditional dance in the past into a more evolved form, paving the way for the advancement of traditional dance in the future. And so, new traditional dance could be an ideal dance activity allowing us to connect between the past, the present and the future. Further discussion of new traditional dance for its development is needed and, the study believes, activation of new traditional dance from the further evolved perspective may have a positive effect on the development of the country''s dance world.
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The dancer in training recording dance - walking boldly backwards into the future the constituent features of dance - movement and dancers the constituent features of dance - the physical setting the constituent features of dance - the aural setting form in dance dance appreciation dance today the way ahead.
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There are shown basic drawbacks of the well-known factors of manufacturability. The formulae of manufacturability factors excluding mentioned drawbacks and taking into account the impact of product design characteristics upon complete laboriousness of its manufacturing are offered. The developed formulae of these manufacturability factors give possibility for obtaining an integral estimate of manufacturability by means of their summation.
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We describe a method that generates dance motions with human emotions from motion-capture data. To generate the dance motions, we developed an emotional motion editor (EME). The EME adds human emotions to the dance motions by modifying the original motion-capture data interactively: for instance, by changing the speed of motion or by altering the joint angles. To evaluate the emotional expressions in the dance motions generated by EME, we performed an assessment experiment by conducting a questionnaire survey, and we examined the results with the statistical t-test. As a result, we confirmed that the dance motions with human emotions are obtainable on the EME by just adjusting a few of its parameters.
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