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arXiv · 2502.01190

Dance recalibration for dance coherency with recurrent convolution block

Seungho Eum·Ihjoon Cho·Junghyeon Kim
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Abstract

With the recent advancements in generative AI such as GAN, Diffusion, and VAE, the use of generative AI for dance generation has seen significant progress and received considerable interest. In this study, We propose R-Lodge, an enhanced version of Lodge. R-Lodge incorporates Recurrent Sequential Representation Learning named Dance Recalibration to original coarse-to-fine long dance generation model. R-Lodge utilizes Dance Recalibration method using $N$ Dance Recalibration Block to address the lack of consistency in the coarse dance representation of the Lodge model. By utilizing this method, each generated dance motion incorporates a bit of information from the previous dance motions. We evaluate R-Lodge on FineDance dataset and the results show that R-Lodge enhances the consistency of the whole generated dance motions.

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Seungho Eum, Ihjoon Cho, Junghyeon Kim. 2025-02-03. Dance recalibration for dance coherency with recurrent convolution block. https://arxiv.org/abs/2502.01190

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