用循环卷积块提升舞蹈生成连贯性，解决动作不一致问题。
Dance recalibration for dance coherency with recurrent convolution block
- 引入循环舞蹈重校准机制，逐帧融合历史动作信息。
- 在FineDance数据集上显著提升整体舞蹈动作一致性。
- 适合需要流畅连贯舞蹈生成的研究者与开发者。
随着生成式AI（如GAN、扩散模型、VAE）的进展，舞蹈生成领域取得了显著突破并受到广泛关注。本文提出R-Lodge，即Lodge模型的改进版本，通过引入名为舞蹈重校准（Dance Recalibration）的循环序列表征学习方法，增强原始粗粒度到细粒度的长时舞蹈生成模型。R-Lodge采用$N$个舞蹈重校准模块，旨在解决原Lodge模型在粗粒度舞蹈表示中存在的连贯性不足问题。该方法使每一段生成的动作均融入前序动作的信息，从而提升整体动作流的连贯性。在FineDance数据集上的评估表明，R-Lodge有效增强了生成舞蹈动作的整体一致性。
原文摘要 · Abstract (English)
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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