arxiv: 2601.03323 · v3 · submitted 2026-01-06 · 💻 cs.GR · cs.CV· cs.HC· cs.LG· cs.SD

Recognition: no theorem link

Listen to Rhythm, Choose Movements: Autoregressive Multimodal Dance Generation via Diffusion and Mamba with Decoupled Dance Dataset

Oran Duan , Yinghua Shen , Yingzhu Lv , Luyang Jie , Yaxin Liu , Qiong Wu

Authors on Pith no claims yet

Pith reviewed 2026-05-16 16:35 UTC · model grok-4.3

classification 💻 cs.GR cs.CVcs.HCcs.LGcs.SD

keywords dance generationdiffusion modelsMambamultimodalautoregressivefeature decouplingmotion synthesis

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The pith

Diffusion model with Mamba generates coherent long dance sequences from audio and text inputs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes LRCM, a multimodal diffusion framework for generating dance motions autoregressively. It decouples dance datasets into motion, audio rhythm, and text descriptions to enhance semantic control. The model uses an audio-latent Conformer, a text-latent Cross-Conformer, and a Motion Temporal Mamba Module to handle long sequences smoothly. This approach aims to overcome coarse control and poor coherence in existing dance generation methods. A sympathetic reader would care because it could lead to more realistic and controllable dance animations from natural inputs.

Core claim

LRCM presents a multimodal-guided diffusion framework that supports diverse input modalities and autoregressive dance motion generation. By exploring a feature decoupling paradigm generalized to the Motorica Dance dataset, it separates motion capture data, audio rhythm, and annotated text descriptions. The architecture integrates audio-latent Conformer and text-latent Cross-Conformer with the Motion Temporal Mamba Module to enable smooth, long-duration synthesis.

What carries the argument

The feature decoupling paradigm combined with the Motion Temporal Mamba Module (MTMM) in a diffusion architecture, which processes latents from audio and text to produce coherent motion sequences.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Potential extension to real-time interactive systems where users provide audio or text cues for dance.
Applicability of Mamba modules to other long-sequence generative tasks in graphics.
Opportunity to test the framework on additional datasets beyond Motorica for validation.

Load-bearing premise

The combination of feature decoupling with the audio and text Conformer modules and the Mamba temporal module will produce better semantic control and long-sequence coherence in dance generation.

What would settle it

Quantitative results where LRCM shows no significant improvement over baseline methods in metrics for long sequence coherence or semantic alignment with inputs.

Figures

Figures reproduced from arXiv: 2601.03323 by Luyang Jie, Oran Duan, Qiong Wu, Yaxin Liu, Yinghua Shen, Yingzhu Lv.

**Figure 1.** Figure 1: A conceptual overview of the proposed LRCM framework, showing the decoupling of the text modality from the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Figure 2: Example of decoupled textual modality annotations [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of the proposed architecture. The main generation model is constructed using a DiT backbone. Text inputs [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Internal structure of the denoising residual block. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Motion Temporal Mamba Module (MTMM) architecture and process. Latent features from past motion memory and [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Full LRCM model training strategy showing three [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: Qualitative comparison of generated results across [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: Phase 2 results after fine-tuning with local text. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

**Figure 10.** Figure 10: Noise scheduler 𝛽 and 𝛼 curves. Blue: baseline LDA configuration; Red: experimental setting used in this work; other curves: alternative reference. Noise Scheduler Analysis. We also investigate the effect of the noise scheduler configuration. Using the baseline LDA with DDPM parameters 𝛽 ∈ [0.01, 0.7] and a linear schedule with 150 diffusion steps, we observe that larger 𝛽 values prematurely push the mode… view at source ↗

**Figure 11.** Figure 11: Token statistics for all dance styles in the Motorica [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗

**Figure 12.** Figure 12: Top eight most frequent semantic tokens for each [PITH_FULL_IMAGE:figures/full_fig_p011_12.png] view at source ↗

**Figure 13.** Figure 13: Summary of all dance action tokens across the [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗

read the original abstract

Advances in generative models and sequence learning have greatly promoted research in dance motion generation, yet current methods still suffer from coarse semantic control and poor coherence in long sequences. In this work, we present Listen to Rhythm, Choose Movements (LRCM), a multimodal-guided diffusion framework supporting both diverse input modalities and autoregressive dance motion generation. We explore a feature decoupling paradigm for dance datasets and generalize it to the Motorica Dance dataset, separating motion capture data, audio rhythm, and professionally annotated global and local text descriptions. Our diffusion architecture integrates an audio-latent Conformer and a text-latent Cross-Conformer, and incorporates a Motion Temporal Mamba Module (MTMM) to enable smooth, long-duration autoregressive synthesis. Experimental results indicate that LRCM delivers strong performance in both functional capability and quantitative metrics, demonstrating notable potential in multimodal input scenarios and extended sequence generation. The project page is available at https://oranduanstudy.github.io/LRCM/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LRCM adds a Mamba module to diffusion for autoregressive multimodal dance generation and decouples the Motorica dataset, with internally consistent results but thin baseline comparisons.

read the letter

The main point is that LRCM combines a diffusion backbone with an audio-latent Conformer, a text-latent Cross-Conformer, and a Motion Temporal Mamba Module to generate longer dance sequences from rhythm and text inputs. They also split the Motorica dataset into separate motion, audio, and annotated text streams, which lets the model handle multimodal control more directly than prior end-to-end approaches. The architecture description and training protocol are clear enough that the reported gains in coherence and semantic alignment follow from the components they describe. The Mamba choice makes sense for long sequences because it avoids the quadratic cost of attention while still modeling temporal dependencies autoregressively. The dataset decoupling is a practical step that generalizes an existing idea and supports the multimodal claims without obvious circularity. The soft spots are mostly in the evaluation. The paper shows positive metrics, but the comparisons to recent diffusion or transformer baselines are not detailed enough to judge how large the actual lift is, and there are no error bars or extensive ablations on the Mamba module alone. That leaves some uncertainty about whether the improvements are robust or partly tied to the specific dataset splits. This work is aimed at people building generative motion models for animation, VR, or interactive media. A reader who needs concrete multimodal control and long-sequence handling will get usable ideas from the implementation choices. It is worth sending to peer review because the technical pieces are grounded and the results can be checked against the described protocol.

Referee Report

2 major / 2 minor

Summary. The manuscript presents LRCM, a multimodal-guided diffusion framework for autoregressive dance motion generation. It proposes a feature decoupling paradigm for dance datasets (generalized to Motorica), separating motion capture, audio rhythm, and global/local text annotations. The architecture combines an audio-latent Conformer, text-latent Cross-Conformer, and Motion Temporal Mamba Module (MTMM) to support diverse modalities and long-sequence synthesis.

Significance. If the reported gains hold, the work would advance dance generation by improving semantic control via decoupled features and long-sequence coherence via Mamba-based temporal modeling, with clear relevance to animation and VR applications. The explicit decoupling and autoregressive diffusion setup are strengths that could be built upon.

major comments (2)

[§5] §5 (Experimental Results): The abstract and main text claim 'strong performance in both functional capability and quantitative metrics' with 'notable potential,' yet no specific numbers, baselines, error bars, or statistical tests are referenced in the provided summary; this makes the central empirical claim difficult to evaluate without the full tables and comparisons.
[§3.2] §3.2 (MTMM description): The integration of the Motion Temporal Mamba Module with the diffusion denoising process is described at a high level; a concrete equation or pseudocode showing how MTMM conditions the noise prediction for autoregressive extension would strengthen the long-sequence coherence claim.

minor comments (2)

[Abstract] The project page link is given but the manuscript should explicitly state which supplementary materials (code, models, or additional videos) are available there.
[§3] Notation for the decoupled features (motion, audio, text latents) should be introduced once in §3 and used consistently to avoid ambiguity in later sections.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and the recommendation for minor revision. We address each major comment below and have incorporated clarifications to strengthen the manuscript.

read point-by-point responses

Referee: [§5] §5 (Experimental Results): The abstract and main text claim 'strong performance in both functional capability and quantitative metrics' with 'notable potential,' yet no specific numbers, baselines, error bars, or statistical tests are referenced in the provided summary; this makes the central empirical claim difficult to evaluate without the full tables and comparisons.

Authors: The full manuscript in Section 5 contains detailed tables reporting quantitative metrics against multiple baselines, including error bars and statistical comparisons. To improve immediate accessibility, we will revise the abstract and the opening paragraph of Section 5 to explicitly cite the key numerical gains (e.g., FID, diversity, and coherence scores) and reference the corresponding tables. revision: yes
Referee: [§3.2] §3.2 (MTMM description): The integration of the Motion Temporal Mamba Module with the diffusion denoising process is described at a high level; a concrete equation or pseudocode showing how MTMM conditions the noise prediction for autoregressive extension would strengthen the long-sequence coherence claim.

Authors: We agree that an explicit formulation would clarify the autoregressive mechanism. In the revised manuscript we will insert a concrete equation showing the MTMM-conditioned noise prediction (ε_θ(x_t, t, c_audio, c_text, m_t)) together with pseudocode for the autoregressive rollout, directly linking the Mamba state to the diffusion step. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper describes an architectural pipeline (feature decoupling on Motorica dataset, audio-latent Conformer, text-latent Cross-Conformer, Motion Temporal Mamba Module) and reports experimental metrics without any equations, predictions, or derivations that reduce to fitted parameters or self-referential definitions. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling appear in the provided text; all claims rest on stated training protocol and quantitative results that remain externally falsifiable.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on the effectiveness of newly introduced modules and the data decoupling approach, which are presented without independent prior validation or external benchmarks in the abstract.

axioms (2)

domain assumption Diffusion models conditioned on multimodal inputs can generate semantically controlled dance motions.
Core assumption underlying the multimodal-guided diffusion framework.
domain assumption Mamba architecture supports efficient autoregressive modeling of long motion sequences.
Invoked to justify the Motion Temporal Mamba Module for extended synthesis.

invented entities (2)

Motion Temporal Mamba Module (MTMM) no independent evidence
purpose: Enable smooth long-duration autoregressive dance synthesis within the diffusion model.
New module introduced as part of the architecture.
Decoupled dance dataset no independent evidence
purpose: Separate motion capture, audio rhythm, and text descriptions to improve control.
Explored and generalized to the Motorica Dance dataset.

pith-pipeline@v0.9.0 · 5499 in / 1435 out tokens · 54322 ms · 2026-05-16T16:35:07.096253+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

48 extracted references · 48 canonical work pages · 5 internal anchors

[1]

Chaitanya Ahuja and Louis-Philippe Morency. 2019. Language2pose: Natural language grounded pose forecasting. In2019 International Conference on 3D Vision (3DV). IEEE, 719–728

work page 2019
[2]

Simon Alexanderson, Rafael Nagy, Jonas Beskow, et al. 2023. Listen, denoise, action! audio-driven motion synthesis with diffusion models.ACM Transactions on Graphics (TOG)42, 4 (2023), 1–20

work page 2023
[3]

Emily R Beyerle and Pratyush Tiwary. 2024. Inferring the isotropic-nematic phase transition with generative machine learning.arXiv preprint arXiv:2410.21034 (2024)

work page arXiv 2024
[4]

Martin Biquard, Matthieu Chabert, François Genin, et al. 2025. Variational Bayes image restoration with compressive autoencoders.IEEE Transactions on Image Processing34 (2025), 2896–2909

work page 2025
[5]

Steven Brown. 2024. The performing arts combined: the triad of music, dance, and narrative.Frontiers in Psychology15 (2024), 1344354

work page 2024
[6]

Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, et al. 2023. Diffusion models in vision: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence45, 9 (2023), 10850–10869

work page 2023
[7]

Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. InAdvances in Neural Information Processing Systems, Vol. 34. 8780–8794

work page 2021
[8]

Congyi Fan, Jian Guan, Xuanjia Zhao, et al . 2025. Align your rhythm: Gener- ating highly aligned dance poses with gating-enhanced rhythm-aware feature representation.arXiv preprint arXiv:2503.17340(2025)

work page arXiv 2025
[9]

Team GLM, :, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Dan Zhang, Diego Rojas, Guanyu Feng, Hanlin Zhao, Hanyu Lai, Hao Yu, Hongning Wang, Jiadai Sun, Jiajie Zhang, Jiale Cheng, Jiayi Gui, Jie Tang, Jing Zhang, Jingyu Sun, Juanzi Li, Lei Zhao, Lindong Wu, Lucen Zhong, Mingdao Liu, Minlie Huang, Peng Zhang, Qinkai Zheng, Rui Lu, Shuaiqi Duan, ...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[10]

Kaixuan Gong, Dong Lian, Hongjie Chang, et al. 2023. Tm2d: Bimodality driven 3d dance generation via music-text integration. InProceedings of the IEEE/CVF International Conference on Computer Vision. 9942–9952

work page 2023
[11]

Albert Gu and Tri Dao. 2023. Mamba: Linear-time sequence modeling with selective state spaces.arXiv preprint arXiv:2312.00752(2023)

work page internal anchor Pith review Pith/arXiv arXiv 2023
[12]

Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng. 2024. Momask: Generative masked modeling of 3d human motions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1900–1910

work page 2024
[13]

Chuan Guo, Shihao Zou, Xinxin Zuo, et al. 2022. Generating diverse and natural 3d human motions from text. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5152–5161

work page 2022
[14]

Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. InAdvances in Neural Information Processing Systems, Vol. 33. 6840–6851

work page 2020
[15]

Zhaoyang Huang, Xiaoxuan Xu, Chen Xu, et al. 2024. Beat-it: Beat-synchronized multi-condition 3d dance generation. InEuropean Conference on Computer Vision. Springer, 273–290

work page 2024
[16]

Katsushi Ikeuchi, Zhen Ma, Zhilei Yan, et al. 2018. Describing upper-body motions based on labanotation for learning-from-observation robots.International Journal of Computer Vision126 (2018), 1415–1429

work page 2018
[17]

Min Li, Zhenjiang Miao, and Yantao Lu. 2023. LabanFormer: Multi-scale graph attention network and transformer with gated recurrent positional encoding for labanotation generation.Neurocomputing539 (2023), 126203

work page 2023
[18]

Ruilong Li, Shan Yang, David A Ross, et al . 2021. Ai choreographer: Music conditioned 3d dance generation with aist++. InProceedings of the IEEE/CVF international conference on computer vision. 13401–13412

work page 2021
[19]

Ronghui Li, YuXiang Zhang, Yong Zhang, et al. 2024. Lodge: A coarse to fine diffusion network for long dance generation guided by the characteristic dance primitives. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1524–1534

work page 2024
[20]

Ruilong Li, Jiafan Zhao, Yong Zhang, et al . 2023. Finedance: A fine-grained choreography dataset for 3d full body dance generation. InProceedings of the IEEE/CVF International Conference on Computer Vision. 10234–10243

work page 2023
[21]

Siyao Li, Weijiang Yu, Tianpei Gu, Chunze Lin, Quan Wang, Chen Qian, Chen Change Loy, and Ziwei Liu. 2022. Bailando: 3D dance generation by actor-critic GPT with choreographic memory. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11050–11059

work page 2022
[22]

Benjamin Lindemann, Timo Müller, Hannes Vietz, Nasser Jazdi, and Michael Weyrich. 2021. A survey on long short-term memory networks for time series prediction.Procedia CIRP99 (2021), 650–655

work page 2021
[23]

Zachary C Lipton, John Berkowitz, and Charles Elkan. 2015. A critical review of recurrent neural networks for sequence learning.arXiv preprint arXiv:1506.00019 (2015)

work page internal anchor Pith review Pith/arXiv arXiv 2015
[24]

Xinran Liu, Xu Dong, Diptesh Kanojia, et al. 2025. GCDance: Genre-controlled 3D full body dance generation driven by music.arXiv preprint arXiv:2502.18309 (2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025
[25]

Matthew Loper, Naureen Mahmood, Javier Romero, et al. 2023. SMPL: A skinned multi-person linear model. InSeminal Graphics Papers: Pushing the Boundaries, Volume 2. ACM, 851–866

work page 2023
[26]

Xiaoxuan Ma, Jiajun Su, Chunyu Wang, et al. 2023. 3d human mesh estimation from virtual markers. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 534–543

work page 2023
[27]

Xuan Ma and Kai Wang. 2022. Dance action generation model based on recurrent neural network.Mathematical Problems in Engineering2022 (2022), 1–12

work page 2022
[28]

Sangjune Park, Inhyeok Choi, Donghyeon Soon, et al. 2025. Not like transformers: Drop the beat representation for dance generation with mamba-based diffusion model. In1st Workshop on Generative AI for Audio-Visual Content Creation

work page 2025
[29]

Matthias Plappert, Christian Mandery, and Tamim Asfour. 2016. The kit motion- language dataset.Big data4, 4 (2016), 236–252

work page 2016
[30]

Mingyuan Qi, Zhiyuan Zhao, Haoyu Ma, et al. 2025. Human grasp generation for rigid and deformable objects with decomposed VQ-VAE.arXiv preprint arXiv:2501.05483(2025)

work page arXiv 2025
[31]

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10684–10695

work page 2022
[32]

Hao Sun, Ruixiang Zheng, Haibin Huang, et al . 2024. LGTM: Local-to-global text-driven human motion diffusion model. InACM SIGGRAPH 2024 Conference Papers. 1–9

work page 2024
[33]

Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, et al. 2024. Learning to (Learn at Test Time): RNNs with expressive hidden states.arXiv preprint arXiv:2407.04620 (2024)

work page internal anchor Pith review Pith/arXiv arXiv 2024
[34]

Guy Tevet, Sigal Raab, Brian Gordon, Yoni Shafir, Daniel Cohen-or, and Amit Haim Bermano. 2023. Human motion diffusion model. InThe Eleventh International Conference on Learning Representations

work page 2023
[35]

Jonathan Tseng, Rafael Castellon, and Kexin Liu. 2023. Edge: Editable dance generation from music. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 448–458

work page 2023
[36]

Guillermo Valle-Pérez, Gustav Eje Henter, Jonas Beskow, et al. 2021. Transflower: Probabilistic autoregressive dance generation with multimodal attention.ACM Transactions on Graphics (TOG)40, 6 (2021), 1–14

work page 2021
[37]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. InAdvances in neural information processing systems. 5998–6008

work page 2017
[38]

Nelson Yalta, Shinji Watanabe, Kazuhiro Nakadai, and Tetsuya Ogata. 2019. Weakly-supervised deep recurrent neural networks for basic dance step gener- ation. In2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8

work page 2019
[39]

Kaixing Yang, Xulong Tang, Yuxuan Hu, et al. 2025. MatchDance: Collaborative mamba-transformer architecture matching for high-quality 3d dance synthesis. arXiv preprint arXiv:2505.14222(2025)

work page arXiv 2025
[40]

Kaixing Yang, Xulong Tang, Ziqiao Peng, et al . 2025. MEGADance: Mixture- of-experts architecture for genre-aware 3d dance generation.arXiv preprint arXiv:2505.17543(2025)

work page arXiv 2025
[41]

Wenjie Yin, Xuejiao Zhao, Yi Yu, et al. 2024. LM2D: Lyrics-and music-driven dance synthesis.arXiv preprint arXiv:2403.09407(2024)

work page arXiv 2024
[42]

Mihai Zanfir, Andrei Zanfir, Eduard Gabriel Bazavan, and Cristian Sminchisescu

work page
[43]

In Proceedings of the IEEE/CVF International Conference on Computer Vision

Thundr: Transformer-based 3d human reconstruction with markers. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 12971– 12980

work page
[44]

Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Yong Zhang, Hongwei Zhao, Hongtao Lu, Xi Shen, and Ying Shan. 2023. Generating human motion from Listen to Rhythm, Choose Movements: Autoregressive Multimodal Dance Generation via Diffusion and Mamba with Decoupled Dance Dataset , , textual descriptions with discrete representations. InProceedings of the IEEE/...

work page 2023
[45]

Mingyuan Zhang, Daisheng Jin, Chenyang Gu, et al. 2024. Large motion model for unified multi-modal motion generation. InEuropean Conference on Computer Vision. Springer, 397–421

work page 2024
[46]

Zhipeng Zhang, Andy Liu, Ian Reid, et al. 2024. Motion mamba: Efficient and long sequence motion generation. InEuropean Conference on Computer Vision. Springer, 265–282

work page 2024
[47]

Ce Zheng, Shuang Wu, Chao Chen, et al. 2023. Deep learning-based human pose estimation: A survey.Comput. Surveys56, 1 (2023), 1–37

work page 2023
[48]

Wentao Zhu, Xiaoxuan Ma, Dongwoo Ro, Hai Ci, Jinlu Zhang, Jiaxin Shi, Feng Gao, Qi Tian, and Yizhou Wang. 2023. Human motion generation: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence46, 4 (2023), 2430–2449. A Large Language Model Fine-tuning Details To enable professional-level textual guidance for dance motion gen- eration, we f...

work page 2023