MEGADance: Mixture-of-Experts Architecture for Genre-Aware 3D Dance Generation
Authors: kaixing yang
XulongTang
Ziqiao Peng
Yuxuan Hu
Jun He
Hongyan Liu
Venue: NeurIPS 2025 poster ()
Keywords: AI for Art
Creative AI
Multimedia Learning
3D Motion Generation
Music-to-Dance Generation
Primary area: applications

Abstract: Music-driven 3D dance generation has attracted increasing attention in recent years, with promising applications in choreography, virtual reality, and creative content creation. Previous research has generated promising realistic dance movement from audio signals. However, traditional methods underutilize genre conditioning, often treating it as auxiliary modifiers rather than core semantic drivers. This oversight compromises music-motion synchronization and disrupts dance genre continuity, particularly during complex rhythmic transitions, thereby leading to visually unsatisfactory effects. To address the challenge, we propose MEGADance, a novel architecture for music-driven 3D dance generation. By decoupling choreographic consistency into dance generality and genre specificity, MEGADance demonstrates significant dance quality and strong genre controllability. It consists of two stages: (1) High-Fidelity Dance Quantization Stage (HFDQ), which encodes dance motions into a latent representation by Finite Scalar Quantization (FSQ) and reconstructs them with kinematic-dynamic constraints, and (2) Genre-Aware Dance Generation Stage (GADG), which maps music into the latent representation by synergistic utilization of Mixture-of-Experts (MoE) mechanism with Mamba-Transformer hybrid backbone. Extensive experiments on the FineDance and AIST++ dataset demonstrate the state-of-the-art performance of MEGADance both qualitatively and quantitatively. Code is available at https://github.com/XulongT/MEGADance.

MEGADance: Mixture-of-Experts Architecture for Genre-Aware 3D Dance Generation
Kaixing Yang Xulong Tang'
Ziqiao Peng
Yuxuan Hu'
Jun Hel Hongyan Liu
'Renmin University of China TTinghuu University
MMlouu Tech Inc
Abstract
Music-driven 3D dance generation has attracted increasing attention in recent years, with promising applications in choreography, virtual reality. and creative content creation. Previous research has generated promising realistic dance move- ment from audio signals. However, traditional methods underutilize genre condi- tioning, often treating as auxiliary modifiers rather than core semantic drivers. This oversight compromises music-motion synchronization and disrupts dance genre continuity, particularly during complex rhythmic transitions, thereby lead- ing to visually unsatisfactory effects. To address the challenge, we propose MEGADance, novel architecture for music-driven 3D dance generation. By decoupling choreographic consistency into dance generality and genre specificity, MEGADance demonstrates significant dance quality and strong genre controlla- bility. It consists of two stages: (1) High-Fidelity Dance Quantization Stage (HFDQ), which encodes dance motions into a latent representation by Finite Scalar Quantization (FSQ) and reconstructs them with kinematic-dyyamic straints, and (2) Genre-Aware Dance Generation Stage (GADG), which maps mu- sic into the latent representation by synergistic utilization of Mixture-of- -ff-exerts (MoE) mechanism with Namba-Transfoon ments on the FineDance and AIST+ dataset demonstrate the state-of-the-ar per- formance of MEGADance both qualitatively and quantitatively. Code is available at https://gtthaa.coom/uuuumn l/MeGADance
hybrid backbone. Extensive experi-
Introduction
Music-to-dance generation is crucial task that translates auditory input into dynamic motion, with significant applications in virtual reality, choreography, and digital entertainment[[] [2]. By au- tomating this process, it enables deeper exploration of the intrinsic relationship between music and m3vement[[3], while expanding possibilities for creative content generation. Due its broad impact, music-to-dane do-dnce generation has attracted increasing attention PL D, [g].
Current music-to-dance generation approaches have witnessed rapid progress and can be broadly categorized into two paradigms[{L] M (1) One-stage methods directly map musical features to human motion [ B. (2) Two-stage methods first construct choreographic units and then learn their probability distributions conditioned on music [EL 1S, I[]]. However, previous methods only treat genre weak auxiliary bias rather than the core semantic driver[[ p. H], facing several essential problems such as misaligned music-motion synchronization and disrupted dance genre continuity, For example, Uyghur movements clip inappropriately mixed into the Popping routine, when typical Popping music exhibits complex transitions in rhythm and intensity, as shown in Fig. M. This oversight leads to unsatisfactory visual effects, and fails meet genre- cenre-specific user demands.
"qqual contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Farebeling comples transaiions inclutii fart slow Sust modtim stong woot slone Intorat:
Musti-Modal Fusion
Donce Generator
Previous Work Alyitur Lolp the roultso aystronization
Genre
undlrmningg dience geore continaly
Popping Music
Spacialized
Universal Expert
Expert
exppiing
www.aa//llccctaaa........oo malttaciing syttchocch music DIt also presensing shance genee continstty
zadion
Figure 1: MEGADDnne enhances choreography consistency by decoupling it into dance generality and genre specificity via the Mixture-of- design. Compared to previous methods, it produces synchronized dance with genre continuity, even under complex music conditions.
To address these limitations, we propose MEGADDnce, the first ofxture-of-Fxpertt//eee (MoE) architecture for Genre-Aware 3D Dance Generation. By decoupling choreographic consistency into dance generality, modeled by Universal Experts shared across all genres, and genre speci- ficity, captured by Specialized Experts selected via genre-guided hard routing, MEGADance en- ables robust musical alignment and fine-grained stylistic fidelity. Due to its structured inductive bias, MEGADance exhibits strong genre control and remains robust even in the presence of modal- ity conflicts, such as generating a Breaking dance for soft-paced Chinese music while preserving rhythmic and dynamic alignment.
Specifically, MEGADance comprises two stages. (1) High-Fidelity Dance Quantization Stage (HFDQ). which encodes dance motions into a latent representation. In the HFDQ stage, we intro- duce Finite Scalar Quantization([I) (FSQ), which replaces traditional VQ-VAE codebooks miti- gate codebook collapse and enhance latent diversity. Additionally, we impose kinematic constraints by simultaneously reconstructing 3D joints through Forward Kinematics[IEI] from SMPL[[E], and dynamic constraints by reconstructing dances while considering velocity and acceleration, to en- hance apatio-tempora coherence. (2) Genre-Aware Dance Generation Stage (GADG), which maps music into the latent representation. In the GADG, the Universal Experts and Specialized Ex- perts work synergistically to jointly modeling dance generality and genre specificity. For genre- disentangled expert design, Universal Experts model universal rhythmic and temporal structures across all genres, enhaneing robustness to diverse musical inputs and stabilizing cross-modal align- ment; and Specialized Experts specialize in fine-grained stylistic variations unique to each genre, guided by hard routing to effectively disentangle genre-dependent features from genre-invarian dy- namics. For expert structure, each expert, whether Specialized or Universal, adopts autoregressive Mamba-Transfor
hybrid backbone, combining Mamba's efficient intra-modal local dependency capture [|S]] with the Transformer's cross-modal global contextual understanding(II)) thereby en- abling the generation of temporally coherent and musically aligned dance motions.
The contributions of our work can be summarized as: (1) We introduce MEGADance, the first Mixture-of- -xperts (MoF) architecture for musie-to-dance generation, designed to enhance chore- ographic consistency by decoupling it into dance generality and genre specificity. MEGADDance achieves state-of-the-art (SOTA) performance and demonstrates robust genre controllability, as demonstrated through extensive qualitative and quantitative experiments on the AIST++[I] and FineDance[[]] [2] datasets. (2) We propose a High-Fidelity Dance Quantization framework that in- troduces FSQ with kinematic-dynamic dual constraints, ensuring complete codebook utilization (100% vs. VQ- VAE[LZ))ss 75%) while achieving excellent reconstruction accuracy. (3) We design Mamba- Transformer hybrid backbone for music-to-danee generation, combining Mamba's 's effi- cient intra-modal local dependency capture with the Transformer's 's cross-modal global contextual understanding.
2 Related Work
2.1 One-Stage Music-to-Dance Generation
Music and dance are deeply interconnected, leading to significant advancements in the field of music- driven 3D dance generation. Researchers utilize musical features extracted via tools like Librosa[[]].

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Jukebox [18], and MERT[[9] to predict human motion, including SMPL[[E] parameters[[]] and body keypoints[H]] Early methods primarily employ encoder-decoder architectures to directly obtain en- tire human motion sequence [20X 21.. 22 23, M]. Recognizing the natural hierarchical structure of human joints, some researchers introduced Graph Convolutional Networks [[C [5] en- hance interaction at the joint level, thereby improving the biomedical plausibility of the generated motions. In AIGC, Generative Adversarial Networks (GANs) are widely applied, some researchers introduced it in music-to-dance [2sks|26, 9. p.].. Specifically, GANs' generators produce dance mo- tions from music, with discriminators providing feedback to guide generated motions more natural. Recently, Diffusion Models have shown remarkable success in various AIGC tasks, with notable applications extending to the music-to-daned domain [T. D. K. G. [ but the computational cost of the sampling process remains high, especially in long- sequence generation scenarios for the music- to-dance task. However, the lack of explicit constraints to maintain the generated pose within proper spatial boundaries often leads nonstandard poses that extend beyond the dancing subspace during inference, resulting in low dance quality in practical applications.
2.2 Two-Stage Music-to-Dance Generation
Leveraging the inherent periodicity of dance kinematics, researchers propose Two-Stage methods, including (1) Dance Quantization stage: curating choreographic units from motion databases, and (2) Dance Generation stage: learning music-conditioned probability distributions over these units. As these choreographic units are derived from real human motion data, two-stage approaches naturally benefit from a biomechanical plausibility prior, contributing to the realism of generated dances.
26] construct choreographic units through uniform segmentation of motion sequence, incurring high computational overhead. Recent works [32. 33] employ VQ-VAE for intelligent unit construction, significantly reducing time/space complexity. Considering the relative independence of upper and lower body movements, [E], [S] construct choreographic unit for lower and upper parts separately, improving motion reconstruction through expanded unit capacity (L + L x L). However, above works predominantly operate on 3D human body keypoints, which lack expressiveness in capturing nuanced motion details. [SS Gi] construct choreographic units in the SMPL pose space but apply uniform treatment across joints, neglecting the body's 's kinematic hierarchy, such root errors propagate globally through kinematic chains while hand errors remain localized.
Dance Quantization Stage. Traditional methods [29,
Dance Generation Stage. To model choreographic unit distributions, Choreomaster [26] employs GRU-based backbone, while DanceRevolution [9]] uses RNNs. Recent works like Bailando [[]] and Bailando++[[] adopt cross-moda. Transformers for improved temporal modeling and music-motion alignment. Moreover, Everything2Motion [6E]] and TM2D [B>]]] leverage pretrained models in text- [o-motion[ [SI generation to improve motion quality, often at the expense of choreographic com- plexity and creativity. To enrich input representations, [2) S GT. M introduce genre information via shallow fusion, such cross attention [3I]] or feature addition [А]] However, these approaches re- main insufficient for achieving robust genre controllability, particularly under cross-moda moosl conflicts, such generating Breaking dances conditioned on slow-tempo traditional Chinese music.
In conclusion, existing methods face two key limitations: (1) VQ- rQ-VAE-based quantization suffers from low codebook utilization; (2) Insufficient utilization of genre information results in poor music- motion synchronization and disrupted dance genre continuity. Thus, we propose MEGADance, a two-stage framework. The Dance Quantization stage employs FSQ with kinematic and dynamic constraints to enhance codebook efficiency while preserving reconstruction fidelity. The Dance Generation stage adopts an MoE architecture with Transforrneo backbone to jointly cap- ture dance generality and genre specificity with efficiency.
3 Methodology
3.1 Problem Definition
Given a music sequence M = (mo, mi,..., m and a dance genre label g, our objective to synthesize the corresponding dance sequence S = (1..,1...., ST), where my and 8, denotes the music and dance feature at time step We define each music feature m, as 35-dim vector extracted by Librosa( including 20-dim MFCC, 12-dim Chroma, 1-dim Peak, 1-dim Beat and
3

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Stagel: High- Fighliid Donce Quantization Stage (HFDQ)
Dance Encodor
Decoder
(HL5,-1) -2
Stage2: Genre-Aware Dance Generation Stage (GAD6)
Genre- Disneaagidd Experts Design
X
Juzz Expert
Poosann Balle
Popplig Expert
Autoregrossive Ehsturs-of-Experts Architacture
Jazz
Expert
for
Universal Expert
Mamba- Transforran Hybrid Backbone
Librosa
Motion Tokenizer
eauaa popo
Figure 2: Overview of MEGADDnce. Stage 1 (HFDQ) quantizes dance into upper/lower-booyy latent codes using FSQs with kinematic and dynamic reconstruction constraints. Stage (GADG) maps music to codes via an L-layer Mixture-of- architecture (blue: Specialized Experts, pink: Universal Experts), where each expert is equipped with a
hybrid backbone
1-dim Envelope. We encode genre label g as a one-hot vector. We represent each dance feature as 147-dim vector [r: f). where and 0 encapsulate the root translation and 6-dim rotation representation[55]] the SMPL[EE] model, respectively. Furthermore, we synchronize the music sequence with the dance sequence at a temporal granularity of 30 frames per second.
3.2 High-Fidelity Dance Quantization
Finite Scalar Quantization with Motion Decomposition. Choreographic units serve as the fun- damental building blocks of dance composition, forming the basis for structuring and connecting movements. Despite variations in style and tempo, dances across genres exhibit common underly- ing units. Our objective is unsupervisedly encapsulate these units into versatile and reusable codebook, enabling any dance sequence to be represented as sequence of discrete codebook ele- ments. To account for the relative independence between upper-body and lower-body movements during dance, we maintain separate codebooks for the upper and lower = (2 z body, where k represent codebook size. Additionally, root translation velocities are associated with the lower body to preserve natural motion dynamics. This decomposition allows the combination of different code pairs cover wider array of choreographic units.
Our 3D motion reconstruction approach, illustrated in Fig. A initiates with a Dance Encoder E (a three-layer ID-CNN for information aggregation and two-layer MLP for dimension adjustment) encoding the dance sequence = {S", S into context-aware features z = {z" 22! These fea- tures quantized using Finite Scalar Quantization (FSQ) to obtain 2 {zi 2²), which are then decoded by Dance Decoder D (a two-layer MLP for dimension adjustment and a three-layer ID TransConv for information restoration) to reconstruct the dance movement $ {
S To re- solve the codebook collapse problem caused by the conventional VQ-VAE[ES based quantization, adopt FSQ. By replacing the discrete "argmin" codebook selection with scalar quantization via differentiable bounded rounding, FSQ enables balanced utilization and stable gradient propagation:
à f(z) + sg [found[[(z)) f())),
where f(-) is the bounding function, setting as the sigmoid(()) function in our practice. Each channel in 2 will be quantized into one of the unique L integers, therefore we have 2 E (1...., [i....,L' L)dd The codebook size is calculated as A I 1= Ls. and L,, d are super parameter. In conclusion, FSQ with Motion Decomposition expands its effective motion representation capacity, thereby enhancing the diversity of subsequent generated dance.

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Motion Reconstruction with Kinematic- -Dammicc Constraint. Unlike VQ-VAE requiring addi- tional loss to update any extra lookup codebook, FSQ directly integrates numerical upproximations "round" within its workflow. The Dance encoder E and decoder D are simultaneously learned with the codebook via the following loss function:
LFsQ =
+ Cjoins (J.
(2)
The Campi the reconstruction loss that ensures the predicted 3D SMPL sequence closely aligns with the ground truth. Simple reconstruction on SMPL parameters treats all joints equally, neglecting the complex hierarchical tree structure of human body joints, different joints vary in their tolerance to emror.. For instance, errors at the root node propagate throughout all nodes, whereas errors at the hand node primarily affect only itself. Thus, we execute Forward Kinematic [[ techniques to derive 3D joints and apply reconstruction constraints Ljoint between J and J. Moreover, the reconstruction loss accounts not only for the spatial positions but also for the velocities (a₂) and accelerations (a²)) of the movements, where / and " correspond to the first- and second-order derivatives respectively:
S'I11 + az2ls" 5 Camp1 (S, -15- + an a/ -
Liooim (J.J) = || j J|| + anl - J'll + a2|l J"
Through training, our method facilitates the interchangeability of orthographic memory codes, en- abling the synthesis of new motions from existing choreographic units by recombining different code elements.
3.3 Genre-Aware Dance Generation
With dance sequences represented as discrete latent codes, the music-to-dance generation task is simplified from a regression problem into a classification problem, where the goal is select appro- priate pose codes from a codebook rather than predict continuous motion parameters.
3.3.1 Mixture-of-Experts Architecture
As illustrated in Fig. 2 we perform cross-modal autoregressive generation. Given music fea- tures mı.7 extracted using Librosa, dance genre label g. and the previous pose codes Po:T-1 (Po:r-1.Pb-r-1 encoded by the Motion Tokenization in HFDQ, the GADG predicts action prob- abilities -0 = (ob-r-1:00:--
of every 2; E Z, using an L-layer MoE architecture. To align the predicted action probabilities d0:7-1 with the
pose codes Рı:T, employ supervised Cross-Entropy [ loss. where each predicted action aN is matched to its corresponding target pose code P-+1- The inference of GADG includes: 1) Short sequences (S 5.5s) via autoregressive gener- ation, 2) Long sequences via sliding-window prediction with 5.5s overlap.
Specifically, each MoE layer contains Specialized Expert and Universal Expert, which jointly model dance generality and genre specificity. Specialized Experts (e.g., Pop Expert, Jazz Expert) are conditionally activated based on the genre label g. and input features expert via hard routing mechanism. In parallel, features from all genres are processed by the shared Universal Expert capture genre-invariant dynamics. For expert structure, each expert, whether Specialized or Universal, adopts an autoregressive Mamba- Transformer hybrid backbone: Mamba captures intra-modal local dependencies, while Transformer encodes cross-modal global context, thereby enabling the generation of temporally coherent and musically aligned dance motions.
routed to the corresponding
3.3.2 Genre-Disentangled Experts Design
Specialized Experts. The Specialized Experts are designed to capture genre-specific stylistic pat- terns, motivated by two core considerations: (1) Structural Inductive Bias: By isolating parame- ters across experts, the model enforces separation of genre-specific motion motifs (e.g., Krump's grounded explosiveness vs. Contemporary's 's fluid transitions), thereby preserving distinct stylistic representations. This separation also introduces genre-aware control priors that mitigate cross- genre interference, which is critical for genre-conditioned dance generation. Leveraging sparse MoE design [39], each input routed to a single expert, significantly reducing parameter redundancy and computational cost.
(2) Computational Efficiency:
5

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Universal Experts. The Universal Expert learns generalizable representations to complement Spe- cialized Experts through two key roles: (1) Fundamental Choreographic Prior: It learns shared low-level patterns across genres (e-g- periodicity, beat synchronization, and biomechanical consis- tency). contrast, models relying solely on Specialized Experts often fail under modality mismatch (e.g., producinn static or repetitive movements when Ballet music is processed by Popping Expert). The Universal Expert provides a genre-agnostic prior that enhances stability and expressiveness under complex input conditions. (2) Disentangled Representation: By disentangling shared and genre-specific factors, the model allows each expert to specialize in distinct subspaces, enhancing generation quality|AOI.
3.3.3 Trando-rransoommr Hybrid Backbone
Cross-Modal Global-Context Modeling. Leveraging the Transformer's global receptive field [T6]. we concatenate cross-modal features along the temporal axis and facilitate structured interactions among music, upper-body, and lower-body representations, by Attention layer and a Feed Forward layer. The attention layer is the core component that defines the computational dependencies among sequential data elements and is implemented
QQKT + M V,
Attention(Q. K, V, M) = softmax
VC
where Q, K, denote the query, key, and value from input, and M is the mask, which determines the type of attention layers. The two most common attention types are full attention [I6]], which enables global context exchange, and causal attention [H]]], which restricts each position to attend only current and past inputs. Given that music-to-dance generation is typically applied long sequence while being constrained by limited computational resources, training is commonly con- ducted on short clips. During inference, the sequence first extended autoregressively up to the training length (step 1), and then completed using a sliding window approach for the remaining part (step 2). Training driven by standard causal attention only aligns with step and fails to account for the dominant step 2 during inference, thereby limiting generation performance. To better align train- ing with inference, we introduce a diddng-windors attention mechanism that mimics the generation process. The attention mask M E 33 x3T is structured as a 3 x 3 block matrix, where each block a T x T sliding-window mask, enabling cross-modal global-context attention.
Intra-Modal Local-Dependenee Modeling, While the Transformer excels at temporal modeling, it inherently position-invariant and captures sequence order only through positional encodings [[G]], which limits its deep understanding of local dependencies. In contrast, nusiccto-dance generation demands strong local continuity between movements. Owing to its inherent sequential inductive bias, Mamba [[S]] has demonstrated strong performance in modeling fine- ime-grained local dependen- cies [222 A We therefore apply independent Mamba to the music, upper-body, and lower-body features respectively, to model their intra-modal local dependencies. Specifically, Selective State Space model (Mamba) incorporates Selection mechanism and Scan module (S6) [[S]]] to dy- namically select salient input segments for efficient sequence modeling. Unlike the traditional S4 model [SE]] with fixed parameters A, B, C, and scalar A, Mamba adaptively learns these parameters via fully fully-connected layers, enhancing generalization capabilities. Mamba employs structured state- space matrices, imposing constraints that improve computational efficiency. For each time step f, the input 24, hidden state ht and output y: follow:
hr Á B
(5)
Cche.
where Ar, Br, C2 are dynamically updated parameters. Through discretization with sampling inter- val A, the state transitions become:
À = exp( AA),
B = (AA) (exp(AA) 7) AB,
h4 = Ah--1 Bar.
where -1 is the inverse AA, and 7 denotes the identity matrix. The sean module captures temporal dependencies by applying trainable parameters across input segments.

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Table I: Comparison with SOTAs the FineDance dataset.
Quality
Creativity
Alignment
User Study
FID& FID&
FID,4
DIV&
DIV, of
DIV,t
BAST
DQ1
DS1
DC1
GT
0
10.98
7.45
6.07
0.215
4.39
4.35
4.48
Bailando++13l
54.79
16.29
8,42
5.98
4.73
0.213
3.85
3.50
3.82
FineNet[2]]
65.15
23.81
13.22
5.84
5.19
4.29
0.219
3.62
3.65
3.47
Lodge[[]
55.03
14.87
5.22
6.14
6.18
5.50
0.218
4.18
4.17
4.08
MEGADance
50.00
13.02
2.52
6.23
6.27
5.78
0.226
4.25
4.30
4.23
4
Experiment
4.1
Dataset
FineDance. Finelance [2]] is the largest public dataset for 3D music-to-dance generation, featur- ing professionally performed dances captured via optical motion capture. It provides 7.7 hours of motion data at 30 fps across 16 distinet dance genres. Following [8], evaluate test-set music clips, generating 1024-frame (34. 13s) dance sequences. 2) AIST++. + [M] is a widely used benchmark comprising 5.2 hours of 60 fps street dance motion, covering 10 dance genres. Follow- ing [0], we use test-set music clips to generate 1200-frame (20.00s) sequences.
4.2 Quantitative Evaluation
Comparison. We evaluate MEGADance against state-of-the-art (SOTA) baselines both the FineDance and AIST+ datasets using a comprehensive suite of metrics. For each generated se- quence, compute Fréchet Inception Distance (FID) and Diversity (DIV) across three feature spaces: (I) Kinetic (k), capturing motion dynamics; (2) Geometric (g), encoding spatial joint rela- tions; and (3) Style (s), extracted via Transformer-basee genre classifier. We also assess music- motion synchronization using Beat Align Score (BAS), following (II. 8J. All baseline results are reproduced under our experimental setup
fair comparison. On the FineDance dataset (Tab. M1, MEGADance outperforms all baselines, achieving the lowest FID in all three feature types (FID;=50.00, FID,=13.00 FID =22..22) the highest motion diversity (DIV,=6.23, D6V,= DIV,=5.78), and the best BAS (0.226). On the AIST+ datasets (Tab. DD) MEGADance again ranks first FID (FID;=25.8 89, 11D,212.62), and achieves strong performance in diversity (DIV,=5.84, 6.V,= and BAS (0.238). These results underscore the effectiveness of our genre- aware Mixture-of-Expert design in balancing motion quality, creativity, and synchronization across diverse datasets. Performance efficiency analysis is provided in Appendix A.3.
User Study. Dances inherent subjectivity makes feedback essential for evaluating generated movements|35) [>] in the music-to-dance generation task. We select 30 in-the-wild music segments (34 seconds each) and generate dance sequence using above models. These sequences are evaluated through a double-bling questionnaire, by 30 participants with backgrounds in dance practice, in- cluding undergraduate and graduate-level students. The questionnaires based on 5-point scale (Great, Good, Fair, Bad, Terrible) and assess three aspects: Dance Synchronization (DS, alignment with rhythm and style), Dance Quality (DQ, biomechanical plausibility and aestheties), and Dance Creativity (DC, originality and range). As shown in Tab. MI MEGADance significantly outperforms all baselines across user-rated metrics (i.e., DS 4.30, DQ = 4.25, DC 4.23). high scores in various aspects underscore its superiority in generating movements in terms of human preferences.
4.3 Qualitative Evaluation
To assess the visual quality of the generated dance sequences, we perform qualitative comparison between MEGADance and several existing baseline models, as depicted in Fig. B. In terms of ex- pressiveness, MEGADance outperforms the competing methods in several key areas. For instance, Lodge|8] struggles with stylistic consistency, often blending conflicting dance genres, such as in- corporating Uyghur movements into a typical Breaking routine, leading to a disjointed aesthetic. [2)) while capable of generating movement sequences, suffers from significant artifacts, in- cluding unnatural sliding and teleportation, which detract from the fluidity and physical realism of the motions. Additionally, Bailando++ demonstrates lack of diversity, with movements fre-

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a) DEGADance generates stylistically consistent, and diverse donce motions with natural dance steps.
Lodge exhibits weaker expressiveness inconsistent dance styles, mixing Uyghur Breaking
<) FineNltt suffers from reduced motion quality, with frequent artifacts such teleportation and unnatural sliding.
d) Bailandos+ shows limited motion diversity, characterized by repetitive routines excessive in-place movements.
Figure 3: Qualitative Analysis on typical Breaking Battle music clip.
Table 2: Comparison on the +IST+ Dataset.
Braaking:
FID
FID,
DIVat
DIV,1
BAST
0.232
FACT
35.35
22.11
5.94
6.18
0.221
Dunhuang:
Bailandot+ S
30.21
15.48
5.35
5.13
0.228
EDGEJES]
42.16
22.12
3,96
4.61
0.233
Lodge(88
35.72
17.92
5.72
5.91
0.247
MEGADance
Locking:
Table 3: Comparison for Genre Controllability.
FID,4
DIV,t
ACC1
FIf
Uyghur:
GT
0
6.07
78.31
76.35
FineNet[[i]
13.22
4.29
42.06
37.44
Figure 4:
Visualization of Genre Controlla- bility representative Chinese music clip.
Lodge(8]
5.22
5.50
$1.86
45.23
MEGADance
2.52
5.78
75.64
70.81
quently repeating and heavy reliance static, in-place gestures, limiting the range of expressive movement patterns. These findings underscore the superiority of MEGADance in generating diverse, genre-consistent, and musically synchronized dance sequences.
4.4 Genre Controllability Evaluation
To quantitatively assess genre controllability. we compare MEGADance with Lodge [8] and FineNet [D]]. Using ground-truth genre labels, we evaluate style alignment (FID,) and style di- versity (DIV) on 20 test clips. We further assess genre classification accuracy (ACC) and F1 score (F1), conditioned on the correct genre and four randomly sampled negative genres. shown in Tab. BL MEGADance achieves the best performance across all metrics. It significantly reduces FID, (2.52) while improving diversity (DIV, = 5.78). Despite potential cross- tross-modal conflicts (c.g., assigning Popping genre typical Chinese music clip), MEGADance achieves high genre discriminability (ACC = 75.64, F'1 = 70.81), closely approaching ground-trutt performance. Com- pared to FineNet and Lodge, our method produces motion sequences that are both more stylistically coherent and genre-distinetive. Our MoE-based genre routing prevents cross-genre interference via disentangled expert subspaces activated by discrete labels, whereas naive continuous fusion (e.g. Feature Addition in [2L N or Cross Attention in [GI]] inherently blurs stylistic boundaries.
To explore genre controllability from visual perspective, assign different dance genres (distal: Breaking/LLocking proximal: Dunhuang/ (uggnrrr to a representative Chinese music clip. We recom- mend watching the supplementary vidco for more details. As shown in Fig. EL the generated motions

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Table 4: Ablation study of the two-stage MEGADance architecture on the Finelance dataset.
F
FID,L
FID,4
BAST
SMPL
Joint
GT
0
0.215
MSEL
MAEL
MSEJ
MAEL
wo Sts
53.05
19.26
7.95
0.218
w/o Kin. Loss
0.0238
0.0847
0.0089
0.0507
w/o UE
54.50
15.52
2,91
0.223
w/o Dyn. Loss
0.0201
0.0779
0.0073
0.0482
w/o Mamba
56.29
14.51
2.67
0.221
FSQ - VQ- VAE
0.0308
0.0984
0,0220
0.0842
Ours
50,00
13.02
2,52
0.226
Ours
0.0200
0.0770
0.0069
0.0469
(a) Genre-Aware Dance Generation Stage.
(b) High-Fidelity Dance Quantization Stage.
exhibit both genre fidelity and music synchrony: (1) Breaking: agile footwork with rapid steps and
directional shifts, driven by percussive rhythms and dynamic weight transfers; (2) Locking: exag-
gerated arm swings and torso isolations, punctuated by syncopated, guitar-mimicking gestures; (3) Dunhuang: fluid upper-body arcs with slow rotations and knee undulations, mirroring melodic phras- ing and visual symmetry; (4) Uyghur: rapid spins with hand-to-face motifs, emphasizing rotational clarity and rhythmic precision.
4.5 Ablation Study
4.5.1
Genre-Aware Dance Generation Stage
We conduct an ablation study evaluate the contribution of three core components in the Genre- Aware Dance Generation stage: Specialized Experts (SE), Universal Experts (UE), and the Intra- Modal Local-Dependency Modeling (Mamba), with results summarized in Tab. A We recom- mend watching the supplementary video. (1) Specialized Experts (SE). Replacing the SE results in substantial performance degradation across all metries, especially on FID, (7.95 vs. 2.52), con- firming its critical role in preserving stylistic fidelity across genres. (2) Universal Expert (UE). Removing the UE leads to clear drops in FIDx (54.50 50.00) and FTD, (15.52 13.02), while having only minor impact on FID, and BAS. This suggests the UEs effectiveness in providing generalizable priors that enhance structural and dynamic consistency. (3) Mamba. Replacing the Mamba in backbone results in moderate performance declines on all metrics (e.g., FIDx from 50.00 56.29), demonstrating Mambas advantage in modeling fine-grained local dependencies and improving overall motion quality.
4.5.2 High-Fidelity Dance Quantization Stage
We investigate the effectiveness of three key components in High-Fidelity Dance Quantization Stage: Finite Scalar Quantization (FSQ), the Kinematic Loss (Lkin.). ), and the Dynamic Loss (LDyn.). ). Tab. HR reports the M E and MAE on both SMPL parameters and 3D joint positions. (1) FSQ. Replac- ing FSQ with VQ-VAE leads to a significant performance drop in all metrics (c.g., Joint MSE increases from 0,0069 to 0.0220), validating FSQs superiority. Moreover, replacing VQ-VAE with FSQ achieves full codebook utilization (75% 100%). (2) Kinematic Loss. Removing the kinematic loss notably increases errors both SMPL (MSE: 0.0200 0.0238) and joint space AMEE: 0.0469 0,0507), highlighting its role in enforcing accurate structural constraints via forward kinematics. (3) Dynamic Loss. Excluding the dynamic loss results in a moderate degradation in temporal fidelity (e.g., Joint MSE: 0.0069 0.0073), demonstrating its contribution for temporal fidelity.
5 Conclusion
In this paper, we present MEGADance, genre-aware MoE-based architecture for music-to-dan dance generation. MEGADance enhances choreography consistency by decoupling it into dance general- ity and genre specificity via an MoE design. Through the synergy of high-fidelity dance quantiza- tion stage and genre-adaptive dance generation stage, MEGADance achieves state-of-the-ard perfor- mance and strong genre controllability. In future work, we plan to extend MEGADance with text conditioning to enable more interactive and flexible dance generation.
6 Acknowledgements
This work was supported by National Natural Science Foundation of China (NSFC) under Grant Nos. 72572090, 62436010, 62572474 and 62172421.

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Appendix
A.1 Implementation Details
High-Fidelity Dance Quantization. In the High-Fidelity Dance Quantization Stage, we use shared codebook configuration for the upper and lower body branches. The model is trained on 8-second SMPL 6D rotation sequences sampled at 30fps, where S, S E RTx147 (i.e., T = 240). For data construction, we augment the training set using sliding window approach with window size of 240 and a stride of 16. A three-layer CNN encoder performs temporal downsampling, and three-layer transposed convolution decoder D performs upsampling. The latent codes for the lower and upper body are p', p" I with T 30. In the Finite Scalar Quantization module, the codebook size is 4375, with I, = 17,5,555 5, 5, 5]. and the feature dimension is set to 512. For reconstruction,
both SMPL-parameter loss Sampl and joint-position loss Ljoint- with velocity and acceleration terms weighted by a1 = 0.5 and 02 = 0.25, respectively. The model is trained for 200 epochs using the Adam optimizer, with exponential decay rates of 0.5 and 0.99 for the first and second moment estimates. A fixed learning rate is used with a batch size of 32. The experimental setup is consistent across FineDance and +IST++
Genre-Aware Dance Generation. In the Genre-Aware Dance Generation Stage, we adopt a hybrid architecture, trained latent codes p', R extracted from the High-Fidelity Dance Quantization Stage, using 8-second dance sequences at 30fps. For data con- struction, augment the training using sliding window approach with window size of 240 and a stride of 16. In MEGADance, the Music Encoder consists of = 6 processing layers. The Mamba block is configured with model dimension of 512, state size of 16, convolution kernel size 4,4, and expansion factor of 2. The Transformer block uses a hidden size of 512, 8 attention heads, a feedforward dimension of 2048, and dropout of 0.25. For Slide Window Attention, autoregressive step to 22 and the sliding window step to 8 to construct the attention matrix. For in- put representation, genre labels (16 classes from FineDance) match the 112-dimensional latent space, while music features extracted by Librosa (35 dimensions) are projected to 512 dimensions via two-layer MLP. For output, MEGADance predicts 4375-class distributions via softmax for upper-body and lower-body codebook respectively. The model is op- timized using Adam with exponential decay rates of 0.9 and 0.99 for the first and second moment estimates, respectively, trained for 80 epochs with a fixed learning rate and a batch size of 64. The experimental setup consistent across Finc and AIST++
set the
embedded using mn. Embedding
A.2 Qualitative Analysis for Ablation Study
w/ UE:
A
Mamba:
Figure 5: Qualitative Analysis for Ablation Study. MEGADance generates visually expressive dance motions, outperforming others in terms of stylistic consistency and movement diversity.
In this section, we conduct a qualitative analysis to evaluate the contribution of each component in the Genre-Aware Dance Generation stage. As illustrated in Fig. EL the Specialized Experts (SE), Universal Experts (UE), and the Mamba-enhanced backbone (Mamba) each play a crucial role in shaping the quality of the generated dance motions.
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Removing SE, which is responsible for capturing genre- genre-specific stylistic features, results in sig- nificant loss of genre identity. This removal leads mismatches where, for example, soft or fluid movements are applied to intense, percussive music, breaking the stylistic coherence expected for the genre. In contrast, the exclusion of UE impacts the overall complexity of the generated motion. Without UE, the generated sequences tend to be overly simplistic, often consisting of static poses or repetitive, monotonous movements, such as constant hand-raising, which lack the dynamic variation essential for engaging dance sequences. Furthermore, omitting the Mamba module, which enhances the backbone with a selection mechanism and scan module, results in significant decrease both movement diversity and alignment with the music. The generated dances become less responsive to the rhythmic and dynamic changes in the music, leading to sequences that feel disjointed or fail to reflect the musical structure accurately.
Collectively, these observations highlight the importance of each component in the overall frame- work. The combination of SE, UE, and Mamba ensures that the generated dance is not only genre- appropriate but also rich in motion variety and tightly aligned with the music.
A.3 Computational Analysis
Table 5: Comparison for Computational Analysis.
Model
Parameters
Run Time
Bailando++
152M
5.46s
FineNet
94M
3,97s
Lodge
235M
4.57s
MEGADance
120M
4.31s
In this section, we present the computational analysis of our approach. Providing quantitative com- parisons of parameters and runtime helps clarify the efficiency advantages of our design. We report results for generating a 1024-frame 13s) dance sequence. Notably, MEGADance employs sparse expert activation, where only specialized expert is activated per input, substantially reducing the effective computation during inference. "Parameters" are only calculated the activated parts. All "Run Times" are conducted on an RTX 3090 GPU with Intel Xcon Gold 5218 CPU. As shown in Table 5L MEGADance exhibits slightly higher complexity than FineNet but delivers significantly better generation quality (see Tables 13). Compared to Bailando++ and Lodge, it achieves lower latency and requires fewer parameters while maintaining superior performance. These results high- light MEGADances favorable balance between efficiency and quality.
A.4 Scalability Analysis
Table Scalability Analysis of MEGADance.
Model
FID; + FID,
FID, 4 DIV, t DIV, t DIV, t
BAS
MEGADance
50.00
13.02
2.52
6.23
6.27
5.78
0.226
MEGADance (-Dunhuang)
54.70
14.10
2.41
6.35
6.12
5.61
0.224
MEGADance (-Breaking)
51.90
13.30
2.60
6.18
6.24
5.94
0.211
In this section, we discuss the scalability of MEGADance. (1) The FSQsin the HFDQ stage op- erates without retraining when new genres or samples are introduced. The motion patterns in the FineDance dataset are sufficiently diverse and representative to support generalization. (2) As de- scribed in Section 3. MEGADance employs a Mixture-of-Expera (MoE) design in which Spe- cialized Experts are selected according to discrete genre labels. This modular architecture inherently supports genre extension. Specifically: (i) the General Expert and existing Specialized Experts can be preserved; (ii) new Specialized Expert can be added and trained exclusively on subset con- taining the new genre; and (iii) only the genre-to-expert mapping the routing policy needs to be updated, without requiring structural modifications.
(3) To verify this capability, we performed an additional experiment in which the Dunhuang and Breaking genres are removed from the original 16 16-genre training set, and subsequently reintroduced following the above extension strategy. As
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shown in Tab. G. the performance degradation relative to full retraining is negligible, indicating that MEGADance can efficiently incorporate new genres without complete retraining.
A.5 Mixed Genre Generation
Figure 6: Visualization.
Figure 7: User Study.
Model
SQT
MQ
FineNet
4.12
Lodge
3.14
4.27
MEGADance
4.58
4.25
Hantang
Natural Transition
Breaking
Note: Analysis for dance generation under mixed genre condition.
In this section, we evaluate the ability of MEGADance to generate dances under mixed-genre condi- tions. Thanks to its autoregressive generation manner, MEGADance can maintain motion continuity even when the accompanying music contains transitions between distinct genres. For example, in challenging case transitioning from Han HanTang to Breaking, MEGADance not only produces highly coherent transitions but also preserves the distinctive stylistic characteristics of each genre before and after the switch, as shown Fig. controllability) we conducted an additional user study using the same music tracks and participant setup as described in Section 4.2. In each test sequence, the first half conditioned on genre and the second half on genre 2, with genre pairs randomly sampled. Participants rated the generated dances on 5- 5-point scale for Style Quality (SQ: alignment with the target genre) and Motion Quality (MQ: smoothness and naturalness). As summarized in Tab. M MEGADance achieves high MQ scores and clearly outperforms other methods in SQ for mixed-genrs generation.
further assess this capability (an essential aspect of genre
A.6 Questionnaire for User Study
User feedback is essential for evaluating generated dance movements in the music-to-dance gener- ation task, due the inherent subjectivity of dance [45]]. We select 30
music segments, each lasting 34 seconds, and generated dance sequences using the models described above. These se- quences are evaluated through double-blind questionnaire completed by participants with dance backgrounds, including undergraduate and graduate students. Participants are compensated exceeding the local average hourly wage. The questionnaires used a 5-point scale (Great, Good, Fair, Bad, Terrible) to assess three aspects: Dance Synchronization (DS, alignment with rhythm and style), Dance Quality (DQ, biomechanical plausibility and aesthetics), and Dance Creativity (DC, originality and range). The screenshot of our user study website is shown in Fig. E. displaying the template layout presented to the participants. In addition to the main trials, participants are also subjected to several catch trials, which involved displaying Ground Truth videos and videos with distorted motion. Participants who failed to rate the GT videos higher and the distorted motion videos lower are considered unresponsive inattentive, and their data are excluded from the final evaluation.
rate
A.7 Future Work
Customized Dance Generation While our current work successfully enables genre-aware control in music-to-dance generation, genre labels inherently impose rigid constraints and offer limited flex- ibility for user intent expression. Existing controllable generation approaches remain insufficiently expressive for practical deployment [3TL [IL Ø]. In future work, we plan to extend control modali- ties beyond predefined genre categories by incorporating free-form textual descriptions. Compared to genre labels, text allows users to articulate choreography requirements nuanced manner, facilitating personalized and expressive dance generation. This direction not only enhances user interactivity and creativity but also opens up new opportunities for content-driven applications in virtual performance and human-computer interaction.
more intuitive and
Roise-Resistant Dance Generation 3D motion capture data often suffer from noise artifacts such as sudden positional jumps or temporal discontinuities, as observed even in high-quality datasets like
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Rate score on these videos.
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Plecs for codl cor comerrent oinia riserre
Pleand on
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Oyet oegemen and proied!
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Figure The screenshots of user study website for participants.
FineDance[2].. Moreover, the limited scale of 3D dance datasets makes models prone overfitting. Future research should explore robust architectures and data augmentation strategies that maintain motion plausibility and stylistic coherence under noisy or incomplete input, thereby improving the reliability and generalization of music-to-dance generation systems.
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Reviews:
[{"review_id": "N3LIEmQ9pL", "reviewer": "Reviewer_oRpN", "summary": "This paper proposes an MOE-based architecture MEGADance for 3D music-to-dance generation by decoupling it into the dance content part and the genre style part. MEGADance consists of two stages - the High-Fidelity Dance Quantization Stage (HFDQ) and the Genre-Aware Dance Generation Stage (GADG). HFDQ learns the motion representation as discrete tokens through 3D motion reconstruction. Then the GADG generates motion tokens based on music features, genre features, and previously generated motion tokens in a generative way. To enhance the autoregressive generation process, MoE and Mamba-Transformer are applied. Experiments on two benchmark datasets show MEGADance outperforms current baselines, especially in genre controllability.", "questions": "Please see weakness.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\n- Experiments show that the proposed framework outperforms baselines on two datasets (FineDance and AIST++).\n- The MoE design to decouple genre and dance content sounds plausible.\n\nWeakness:\n- It's a little bit difficult to judge the novelty of the proposed framework. MEGADance is based on Transformer autoregressive decoder backbone with the generation target being discrete motion tokens, which is a relatively common practice for vision generation task using autoregressive Transformer. Although MEGADance proposes to include MoE and Mamba-Transformer for the autoregressive backbone, the core idea still remains the same and the proposed module/strategy themselves are not novel either. Motivation and analysis of the necessity of the proposed strategies are needed to help better understand the contribution.\n- The complexity analysis in time and memory of the proposed framework is missing.", "was_revised": true, "final_justification": "After reading the rebuttal, my concern on the motivation of the proposed method and complexity analysis has been solved.", "rebuttal": "**To Reviewer oRpN:**\n\n**Q1: Concerns on the novelty of MEGADance.**  \n**A1:** Thank you for the insightful comment. While autoregressive Transformer-based token generation is indeed a common paradigm, MEGADance introduces several key architectural distinctions beyond simple module application:  \n1.  **Structural Decoupling of Semantics**. Unlike monolithic sequence modeling in prior work, we explicitly decouple choreographic semantics into: Genre-invariant rhythmic structure (modeled by **Universal Expert**) and Genre-specific stylistic priors (modeled by **Specialized Experts**). This reflects real-world choreographic composition and improves both genre control and generalization.  Ablation (Table 4a): Removing either of these two structures significantly degrades genre fidelity or structural coherence.  \n2.  **Task-Specific Modular Routing via MoE**. MEGADance uses a **genre-conditioned hard routing policy** where each sample activates a single genre-specific expert, which differs fundamentally from Conventional sparse MoE (e.g., for language modeling) where activation is learned. Our approach encodes task-specific inductive bias and prevents expert collapse.  \n3.  **Hybrid Modeling of Cross/Intra-modal Dynamics**. We replace homogeneous Transformers with a **Mamba–Transformer hybrid**: Mamba captures fine-grained *intra-modal continuity* (especially motion), and **Transformer** handles long-range *cross-modal alignment* (music and dance). This addresses Transformers' lack of inductive bias for temporal locality. Ablation (Table 4a): Hybrid modeling design improves generation quality and synchronization.  \nWe will further clarify these points in the revision.\n\n\n\n**Q2: Suggestions on the complexity analysis.**  \n**A2:** Thank you for the helpful suggestion. We make following clarification regarding this issue:  \n1.  **Runtime Latency Focus** . In practical applications, user priority is run-time latency (reported in Line 279, Section 4.2). This remains our primary efficiency metric.  \n2.  **Complexity Analysis Expansion**. As suggested, we now include comparison for generating **a 1024-frame (34.13s) sequence**. MEGADance activates only relevant experts (not all) during computation. All benchmarks run on **RTX 3090 GPU + Intel Xeon Gold 5218 CPU**. As summarized below, MEGDance has slightly higher complexity than FineNet but substantially better generation quality (refer to Tables 1-3). Compared to Bailando++ and Lodge, it has lower latency and fewer parameters while maintaining superior performance. These results reflect MEGDance's favorable trade-off between efficiency and quality. We will include this analysis in the revision.\n\n| Model        | Parameters (↓) | Run Time (↓) |\n|--------------|----------------|--------------|\n| Bailando++   | 152M           | 5.46s       |\n| FineNet      | **94M**            | **3.97s**       |\n| Lodge        | 235M           | 4.57s       |\n| *MEGADance* | *120M*       |  *4.31s*  |"}, {"review_id": "thRPDX9o5b", "reviewer": "Reviewer_xz7a", "summary": "This paper presents a system for music-conditioned and genre-aware dance motion generation. It contains some key innovative components. Finite scalar quantization (FSQ) is used to encode motions into latent space representations. Mixtures of genre-specialized and universal experts are then used to generate genre-aware dance motion sequences. To capture both long-range dependencies and finer local variations in motion, these experts are implemented using combinations of Mamba and Transformers. Through a number of experiments, the proposed system was evaluated with key characteristics highlighted.", "questions": "The paper includes some rather specific qualities of what the specialized and universal experts are expected to capture, but how can you know that these are true? The only difference seems to be a switch based on the genre when selecting the specialized expert.\n\nWhere do the numbers for Bailando++ come from, given that they seem to be different from the original paper or what has been published in other papers? Is it related to the fact that this method is not genre-aware?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The proposed system is shown to be very competitive compared to the state-of-the-art when tested on the two most common datasets. The best results are shown in the genre controllability evaluation, which is indeed what the paper aims to improve. A rather extensive ablation study further highlights the importance of the different components of the system.\n\nThe paper is very well written and structured, which includes illustrations. Even if it contains a lot of content, it is still rather easy to read and understand. The proposed architectural components are both easy to understand and make perfect sense. One of the more intriguing components of the proposed system is FSQ for quantization. FSQ is more lightweight than VQ-VAE and doesn't require a codebook to be trained, a codebook that may be poorly utilized. In the ablation study provided in the paper, FSQ is shown to perform better than VQ-VAE, despite its simplicity.\n\nAnother characteristic component that makes sense, and is supported by positive results, is the distinction between specialized and universal experts. Even if there are many different genres in dance, there are few enough to motivate a specialized expert for each, rather than having a single generic generator and the genre as an argument that conditions the output.\n\nThe combination of Mamba and Transformers is not novel, but appears to be suitable for the task. Transformers are known to be good at handling long-range dependencies and interaction between music and motion, while Mamba may be better at capturing local variations in motion. The benefit of the combination is clear from the reported results.\n\nIn the comparison between different methods, the number of tested methods is quite low. There are more methods in the literature than those shown in the paper, especially if you look back in time. Those included, however, are representative of the current state-of-the-art. The numbers seem to have come from the original papers, except for Bailando++, where the numbers are worse than what has been reported earlier. There ought to be a good reason for this to be clarified.\n\nMinor things: The captions of Fig. 4a and Fig. 4b seem to be switched. Sometimes there are spaces before citations, and sometimes not. It would be better to make it more uniform. There is a missing bold value for FACT in Table 2. In the same table, it ought to be $DIV_k$ and $DIV_g$, not $DIV_g$ and $DIV_s$.", "was_revised": true, "final_justification": "The rebuttal addressed the few concerns raised in good a manner. There is thus no reason to change recommendation.", "rebuttal": "**To Reviewer xz7a:**\n\n**Q1: Questions about the experimental result of Bailando++.**  \n**A1:** Thank you for the insightful question. Here is our answer: \n1. The reported results for Bailando++ are obtained using the publicly available official code, which we retrained and evaluated following the instructions. Despite our efforts to replicate the original experimental setup, we were unable to reproduce the exact numbers reported in the paper. Therefore, we report our evaluation results for fair and consistent comparison across all baselines under the same environment. We will explicitly annotate in the revised table that these numbers are from our reproduction.  \n2. Regarding your question about genre awareness: it is true that Bailando++ does not incorporate genre conditioning, which may partially explain its lower performance compared to genre-aware methods like MEGADance. However, this difference in model design does not account for the discrepancy between our reproduced results and those reported in the original paper. We will clarify it in the revision.\n\n**Q2: Typos in the paper.**  \n**A2:** Thank you for pointing them out. The following is our response:\n1. We acknowledge the typo in *Table 2*—column headers should be $DIV_k$ and $DIV_g$.  \n2. The $DIV_g$ value of FACT in *Table 2* should indeed be bolded. \n3. We will standardize citation spacing throughout the paper. \n4. We would like to point out that *Figure 4* does not contain subfigures (a) and (b).\n\nThese typos will be corrected in the revision.\n\n**Q3: Concerns on whether specialized and universal experts truly capture distinct roles as intended.**  \n**A3:** Thank you for the insightful question. Here is our answer:\n1. $FID_k$ and $FID_g$ primarily reflect universal dance quality, while $FID_s$ captures genre-specific fidelity. As shown in *Table 4*, removing the Universal Expert (*UE*) notably degrades $FID_k$ and $FID_g$, whereas removing the Specialized Expert (*SE*) leads to a sharp drop in $FID_s$—indicating their distinct contributions. \n2. We further conducted a *t-SNE* analysis on the final-layer features of *SE* and *UE*. Visualizing genre-specific color-coded clusters, *SE* features form well-separated clusters while *UE* features do not—empirically supporting their intended functional distinction. As figures are not allowed during the rebuttal phase, we only provide this textual description.  \n\nWe will include both figure and explanation in the revision."}, {"review_id": "gdAtCBLP1b", "reviewer": "Reviewer_1pZx", "summary": "The authors propose a two-stage music-to-dance generation framework that integrates a Mixture-of-Experts (MoE) architecture, Finite Scalar Quantization (FSQ) with kinematic-dynamic dual constraints, and a Mamba-Transformer hybrid backbone. The model is designed to generate high-fidelity and genre-controllable dance motions.", "questions": "1.\tMany prior works have pointed out that autoregressive frameworks often suffer from error accumulation and motion freezing [8], how did the authors address this issue?\n\n2.\tPlease refer to the weaknesses section.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The paper introduces a two-stage music-to-dance generation model that combines MoE, FSQ, and a Mamba-Transformer backbone to improve motion fidelity and genre controllability.\n\nStrengths\n\n(1)\tBy using a Mixture-of-Experts (MoE) to separate genre-specific and shared motion patterns, and combining it with Finite Scalar Quantization (FSQ) and a Mamba-Transformer hybrid backbone, the model achieves better choreographic consistency and motion fidelity. This combination is both original and novel, offering an effective solution for controllable and high-fidelity dance generation.\n\n(2)\tThe paper presents solid experimental comparisons and ablation studies, supported by comprehensive visualizations to validate the effectiveness of each component.\n\nWeaknesses:\n\n(1)\tWhile the authors claim to compare against multiple methods, Table 1 only includes two recent baselines: FineDance (2023) and LODGE (2024), with no broader comparison to other relevant or competitive approaches. Moreover, it is unclear why Table 2 omits standard metrics FID_s and DIV_k for the AIST++ evaluation.\n\n(2)\tAlthough the paper claims that both the MoE design and the Mamba-Transformer backbone improve computational efficiency, only the inference time of the proposed model is reported. It would strengthen the argument to provide FLOPs or latency comparisons with other models, or at least include ablation results showing the impact of these modules on efficiency.\n\n(3)\tThe loss formulation involving velocity and acceleration (Eq. 3) lacks clarity. Notations such as S’ and S’’ are introduced without explanation. It would be helpful to rewrite the equation in a more explicit form.", "was_revised": true, "final_justification": "The authors' rebuttal has addressed most of my concerns. After reading the other reviewers' comments, I believe the manuscript is generally suitable for publishing in NeurIPS. However, the authors may need to address all the comments carefully in the camera-ready version. In this case, I remain with my original recommendation.", "rebuttal": "**To Reviewer 1pZx**\n\n**Q1: Concerns on the evaluations on AIST++.**  \n**A1:** Thank you for the helpful suggestion. We clarify as follows:\n1. *Table 2* contains a typo and it actually represents $DIV_k$ and $DIV_g$. \n2. *FineDance*, as the largest dataset with more diverse genres, already provides strong evidence of *MEGADance*'s superiority through its competitive $FID_s$ and $DIV_s$ results. And most baseline methods on *AIST++* do not incorporate genre conditioning (except *Lodge*), making direct comparisons on $FID_s$ and $DIV_s$ potentially unfair. \n4. Following your suggestion, we additionally report *AIST++* results on $FID_s$ and $DIV_s$. As summarized below, *MEGADance* substantially outperforms all baselines. We will include this in the revision.\n\n| Model       | FID_s | DIV_s |\n|-------------|-------|-------|\n| GT          | 0.00  | 7.24  |\n| FACT        | 33.80 | 4.68  |\n| Bailando++  | 22.75 | 4.32  |\n| EDGE        | 19.63 | 5.37  |\n| Lodge       | 13.85 | 5.93  |\n| MEGADance | **7.12** | **6.76** |\n\n**Q2: Suggestions on providing the computational cost comparison or ablation.**  \n**A2:** Thank you for the helpful suggestion. We make clarification as follows:  \n1. In practical applications, users usually prioritize *inference latency* (reported in Line 279, Section 4.2). *MEGADance* achieves **0.19 seconds per second** of generated dance, enabling real-time responsiveness. \n2. We agree that providing additional computational cost analysis, such as Parameters and Run Time comparisons, helps clarify the efficiency benefits of our design. We now include a comparison for generating a 1024-frame (34.13s) dance sequence. Importantly, due to sparse expert activation, MEGADance only activates one Specialized Expert per input, which significantly reduces the effective computation during inference. All “Run Time” evaluations were conducted on an RTX 3090 GPU with an Intel Xeon Gold 5218 CPU. As summarized below, MEGDance has slightly higher complexity than FineNet but substantially better generation quality (refer to Tables 1-3). Compared to Bailando++ and Lodge, it achieves lower latency and fewer parameters while maintaining superior performance. These results reflect MEGDance's favorable trade-off between efficiency and quality. We will include this analysis in the revision.\n\n| Model       | Parameters(↓) | Run Time(↓) |\n|-------------|---------------|-------------|\n| Bailando++  | 152M          | 5.46s      |\n| FineNet     | **94M**           | **3.97s**      |\n| Lodge       | 235M          | 4.57s      |\n| MEGADance | *120M*     | *4.31s*  |\n\n3. Since MEGADance already achieves real-time performance, our ablation study primarily focuses on generation quality and controllability rather than parameters or runtime, as these aspects are more critical for evaluating the effectiveness of our design—particularly in genre-aware choreography generation. However, we agree that computational efficiency is an important perspective and will discuss it in the revision.\n\n**Q3: Suggestions on the statement of Eq. 3.**  \n**A3:** Thank you for the helpful suggestion. In *Eq. 3*:\n- `S′` and `S″` denote first- and second-order derivatives of `S`\n- `J′` and `J″` denote first- and second-order derivatives of `J`  \nWe will clarify these in the revision.\n\n**Q4: Question about the exposure bias in autoregressive manner.**  \n**A4:** Thank you for the insightful question. Here is our response:  \n1.In practice, exposure bias does not always have a significant negative impact in motion generation tasks, especially when strong temporal models (e.g., Transformer or Mamba) and well-structured conditions are employed—models trained with cross-entropy loss can still produce high-quality sequences.  \n2. Following your suggestion, we implemented Scheduled Sampling (SS) to mitigate exposure bias. As shown in the table below, some metrics even slightly degrade with SS. We will include this discussion in the revision.\n\n| Model          | FID_k↓ | FID_g↓ | FID_s↓ | DIV_k↑ | DIV_g↑ | DIV_s↑ | BAS↑ |\n|----------------|--------|--------|--------|--------|--------|--------|------|\n| MEGADance      | 50.00  | 13.02  | 2.52   | 6.23   | 6.27   | 5.78   | 0.226|\n| MEGADance (SS) | 42.36  | 16.21  | 3.47   | 5.18   | 7.04   | 6.33   | 0.228|"}, {"review_id": "pjVjcCSDcV", "reviewer": "Reviewer_Yz76", "summary": "In this work, the authors introduce MEGADance, a novel two-stage architecture for music-driven 3D dance generation that emphasizes genre-aware choreography. This is achieved by decoupling dance generality and genre specificity across two stages. In the first stage, MEGADance uses Finite Scalar Quantization to learn a diverse motion codebook. Combined with kinematic and dynamic constraints, FSQ improves reconstruction fidelity and motion plausibility. In the second stage, the MEGADance uses a Mixture-of-Experts (MoE) model, where Universal Experts model genre-invariant motion patterns and Specialized Experts model genre-specific patterns, via hard routing genre labels. Each expert is implemented as an autoregressive Mamba-Transformer, enabling both local and global temporal modeling. In part because of its strong inductive biases and genre sensitivity, MEGADance outperforms prior methods across quantitative metrics as well as human evaluations.", "questions": "1. Could authors provide any information on model parameter complexity? The stated generation runtime of 0.19 second is helpful but it would be better to understand the computational cost of the model as a whole, especially as a comparison to previous models. More broadly, quoting the NeurIPS Paper Checklist (line 607), authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.\n\n2. How well does MEGADance scale when new genres are added?\nSince the Specialized Experts are tied to specific genres, it's unclear how the model handles genre extension without full retraining. Could experts be modularly added or fine-tuned?\n\n3. Can MEGADance generate dances that blend multiple genres?\nIn its current form, hard routing limits the model to one genre at a time. Authors mention in App4 that free form text would be an interesting next step to allow for more creative generation... but I am curious to hear if there could be an even simpler next step. For example, would it be possible to mix Specialized Experts' latents? This reminds me of the field of style transfer, that studies this exact sort of exploration of the latent space, ex. Yin et. al https://arxiv.org/abs/2208.09406.", "limitations": "A limitations section is included in App4, although I would like to see it padded with information that I have asked about above, such as computational cost, scaling with new data, and more flexible generation modes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Quality**\n- S: Overall this is a high-quality paper. It is well-written, proposes novel and strong methodology, and performs extensive experiments with convincing quantitative and qualitative results.\t\n- W: See question 1 about complexity analysis; this is important information that appears to be totally left out. Also, the experiments don't show any error bars, although this appears to be the case for other papers in the field. Could the authors expand on this?\n- W: I would have appreciated if the authors could share the code for the purposes of evaluating how reproducible it seems. I understand anonymity concerns prevent a GitHub link, but there are other ways of sharing code anonymously, such as using a code anonymizing website like https://anonymous.4open.science/.\n\n**Significance / Originality**\n- S: The model carries real-world relevance from a creative standpoint, as it enables genre-aware music-generated choreography, albeit only for genres cleanly labelled in training data.\n- S: MoE + FSQ + Mamba-Transformer is novel in this domain.\n- W: It remains unclear how well MEGADance generalizes or adapts to new genres (i.e., how simple would this be? how expensive would this be?). This imposes limits on the architecture and its use cases in any applied scenario. See Q2 / Q3 on this point as well.\n\n**Clarity**\n- S: The text is well written overall and the math notation is consistent.\n- W: This is minor, but I would appreciate better figure captions. For example, the master architecture figure (Fig. 2) should have a clear caption that walks the reader through Stage 1, then Stage 2, and breaks down the components of the figure. It is only clear to me where the blue and pink boxes fit in with respect to the left box after I have studied the methodology, whereas it should be pretty obvious to someone just skimming the paper.", "was_revised": true, "final_justification": "The authors address my (generally lightweight) concerns in a satisfactory manner. However, I don't believe the paper is ``groundbreaking\" in such a way that would warrant raising my score to a 6.", "rebuttal": "**To Reviewer Yz76:**\n\n**Q1: Suggestions on providing the complexity and scalability analysis.**  \n**A1:** Thank you for the insightful question. Beyond generation latency, we agree that analyzing model complexity and scalability is essential, and we make following clarifications:\n1. In practical deployment scenarios, runtime latency is typically the most critical metric, which is why we reported it in Line 279 (Section 4.2). MEGADance achieves 0.19s per second of generated dance, enabling real-time applications.\n2. As you pointed out, complexity analysis is also important. Thus, we include a comparison of complexity for generating a 1024-frame (34.13s) dance sequence. Note that MEGDance activates only the relevant experts rather than all experts when computing \"Parameters\". All \"Run Time\" evaluations were conducted on an RTX 3090 GPU with an Intel Xeon Gold 5218 CPU. As summarized below, MEGDance has slightly higher complexity than FineNet but substantially better generation quality (refer to Tables 1-3 for details). Compared to Bailando++ and Lodge, it has lower latency and fewer parameters while maintaining superior performance. These results demonstrate MEGDance's favorable trade-off between efficiency and quality. We will include this analysis in the revision.\n| Model | Parameters↓ | Run Time ↓ |\n|-------|-------------|------------|\n| Bailando++ | 152M | 5.46 s |\n| FineNet | 94M | 3.97 s |\n| Lodge | 235M | 4.57 s |\n| MEGDance | 120M | 4.31 s |\n3. Regarding scalability with dataset size, MEGADance exhibits favorable scaling properties. Its two-stage structure benefits from increased training data: the FSQ-based quantization achieves better codebook coverage, while the genre-aware MoE design enables each expert to specialize more effectively. Moreover, the sparse routing in our MoE ensures that only one expert is activated per input, keeping computational cost nearly constant regardless of the total number of genres. We have evaluated performance of MEGADance on two large-scale datasets (FineDance and AIST++), and observed stable and generalizable performance across varied genre distributions, as shown in Tables 1 and 2.\n\n**Q2: Suggestion on sharing the code.**  \n**A2:** Thank you for the helpful suggestion. We will release the MEGDance code to benefit the community. As NeurIPS policy prohibits external links in the rebuttal, the code will be made publicly available in the final revision.\n\n**Q3: Concerns about the generalization ability of MEGADance to unseen genres**  \n**A3:** Thank you for the insightful question. As discussed in Section 3.3.1 (Lines 176-192), MEGADance adopts a Mixture-of-Experts (MoE) design where Specialized Experts are selected based on discrete genre labels. This modular architecture inherently supports genre scalability. Specifically: (1) The General Expert and existing Specialized Experts can be retained; (2) A new Specialized Expert can be added and trained solely on a subset containing the new genre; (3) Only the genre-to-expert mapping in the routing policy needs to be updated—no structural changes are required.  \nTo validate this capability, we conducted an additional experiment: we removed Dunhuang and Breaking from the original 16-genre training set, and then reintroduced each genre using the extension strategy stated above. As shown below, the performance gap compared to full retraining is negligible, demonstrating MEGDance's ability to handle genre extension effectively without requiring full retraining. We will include this result in the revision.\n| Model | FID_k↓ | FID_g↓ | FID_s↓ | DIV_k↑ | DIV_g↑ | DIV_s↑ | BAS↑ |\n|-------|--------|--------|--------|--------|--------|--------|------|\n| MEGADance | 50.00 | 13.02 | 2.52 | 6.23 | 6.27 | 5.78 | 0.226 |\n| MEGADance (-Dunhuang) | 54.70 | 14.10 | 2.41 | 6.35 | 6.12 | 5.61 | 0.224 |\n| MEGADance (-Breaking) | 51.90 | 13.30 | 2.60 | 6.18 | 6.24 | 5.94 | 0.211 |\n\n**Q4: Question on MEGADance's ability to generate dances that blend multiple genres.**  \n**A4:** Thank you for the insightful question. Here is our answer: \n1. As you suggested, all experts in MEGDance share a unified codebook, so the latent tokens (i.e., FSQ indices) produced by different Specialized Experts reside in the same space. This allows direct addition, interpolation, and fusion of latent representations, making mixed-genre dance generation a viable and promising direction.\n2. Furthermore, MEGDance’s autoregressive generation scheme ensures strong motion continuity, even when switching genres mid-sequence (e.g., from Hangtang to Breaking). As NeurIPS policy prohibits external links in the rebuttal, we will include corresponding visualizations in the revision.\n3. To evaluate this capability—an essential aspect of genre controllability—we conducted an additional user study using the same music and participant setup as shown in Section 4.2. Specifically, the first half of each sequence was conditioned on genre1 and the second half on genre 2, where genre pairs were randomly sampled. Participants rated the results on a 5-point scale for Style Quality (SQ: alignment with target genre) and Motion Quality (MQ: smoothness and naturalness). As summarized below, MEGDance achieves high MQ and clearly outperforms other methods in SQ for mixed-genre generation. We will include this in the revision.\n| Model | SQ | MQ |\n|-------|----|----|\n| FineNet | 2.81 | 4.12 |\n| Lodge | 3.14 | 4.27 |\n| MEGADance | 4.58 | 4.25 |\n\n**Q5: Concerns about the absence of variance reporting in experiments.**  \n**A5:** Thank you for the insightful question. Here is our answer: (1) In music-to-dance generation, prior works [1,2] typically report mean performance without variance metrics, as evaluations are based on deterministic inference under fixed settings. We followed this convention to ensure comparability. (2) However, we agree that reporting variance is valuable. We now additionally provide standard deviations of our main evaluation metrics below. We will include it in the revision.\n| Model | FID_k ↓ | FID_g ↓ | FID_s ↓ | DIV_k ↑ | DIV_g ↑ | DIV_s ↑ | BAS ↑ |\n|-------|----------|----------|----------|----------|----------|----------|--------|\n| MEGADance | 50.00 ± 4.42 | 13.02 ± 3.67 | 2.52 ± 0.43 | 6.23 ± 0.21 | 6.27 ± 0.10 | 5.78 ± 0.09 | 0.226 ± 0.005 |\n\n[1] Li, Ronghui, et al. \"Lodge: A coarse to fine diffusion network for long dance generation guided by the characteristic dance primitives.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.  \n[2] Li, Ronghui, et al. \"Finedance: A fine-grained choreography dataset for 3d full body dance generation.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n\n**Q6: Suggestions the clarity and readability of Fig. 2.**  \n**A6:** Thank you for the helpful suggestion. We will revise the caption of Fig. 2 to improve clarity and readability, as follows:  \nFigure 2: Overview of MEGADance. Stage 1 (HFDQ) quantizes dance into upper/lower-body latent codes using FSQ with kinematic and dynamic reconstruction constraints. Stage 2 (GADG) maps music to codes via an $L$-layer Mixture-of-Experts architecture (blue: specialized, pink: universal), where each expert is equipped with a Mamba-Transformer hybrid backbone."}]

Author rebuttal:

Meta-review: This manuscript introduces MEGADance, a novel two-stage MoE architecture for genre-aware 3D dance generation, explicitly decoupling dance generality (universal expert) and genre specificity (specialized experts) via FSQ quantization and a Mamba-Transformer hybrid backbone. It achieves SOTA results and modular extensibility to new genres. Weaknesses initially cited (novelty, missing comparisons, scalability) were resolved in rebuttal. All reviewers converged on acceptance post-rebuttal, confirming the technical soundness and readiness of this work. Therefore, I recommend acceptance of this manuscript.
Decision: Accept (poster)