ReactDance: Hierarchical Representation for High-Fidelity and Coherent Long-Form Reactive Dance Generation
Authors: Jingzhong Lin
Xinru Li
Yuanyuan Qi
Bohao Zhang
Wenxiang Liu
Kecheng Tang
Wenxuan Huang
Xiangfeng Xu
Bangyan Li
Changbo Wang
Gaoqi He
Venue: ICLR 2026 Poster (2026)
Keywords: diffusion
dance generation
motion generation
Primary area: generative models
TL;DR: ReactDance: A diffusion framework for reactive dance generation using hierarchical latent representation and achieving efficient long-term coherence through inference-training temporal alignment.

Abstract: Reactive dance generation (RDG), the task of generating a dance conditioned on a lead dancer's motion, holds significant promise for enhancing human-robot interaction and immersive digital entertainment. Despite progress in duet synchronization and motion-music alignment, two key challenges remain: generating fine-grained spatial interactions and ensuring long-term temporal coherence. In this work, we introduce $\textbf{ReactDance}$, a diffusion framework that operates on a novel hierarchical latent space to address these spatiotemporal challenges in RDG. First, for fine-grained spatial control and artistic expression, we propose Hierarchical Finite Scalar Quantization ($\textbf{HFSQ}$). This multi-scale motion representation effectively disentangles coarse body posture from high-frequency dynamics, enabling independent and detailed control over both aspects through a layered guidance mechanism. Second, to efficiently generate long sequences with high temporal coherence, we propose Blockwise Local Context ($\textbf{BLC}$), a non-autoregressive sampling strategy. Departing from slow, frame-by-frame generation, BLC partitions the sequence into blocks and synthesizes them in parallel via periodic causal masking and positional encodings. Coherence across these blocks is ensured by a dense sliding-window training approach that enriches the representation with local temporal context. Extensive experiments show that ReactDance substantially outperforms state-of-the-art methods in motion quality, long-term coherence, and sampling efficiency.

Published as a conference paper at ICLR 2026
REACTDANCE: HIERARCHICAL REPRESENTATION FOR HIGH-FIDELITY AND COHERENT TIVE DANCE GENERATION
REAC-
Jingzhong Lin', Xinru Li', Yuanyuan Qi', Bohao Zhang', Wenxiang Liu', Kecheng Tang', Wenxuan Huang', Xiangfeng Xu Bangyan Li Changho Wang', Gaoqi He
East China Normal University
ABSTRACT
Reactive dance generation (RDG), the task of generating a dance conditioned on a leader's motion and music, holds significant promise for enhancing human-robot interaction and immersive digital entertainment. Despite progress in duet syn- chronization and motion-music alignment, two key challenges remain: generating fine-graines spatial interactions and ensuring long-term temporal coherence. In this work, we introduce ReactDance, diffusion framework that operates on novel hi- erarchical latent space to address these spatiotemporal challenges in RDG. First, for high-fidelity spatial expression and fine- aeeeggraied control, we propose Hierarchical Finite Scalar Quantization (HFSQ). This multi-scale motion representation effee- tively disentangles coarse body posture from high-frequenn frequency dynamics, enabling in- dependent and detailed control over both aspects through layered guidance mecha- nism. Second, to efficiently generate long sequences with high temporal coherence, we propose Blockwise Local Context (BLC), -ooreuttreeressvvee sampling strat- egy. Departing from slow, Trame-by-frame generation, BLC partitions the sequence into blocks and synthesizes them in parallel via periodic causal masking and positional encodings. Coherence across these blocks is ensured by a dense sliding- window training approach that enriches the representation with local temporal con- text. Extensive experiments show that ReactDance substantially outperforms state- of-the-art methods motion quality, long-term coherence, and sampling efficiency. Project page: https: /tappp://rrieee /tt.ppeaammmmooxo...m/ github. io/ //eeatttaannce
INTRODUCTION
Reactive Dance Generation (RDG) aims to synthesize lifelike and artistically coherent reactor mo- tions that align with lead dancer and accompanying music. This form of responsive interaction embodies a complex interplay of artistic expression and interpersonal dynamics that is a key challenge in fields like social robotics and human-agent interaction. The ability to generate such intricate, reactive behaviors holds significant potential for applications like virtual avatar animation for gam- ing and the metaverse (Hu, 2024; Zhang al., 2023), well as embodied AI and human-robot interaction (Granados et al., 2017; Chen et al., 2025).
Despite its promise, RDG faces two critical, unaddressed challenges: (1) modeling fine-grained spatial interactions and (2) maintaining long-term temporal coherence. While recent methods have advanced duet synchronization using tailored network architectures (Yao et al., 2023: Liang et al., 2024; Ghosh et al., 2024). physical constraints (Wang et al., 2024; Xu et al., 2024), or reinforcement learning (Siyao al., 2024), they fail to resolve these core issues and fall short of generating truly authentic dance sequences. Their reliance holistic, high-level constraints overlooks the subtle yet decisive local movements (e.g., the whip-like boleo in Tango), producing movements that are synchronized yet artistically sterile. Moreover, fundamental difficulty for RDG is ensuring long- term temporal coherence. Models are typically trained on short clips, a practice driven by both the
Corresponding Author.

---

Published as a conference paper at ICLR 2026
Pose Mismatch
Torso- Anso-Overrotation
Wrong Orientation
Duolando
240 Frames
Uncoordinated Interaction
Inference
Ours
Time
2000+ Frames (1 Minute+)
Inference 2s
Figure I: ReactDance generates high-fidelity, long-form reactive dance Conditioned leader and music, our model uses a hierarchical representation to refine motion from coarse (translucent) to fine-grained (solid). Its parallel sampling mechanism produces coherent sequences exceeding 2000 frames within seconds, avoiding the quality degradation and slow speed of autoregressive sampling.
algorithmic complexity of learning long-term dependencies and practical computational constraints. This creates inherent discrepancy between training and inference, leading to error accumulation that manifests as temporal drift and an eventual breakdown synchronization.
To address these limitations, we draw inspiration from structural principles of dance theory and propose two core concepts for generating high-fidelity reactive motion:(1) Hierarchical Motion Composition: The principle that dance performance inherently layered, with foundational full-body rhythms providing a coarse structure and fine-grained dynamics adding detailed semantics (Seham, (21 Modular Temporal Coherence: The observation that long-form choreography achieves consistency by composing shorter, coherent motion phrases and ensuring smooth transitions between them (Krug, 2022).
Grounded in these principles, we propose ReactDance, a two-stage diffusion framework that opera- tionalizes this hierarchical and modular approach.
First, to achieve hierarchical refinement, we introduce a Hierarchical Finite Scalar Quantization (HFSQ) representation. Unlike traditional VQ-VAE models (Gong et al., 2023; Jiang et al., 2024) prone to codebook collapse, HFSQ combines the stability of FSQ (Mentzer et al., 2024) with hierarchical structure (Yang et al., 2023) build robust, multi-scale scale latent space. This representation explicitly disentangles coarse global movements from fininne local details. Building on this, we develop
Classifier-Free Guidance (LDCFG), technique providing independent, layered control over motion semantics at different granularities.
Second, to ensure long-term coherence, we introduce Blockwise Local Context (BLC) sampling mechanism. BLC replaces fragile autoregressive sampling with efficient, parallel generation of all motion blocks. Long-term consistency is achieved by precisely aligning each block's 's temporal context inference with the windows learned during our dense sliding-window window (DSW) training. This alignment, enforced using periodic causal masking and positional encodings, effectively eliminates temporal drift. As result, ReactDance efficiently generates coherent sequences exceeding 60 seconds within 2 seconds.
In summary, main contributions are:
We introduce ReactDance, hierarchical framework for Reactive Dance Generation (RDG) that produces high-fidelity, coherent reactive dance sequences. leverages a novel Hierarchical Finite Scalar Quantization (HFSQ) representation to progressively model the reactive performance from
2

---

Published as a conference paper at ICLR 2026
coarse body rhythms to fine-gratn fine-grained local movements, effectively addressing challenges in spatial interaction refinement.
We propose Blockwise Local Context (BLC), novel parallel sampling mechanism that enables coherent generation of long motion sequences within 2 seconds for over 2000 frames, building on temporally continuous latent space. By decomposing the generation process into blocks aligned with the temporal context of training, BLC mitigates error accumulation inherent in prior autoregressive methods, achieving state-of-the-an long-term stability and sampling efficiency.
We propose Layer-Decoupled Classifier-Free Guidance (LDCFG), novel guidance technique tai- lored for the HFSQ representation. LDCFG enables independent conditioning over different scales of the generated motion, offering finer-grained semantic manipulation of reactive performances compared to existing approaches.
RELATED WORKS
2.1 MUSIC-DRIVEN DANCE GENERATION
Music-driven dance generation has advanced from solo to multi-dancer interactions. Early music- driven dance generation relied on
models like LSTMs (Tang et al., 2018) and GANs (Li et al., 2022), but often suffered from rigid transitions and lack of detail. While auto-regressive models (Li et al., 2021; Siyao et al., 2022) improved solo dance quality, they lacked mechanisms for duet interactions. Recent duet-specific models still fall short; approaches like Dan' (Yao et al., 2023) and Duolando (Siyao et al., 2024) use single-scale representations, causing synchronization artifacts, while DuetGen's 's (Ghosh al.. 2025) coupled representation models the joint distribution, inherently limiting the conditional diversity of reactor responses given fixed leader. In contrast, we introduce a hierarchical representation that captures multi-scale spatial dynamics to generate high-fidelity, coherent reactive duets.
2.2 HIERARCHICAL MOTTON GENERATION MODELS
The quality and controllability of generated motion are critically dependent on its underlying represen- tation. Codebook-basd models (Gong al., 2023; Jiang al., 2024) are prone to codehook collapse and detail loss from VQ-VAE (Oord et al., 2017) quantization. Although MoMask (Guo et al., 2024) introduces residual structures, its reliance on RVQ-VAE and serial generation causes cumulative errors across scales. While diffusion models (Qi et al., 2025; Liu et al., 2025) achieve state-of-the-art fidelity, they typically operate on flat representations, lacking structure for multi-level control. Existing hierarchical models (Qi et al., 2023; Li et al., 2024) have focused on temporal, not spatial, refinement. Our work fills this gap by introducing a spatial hierarchy that enables both sideliideitty synthesis and multi-scale control over the generated motion.
2.3 LONG MOTION SEQUENCE GENERATION
Generating coherent long motion sequences remains challenging. For text-to-motion, methods like MotionStreamer (Xiao al., 2025) and priorMDM (Shafir et al., 2024a) two-pass refinement to ensure smooth transitions between clips. The problem is harder in music-to-dande which requires Long-range musical coherence (Tsakalidis, 2020). Prior solutions iterative inpainting in EDGE (Tseng et al., 2023), or inflexible, like the primitive-basee temporal hierarchy in Lodge (Li et al., 2024). In contrast, approach enables efficient, parallel generation by training on overlapping windows, directly embedding temporal context into our representation and avoiding the costly overhead of previous methods.
often inefficient, such as the
METHOD
Our proposed framework, ReactDance, generates coherent long reactive dance sequence in two- stage process. First, an autoencoder with a Hierarchical Finite Scalar Quantizer (HFSQ) bottleneck learns encode dance movements into hierarchical latent representation. Second, conditional diffusion model learns to generate HFSQ latent representations based on leader movements and background music, which then decoded into the final motion.
3

---

Published as a conference paper at ICLR 2026
latent
Residual
Scaling
chunk
Course
F5Q
Motions
FS
Decoder
Encoder
Motion
F5Q
Resolution
PSQ
concat
FSQLR
Fine
Motions
Metion Encoding
HFSQ Representing
(c) Motion Decoding
kinematic
Figure 2: Motion HFSQ overview. The proposed motion HFSQ learns to progressively encode G,R motion sequence into hierarchical representation V g g=1.r=1 and reconstructs motions via a grouped residual architecture. Building on FSQ. HFSQ eliminates codebook collapse while enhancing multi-scale motion expressiveness through grouped residual quantization, which integrates coarse-to- sarse-to-ffne motion semantics.
3.1 MOTION AND MUSIC REPRESENTATION
Motion Representation. We represent a dance sequence of N frames using skeleton with J = 22 joints from the SMPL model (Loper et al., 2023). To better capture the dynamics reactive dance, approach utilizes asymmetric representation for the leader and the reactor.
The leader's motion modeled holistically as single sequence of global joint positions, denoted as Mr E x In contrast, the reactor's motion is decomposed into three independent components: (I) Upper-body motion RMuup E xup The localized positions of the Jup upper-body joints relative to the reactor's own root joint. (2) Lower-body motion (M Rdown TR x.NxxJan JJown x3): The localized positions of the Jaown lower-body joints relative to the reactor's
root joint. (3) Relative root distance (M,r E R2 x3): The displacement vector between the leader's and reactor's roots.
Music Representation. For the accompanying music, use Librosa (Brian McFee et al., 2015) to extract a rich acoustic feature vector c R44 for each corresponding motion frame. This vector comprises 20- 20-dimensional MFCCs, their 20-dimensional deltas, 12-dimensional chroma features, and 1-dimensional onset strength and beat tracking signals.
Our goal is thus generate the of reactor motion components M Rups M Midoon M, conditioned on the music and the leader's 's motion M,. To process the complete reactor motion MR. each of its components passed through dedicated and structurally identical HFSQ stream. For clarity, the following sections will describe the core methodology for a single holistic stream.
3.2 HIERARCHICAL MOTION REPRESENTATION VIA HFSQ
We learn a compressed and structured representation of motion using a hierarchical finite scalar quantizer, inspired by recent work in neural audio codecs (Yang al., 2023; Mentzer et al., 2024).
Encoder and Quantizer. A temporal encoder, consisting of ID convolutional layers, maps an input reactor motion sequence Mg E NyJR to a downsampled feature sequence v xgd where n = N/4. This sequence then processed by our HFSQ module, which operates in two stages:
Grouping: The feature vector is split into G parallel groups, = [ val. where each group vy E (d/G).
Cascaded Residual Quantization: Each group vy is quantized sequentially over R residual stages. Let the initial residual be eg.1 Ug- For each stage r E [1,..., ... R]. we compute:
Zg. FS, ög. Dequantizo(zg.r)
Cy.r+1 .g.r 8, Gg.r-
(1)
4

---

Published as a conference paper at ICLR 2026
The core output is the set of continuous reconstructions ggr from each hierarchical level. We define the complete hierarchical continuous representation as the set of these vectors, denoted by V:
(0g.r]g=1.r=1
Frequency-basee Semantic Decomposition. Crucially, this residual hierarchy naturally disentangles motion semantics based on signal energy. The base layer (r 1), minimizing the primary recon- struction error, captures the Coarse Motion (e.g., global posture, orientation, and cow-freuueccy trajectories). Subsequent layers (r 2 2), encoding the residual error, capture the Fine Motion (e.g., high-frequency local dynamies and subtle articulation details). As we will detail, this structured set serves as the target space for our diffusion model. Notably, we find that a lightweight configuration with just two residual quantization layers (R 2) is sufficient for our final architecture, with more details available in Appendix A.6.
Decoder and Training Objective. The decoder reconstructs the motion from the dequantized features. The final feature vector ú is obtained by summing the reconstructions within each group and concatenating them: 8, Σx= 8, ggrr and - ég)- The HFSQ autoencoder is trained to minimize combination of latent loss and physical plausibility losses on the final decoded motion MR (ec
CursQ = Akin Lainematic + Atat Mathlatents
where
(3)
Cxinematic
Σ
Apk
||
M
(k) ft
Ih.
(4)
A vel,ac)
Liatent
=
||0 sg()))l 1122 + 31| sg(w) 612.
(5)
The ((k) denotes the k-tth order temporal derivative (position, velocity, acceleration) of the ground- truth motion, and (k) denotes the reconstructed counterparts. This objective ensures that the final reconstruction is both physically realistic and faithful to the encoder's output.
Latent Robustness via Progressive Masking.
Dance Motion
Code Making
To construct structured HFSQ latent space, we cor- rupt the hierarchical representation (Eq. 2) using two complementary techniques. First, residual mask- ing promotes inter-layer independence by stochasti- cally occluding higher-level FSQ layers while per- turbing the base layer with scaled Gaussian noise. Second, code masking improves the decoder's bustness by randomly masking dimensions within individual FSQ codes. Together, these operations regularize the latent space, improving the decoder's tolerance imperfect inputs and thereby enhancing the final motion quality.
Encoder
Croup
Progressive
Masking
Residual
Residual Masking
Figure 3: Progressive masking overview,
3.3 LATENT DIFFUSION ON HIBRARCHICAL FEATURES
Generative Target. Unlike methods that diffuse on discrete codes or the final aggregated feature, our diffusion model operates on the hierarchical continuous representation V (Eq. 2). This strategy enables the model learn the coarse-to-fine structure of motion within the continuous space defined by our HFSQ module. The final motion is then robustly decoded from this generated representation.
Diffusion Framework. We model the generation process using DDPM (Ho al., 2020). The forward process gradually adds Gaussian noise to the HFSQ latents z = > over timesteps. The reverse process trains Transformer- conditioned on music e and leader motion M,.. To leverage the hierarchical structure of V, we apply independent weights to the loss for each residual scale. The latent diffusion loss is:
network Go denoise the corrupted latents æ, timestep
R
Lsimple Σ r=
N Emg. [[]V* - Gu(at,, c, M2)
(6)
5

---

Published as a conference paper at ICLR 2026
M,
Lepr-Decoppal
Divens LDCFG Sumples
Clasifier-Free Guilazse Sampling (See 1.59
M
Network
Baa B
Blodicuise
(Sec.
Transformer Decoder
Trnntimmer Decoder
182
1+9
1+4
Decoder
Fog Asa Foor
Maric Autlic
Trandommar Denader
Linoa
Selfi Attertion
FILM
Cross Aittnnnttttionn
Denoise Network
Timestpp
MLP
MLP
FILM
Platened HPSQQ Reproscemation
Figure 4: ReactDance Pipeline Overview. Our ReactDance generates long, high-fidelity reactive dance sequences conditioned on leader motion and music. The core is diffusion model that learns to denoise hierarchical HFSQ latents. Leader motion is injected via cross-attention, while music features are fused using FiLM layer. For coherent generation of long sequences, Blockwise Local Context (BLC) sampling strategy partitions the timeline into parallel blocks with aligned temporal contexts. Within each denoising step, Layer-Decouple Classifier-Free Guidance (LDCFG) provides fine-graing aine-granedd control by applying independent guidance weights to each HFSQ scale. Finally, the denoised latents are decoded into a dance sequence coherently aligned with the input conditions.
where V" = (ūgrr ) represents the set of feature vectors at the r-th level, and Ge(-)" is the corresponding portion of the network's output. The weights X balance the contribution of each residual level, allowing the model to focus on coarse-to- fine details during generation. Moreover, we apply kinematic loss Lkinematic (Eq. 4), foot contact loss Lfe, leader-reactor relative orientation loss Lro (Liang al., 2024) enforce physical plausibility and interaction coherence. The complete loss defined in Eq. 7, with Akin; Afe and Am as balancing weights:
Laifusion
Lsimple
Xkin Kxamemaatic Asehie AroLou-
3.4 EFFICIENT LONG SEQUENCE GENERATION VIA BLOCKWISE LOCAL CONTEXT
Generating long, coherent dance sequences is critical challenge, as the desired inference length K often far exceeds the model's training window T. While autoregressive methods can extend sequence length, they suffer from serial inference latency and error accumulation. To overcome these limitations, we introduce Blockwise Local Context (BLC), a non-autoregreesive sampling strategy that generates partitioned long sequences in parallel. BLC achieves high-fidelity generation through two synergistic principles: Intra-block Consistency (via sampling protocols) and Inter-block Continuity (via training dynamics).
Intra-block Consistency via Parallel Sampling. To enable parallel generation, BLC aligns the inference context of an arbitrary long sequence with the fixed-length context seen during training. We achieve this through:
Periodic Causal Masking: block-diagonal attention mask M is applied across the sequence to enforce causality strictly within non-overlapping blocks of size T, preventing information leakage and halting error propagation between blocks.
Phase-aligned Positional Encoding: periodic positional encoding P; is introduced reset the temporal phase for each block:
(i mod T) P: = sin (T(
(( mod T)
@ cos
(8)
T
T
where i is the global temporal position. This ensures that every inference block acts as an independent, well-formed window identical to the training distribution.
Inter-block Continuity via Dense Sliding Window (DSW). A core challenge in parallel block sampling is ensuring seamless transitions at the boundaries between independently generated blocks.

---

Published as a conference paper at ICLR 2026
We resolve this not by explicit post-processing, but by enforcing Latent Manifold Constraint during training via our Dense Sliding Window (DSW) strategy. We train the model with a stride m significantly smaller than the window size (m < T, e.g., m 4, 240). This design is crucial for two reasons:
Phase-Agnostic Transition Prior:
By processing motion windows at every possible phase shift, the model observes any given motion frame Je in diverse temporal roles (as a start, middle, or end frame). Consequently, the decoder learns phase-agnostic transition function, understanding that adjacent latents 2n and 2141 must resolve to continuous motion regardless of where the window boundary cuts.
During inference, even if the parallel attention blocks create slight boundary discontinuities in the latent space, the HFSQ decoder-having been trained on thousands of overlapping, phase-shifted windows-project these boundary latents onto the valid continuous motion manifold. This implicitly stitches blocks and with smooth, physically plausible transitions.
Decoder as Kinematic Smoother:
In summary, BLC's masking enables efficiency through parallelism, while DSW's dense supervision ensures coherence by learning robust boundary invariance.
3.5
CLASSIFIER- GUIDANCE
Precision Control vs. Spatial Control. While our Multi-stream Input design (Section 3.1) explicitly handles Spatial Control by defining specific body regions for interaction, ReactDance requires a complementary mechanism for Precision Coltro!-allowing users to modulate how strictly the generation adheres to conditioning signals across different semantic scales, Standard Classifier-Free Guidance (CFG) (Ho & Salimans, 2021) applies a single scalar weight across the entire motion representation (Tevet et al., 2023; Shafir et al., 2024b), forcing coupled trade-off between global pose stability and fine-grat fin-grained interaction details. This suboptimal for hierarchical tasks where structural coherence and subtle dynamics may require different guidance strengths.
To address this, we propose leverages the hierarchical nature of HFSQ for orthogonal control over motion structure and details.
Classifier Guidance (LDCFG). This method
Training Strategy:
Independent Condition Dropout. To support decoupled guidance during inference, the model must learn to condition each hierarchical layer independently. Unlike standard CFG training which drops conditions for all layers simultaneously, we employ Independent Condition Dropout. During training, the conditioning signal (c and Mz) for the r-th residual layer is replaced with a null embedding 9 with an independent probability p = 0.2. This forces the denoiser to learn the conditional distribution P(= |c, My) marginally, laying the foundation for layer-isolated control during inference.
Inference Mechanism. During sampling, LDCFG assigns an independent guidance strength s, to each scale of the HFSQ representation. The predicted noise is computed as:
= (1 s, s,)) c, M2) Go f, 0).
where s, controls the guidance scale for the r-th layer. This formulation provides an explicit mechanism for frequency-basee control by modulating the coarse and fine components defined in Section 3.2. The Base Guidance (s1): Modulates the Coarse Motion. Increasing $1 anchors the global posture and orientation, ensuring kinematic stability. While the Residual Guidance (8/22): Modulates the Fine Motion. Increasing $2 specifically enhances the subtle local dynamics and interaction nuances without altering the macro-level motion structure.
This disentanglement allows ReactDance to act as "Detail Specialist" or "Structure Specialist" as needed, achieving the optimal balance between fidelity and diversity.
EXPERIMENT
4.1 DATASETS AND EXPERIMENTAL SETTING
Dataset. We conduct experiments on the DD100 dataset (Siyao et al., 2024), which comprises 1.95 hours of paired dance motion and music data spanning 10 musical genres. We adopt the original
7

---

Published as a conference paper at ICLR 2026
Table 1: Comparison with state-of-the-ar methods on the test dataset of DD100 dataset. Symbols 1. J. and >> indicate the higher, lower and closer to Ground Truth are better. Bold and underline indicate the best and second best results. The dotted line separates methods for single-person motion generation (above) from those for duet and multi-person generation (below).
Solo Metrics
Interactive Metrics
Method
(ID,()) FID,()) Divg(-))
Diy(
MPIPE() MPIVELU PFCL) BAS( (ID
Div,at ->) BED( IPR(U) AITS()
Ground Truth
10.86
7.82
0.1791
12.53
0.530%
GS
14.65
12.20
9.00
171.37
0.7903
0.1734
9.45
0.1903
19.01
EDGE
68.68
113:25
5.62
10.05
235.52
20.76
0.6226
0.1851
1523.27
12.37
0.1901
8.11
2.91
TCDiT
105.97
251.55
14:23
24.54
182.64
22.91
1,1165
0.2270
1472.00
11.31
0.23304
7.58
3.34
InterGen
35.89
47.28
12:24
9.13
210.66
18.69
0.9842
0.1631
176.19
18.10
0.2746
17.58
5.22
Duolando
27.68
35.01
10.95
8.70
174.54
18.72
0.9276
0.2086
17:49
14.73
0.3265
17.42
4.41
RenetDunce
5.57
7.63
10.82
7.76
132.99
15.68
0.6039
0.2031
14.17
10.58
0.3863
7.84
1.75
dataset's 's training/test splits. All motion sequences are split into 8-second clips (30 frames per second) with a sliding window stride of 4.
Evaluation Metrics. We adopt a comprehensive set of quantitative metrics to evaluate the generated motion from four key perspectives: (1) Motion Quality. To assess the realism of individual motions, we measure distributional similarity and reconstruction accuracy. Distributional metrics include Fréchet Inception Distance (FID) and Diversity (Div) on both kinematic (FIDx, Div (Onuma et al., 2008) and graphical (FID,, g: Div,) (Müller et al., 2005) features. Reconstruction accuracy against the ground truth is measured by Mean Per-Joint Position Error (MPJPE) for static poses and Mean Per-Joint Velocity Error (MPJVE) for dynamics (Ghosh et al., 2024). Additionally, the Physical Foot Contact (PFC) (Tseng et al., 2023) detects foot skating artifacts by verifying kinematic consistency. (2) Interaction Quality. To evaluate leader-reactor coordination, we compute FID and Diversity on cross-distance features (FID Div.a), which describe the evolving spatial relationships between the dancers' key joints. To assess physical validity, we calculate the Inter-Penetration Rate (IPR), quantifying vertex-mesh penetrations using the Generalized Winding Number field (Jacobson et al., 2013), accelerated by AABB-based culling. (3) Music-Dance Aligmment. We use two metrics: the Beat Align Score (BAS) (Siyao et al., 2022) to measure synchronization between motion peaks and music beats, and the Beat Echo Degree (BED) (Siyao et al., 2024) to quantify rhythmic consistency between the dancers. (4) Generation Efficiency. We report the Average Inference Time per Sequence (AITS) in seconds.
4.2 COMPARISON WITH BASELINE METHODS
Baselines. We compare our model against several sfate-of-the-ard (SOTA) methods. For a fair comparison, all baselines were adapted for the reactive dance generation (RDG) task and retrained from scratch on the DD100 dataset using the SMPL body model. We integrated the leader's motion as direct condition for several models: the GestureLSM (Liu al., 2025) was modified by replacing its text encoder with a leader motion encoder, while for the music-driven EDGE (Tseng et al., 2023), added the features from leader motion encoder its existing music features. Other baselines required more targeted modifications. The text-to-interaction model InterGen (Liang et al., 2024) was adapted by replacing its text encoder with music encoder and changing its weight-shared network to be independent for each dancer. For the group dance model TCDiff (Dai et al., 2025), we altered its navigator module to predict the reactor's trajectory from the leader's motion. Finally, Duolando (Siyao et al., 2024), an existing RDG model, was retrained on our dataset to ensure body model consistency (SMPL vs. its native SMPL-X).
Analysis. As reported in Table 1, our method, ReactDance, demonstrates superior performance across the majority of metrics. Note that these results are computed on the entire test set, which features long sequences with an average length of 2066 frames. This highlights our model's strong generalization capability and consistency in long-term generation. The significant lead in realism (FID,, FID,,) and accuracy (MPJPE, MPJVE) stems from our effective HFSQ representation, which enables the diffusion model to learn motion hierarchically from coarse postural dynamics to fine- grained articulations. This ime-grained capability also ensures high physical plausibility, evidenced by the lowest PFC score (0.6039), indicating minimized foot skating artifacts compared to baselines. Regarding duet coherence, ReactDance significantly outperforms all baselines, achieving a FID
8

---

Published as a conference paper at ICLR 2026
Head Overrotation
Hand Misalignment
Duolando
f=0
f=80
f=160
f=240
f=320
f=400
f=480
Uncoordinated Interactions
GestureL SMM
f=0
f=80
f=
f=240
f=320
f=000
f=480
Jitter Collapse
InterGen
f=0 f=80 f=160
f=240
f=320
f=400
f=480
ReactDance
f=0
f=80 f=160
f=240
f==220
ff4400
f=480
Figure 5: Qualitative comparison of reactive dance generation. Given the same leader motion, Duolando produces unnatural head rotations and GestureLSM shows uncoordinated interactions. InterGen's 's motion collapses into unrealistic jitter when generalizing beyond its training horizon. In contrast, model generates fluid and coherent reactive motion.
of 14.17 and BED of 0.3863. Crucially, our method maintains low IPR (7.84%), significantly surpassing the autoregressive baseline Duolando (17.42%) and proving that our model generates spatially coherent interactions without severe collision artifacts. Furthermore, ReactDance also achieves the most efficient generation with an AITS of 1.75s due to the parallel generation capability of our BLC sampling strategy. In contrast, competing methods exhibit key limitations. Architectures with a two-stage design like GestureLSM and Duolando, while competitive in solo motion quality, are constrained by their representations. GestureLSM's use of single-scale latent space for generation limits its ability to capture hierarchical interactions (evidenced by a high FID of 40.09), while Duolando's s VQ-VAE is less effective at fine-gra ine-grained reconstruction. Other models like EDGE and TCDiff perform poorly overall, as they learn directly on the high-frequency raw motion space and lack explicit mechanisms for interaction modeling.
User Study. We conducted a user study to evaluate the perceptual quality of long-form (> 60 seconds) reactive dance sequences, involving 20 participants. Fach participant completed 15 paired comparisons, rating videos generated by our method against baseline methods on 5-point Likert scale across three criteria: Motion Naturalness, Music Alignment, and Interaction Coordination. As detailed in the appendix (Fig. 9), our method consistently outperformed all baselines across each criterion, demonstrating strong and consistent participant preference. These results highlight our method's capability to produce high-quality, coherent interactions over extended durations.
4.3 ABLATION STUDY
Effeets of HESQ and Progressive Masking.
We conduct comprehensive ablation study the tokenizer architecture and Progressive Masking (PM) training strategy in Table 2. Comparing different architectures, the VAE suffer from posterior collapse, leading over-smoothed reconstruction (MPJPR 40.87) and poor generation alignment

---

Published as a conference paper at ICLR 2026
(MPJPE 230.21). Standard RVQ-VAE with
Table 2: Ablation study of HFSQ tokenizer and the Progressive Masking (PM).
same number of residual layers (R = 2) im- proves upon VAE but still lags behind HFSQ.
particularly in generation quality (FID, 26.98 vs. 7.63), even when trained with PM strategy. We attribute this to the irregular topology of vector codebooks, where the latent of adjacent indices lack semantic continuity, creating dif- ficult optimization landscape for diffusion. In contrast, HFSQ projects motion onto fixed scalar grid that preserves ordinal relations, pro- viding smoother, "diffusion-friendlyy mani- fold. Finally, analyzing the PM strategy reveals critical trade-off: removing the PM strategy trades a marginal MPJPE improvement for a substantial drop in realism (FIDg g increases to 10.46), demonstrating that PM is crucial for synthesizing high-fidelity motion. Additional results are available in the Appendix A.3.
Solo Metries
Method
(l)
(-),(>>) MPJPE( BAS(
Ground Truth
7.82
0.1791
Stage I: Reconstruction
VAF
5.32
7.78
40.87
0,1805
RVQ-VAR (wlo. PM)
4.57
7.72
36.59
0.1841
RVQ-VAE (w PM)
1.09
7.73
32.98
0.18-12
HFSQ (wlo. PM)
3.77
7.74
24.15
0.1784
HESQ (w PM, Ours)
3.56
7.73
28.66
0.1816
Suage 2: Generation
VAE
18.99
7.77
230 21
0,2705
RVQ-VAE (wla. PM)
36.73
8.82
139.42
0.2040
RVQ VAE (w PM)
26.98
7.75
138.28
0.1946
HFSQ (w/o, PM)
10.46
7.65
132.19
0,2003
Ours (HFSQ w PM)
7.63
7.76
132.99
0.2031
Effects of Blockwise Local Context Sampling. Table 3: Ablation of Dense Sliding Window (DSW)
training and BLCC sampling strategies.
We ablate the two core components of our Blockwise Local Context (BLC) sampling to validate its contributions duet coherence and sampling efficiency, with results presented in Table We analyze the dense sliding window component by increasing the training stride from (our full model) to and 64. We ob- serve significant degradation in coherence, as evidenced by the substantial increase in FID and the decline in BED, Moreover, we replace our periodic positional encoding (PPE) and periodic causal attention masking (PCAM) of BLC with the latent stitching operation (Tseng et al., 2023). This change harms both sampling efficiency (AITS increases from 1.75 to 2.03) and generation quality (FID,z increases from 14.17 to 18.59). These results confirm dense training stride is crucial for coherence, and our periodic sampling mechanism is key to achieving both high quality and efficiency. For a comprehensive analysis of the trade-off between motion continuity and training cost, please refer to Appendix A.7.
Duet Metries
Method
FID,((L)
Div.a(->)
BED(-) AITS())
Grann Truth
12.53
0.5308
x=64 s=16
39.50
12.08
0,2840
22.69
10.06
0.3065
w/o. PPE, PCAM
18.59
11.17
0.3218
2.03
ReaetDance (5=4)
14.17
10.58
0.3863
1.75
Effect of Layer-Decoupler Guidance. Our diffusion model operates on hierarchical HFSQ latents, enabling us to apply independent classifier-free guidance weights s, of Eq. (9) to each motion scale. This provides fine-graie fine-grained control over the fidelity-diversin trade-off across different semantic levels. For instance, user can enforce strong postural alignment with high guidance on coarse scales while encouraging creative local articulation with lower guidance on fine scales. A detailed analysis demonstrating this flexibility available in Appendix A.4.
CONCLUSION
We present ReactDance, a diffusion framework for generating high-fidelity and coherent long-form reactive dance. Our method is built upon hierarchical latent representation that captures multi-scale spatiotemporal dynamics. This representation enables two key contributions: an efficient parallel sampling strategy for long sequence generation and multi-level conditioning mechanism that offers fine-grained artistic control. By solving the fundamental challenge of maintaining kinematic stability over extended durations, ReactDance serves a robust cornerstone for coherent long-forn reactive dance generation. We hope this foundation paves the way for future research to ascend from physical continuity to high-level semantic modeling, enabling the exploration of narrative-driven choreography and emotional storytelling in reactive dance.
Limitation. Despite the efffectiveness of our HFSQ representation, lacks explicit semantic meaning across scales, which reduces control interpretability. Additionally, finger motions are omitted mainly due to the noisy hand data of DD100 dataset, which are crucial for nuanced gestures.
10

---

Published as a conference paper at ICLR 2026
Acknowledgements. We sincerely acknowledge the anonymous reviewers for their insightful feedback. This work is partially supported by the Natural Science Foundation of China (Grant No. 62472178), the Open Projects Program of State Key Laboratory of Multimodal Artificial Intelligence Systems (No. MAIS2024111) the Fundamental Research Funds for the Central Universities, and the Science and Technology Commission of Shanghai Municipality (Grant No. 25511 107200).
REFERENCES
Brian McFee, Colin Raffel, Dawen Liang, Daniel W.W. Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. Librosa: Audio and Music Signal Analysis in Python. In Proceedings of the 14th Python in Science Conference, pp. 18-24, 2015,
Haoran Chen, Yiteng Xu, Yiming Ren, Yaoqin Ye, Xinran Li, Ning Ding, Peishan Cong. Ziyi Wang, Bushi Liu, Yuhan Chen, et al. Symbiosim: Human-in-thee -hoph simulation platform for bidirectional continuing learning in human-robot interaction. arXiv preprint arXiv:2502.07356 2025.
Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Hieu Pham, Xuanyi Dong. Thang Luong, Cho-Jui Hsieh, Yifeng Lu, et al. Symbolic discovery of optimization algorithms. Advances in neural information processing systems, 01:499055492333 2023.
Yuqin Dai, Wanlu Zhu, Ronghui Li, Zeping Ren, Xiangzheng Zhou, Jixuan Ying, Jun Li, and Jian Yang. Harmonious crusic-driven group choreography with controlllc diffusion. Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2645-2653,, Apr. 2025.
Anindita Ghosh, Rishabh Dabral, Vladislav Golyanik, Christian Theobalt, and Philipp Slusallek. Remos: 3d motion-conditioned reaction synthesis for two-person interactions. In European Conference on Computer Vision, pp. 418-437. Springer, 2024.
Anindita Ghosh, Bing Zhou, Rishabh Dabral, Jian Wang. Vladislav Golyanik, Christian Theobalt, Philipp Slusallek, and Chuan Guo. Duetgen: Music driven two-person dance generation via hierarchical masked modeling. Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers, pp. 1-11, 2025.
Kehong Gong, Dongze Lian, Heng Chang, Chuan Guo, Zihang Jiang, Xinxin Zuo, Michael Bi Mi, and Xinchao Wang. TM2D: Bimodality driven 3d dance generation via music-text integration. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9908-9918. IEEE, 2023.
Diego Felipe Paez Granados, Breno A Yamamoto, Hiroko Kamide, Jun Kinugawa, and Kazuhiro Kosuge. Dance teaching by a robot: Combining cognitive and physical human-robot interaction for supporting the skill learning process. IEEE Robotics and Automation Letters, 143-:145-1 2017.
Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng. Momask: Generative masked modeling of 3d human motions. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 1900-1910, 2024.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurlPS 202/ Workshop on Deep Generative Models and Downstream Applications, 2021.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proceedings of the 34th International Conference on Neural Information Prucessing Systems, pp. 12. Curran Associates Inc., 2020.
Li Hu. Animate anyone: Consistent and controllable image-to-video synthesis for character animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8153-8163, June 2024.
Alec Jacobson, Ladislav Kavan, and Olga Sorkine- Robust inside-outside segmentation using generalized winding numbers. ACM Trans. Graph., 32(4):33:1-33:11 2013.
Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen. Motiongpt: Human motion as a foreign language. Advances in Neural Information Processing Systems, 36, 2024.
11

---

Published as a conference paper at ICLR 2026
Maximilian Krug. Temporal procedures of mutual alignment and synchronization in collaborative meaning-makee activities in a dance rehearsal. Frontiers in Communication, 7:957894, 2022.
Buyu Li, Yongchi Zhao, Shi Zhelun, and Lu Sheng. Danceformer: Music conditioned 3d dance generation with parametric motion transformer. In Proceedings the AAAI Conference on Artificial Intelligence, pp. 1272-1279. AAAI, 2022.
Ronghui Li, Junfan Zhao, Yachao Zhang, Mingyang Su, Zeping Ren, Han Zhang, Yansong Tang. and Xiu Li. Finedance: fine-grained choreography dataset for 3d full body dance generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10234-10243, 2023.
Ronghui Li, Yuxiang Zhang, Yachao Zhang, Hongwen Zhang, Jie Guo, Yan Zhang, Yebin Liu, and Xiu Li. Lodge: A coarse to fine diffusion network for long dance generation guided by the characteristic dance primitives. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1524-1534. IEEE, 2024.
Ruilong Li, Shan Yang, David A. Ross, and Angjoo Kanazawa. Ai chorcographer: Music conditioned 3d dance generation with aist++. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13381-13392. IEEE, 2021.
Han Liang, Wenqian Zhang, Wenxuan Li, Jingyi Yu, and Lan Xu. Intergen: Diffusion- based multi- human motion generation under complex interactions. International Journal of Computer Vision, pp. 1-21, 2024.
Pinxin Liu, Luchuan Song, Junhua Huang, and Chenliang Xu. GestureLSSM: Latent Shorteut based Co-Speech Gesture Generation with Spatial-Tempone Modeling. In Proceedings of the IEEF/CVF International Conference on Computer Vision (ICCV), 2025.
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael Black. Smpl: A skinned multi-person linear model. In Seminal Graphics Papers: Pushing the Boundaries, Volume 2, pp. 851-866. 2023.
Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen. Finite scalar quan- tization: VQ-VAE made simple. In Proceedings of the International Conference on Learning Representations. OpenReview.nes 2024.
Meinard Müller, Tido Röder, and Michael Clausen. Efficient content-based retrieval of motion capture data. ACM Trans. Graph., 24(3):677-685, July 2005.
Kensuke Onuma, Christos Faloutsos, and Jessica K. Hodgins. Fmdistance: A fast and effective distance function for motion capture data. In Katerina Mania and Eric R. Reinhard (eds.), Euro- graphics, pp. 83-88 86. Eurographics Association, 2008
Aaron Van Den Oord, Oriol Vinyals, al. Neural discrete representation learning. Adv. Neural Inform. Process. Syst. 30, 2017.
Qiaosong Qi, Le Zhuo, Aixi Zhang, Yue Liao, Fei Fang, Si Liu, and Shuicheng Yan. Diffdaacee: Cascaded human motion diffusion model for dance generation. In Proceedings of the 31st ACM International Conference on Multimedia, pp. 1374-1382. ACM, 2023.
Xingqun Qi, Yatian Wang. Hengyuan Zhang, Jiahao Pan, Wei Xue, Shanghang Zhang, Wenhan Luo, Qifeng Liu, and Yike Guo. Co3gesture: Towards coherent concurrent co-speech 3d gesture generation with interactive diffusion. In ICLR, 2025.
Jenny Seham. Public scholarship in dance: Teaching, choreography, research, service, and assessment for community engagement. Journal of Dance Education, 16(4):159-159, 2016.
Yoni Shafir, Guy Tevet, Roy Kapon, and Amit Haim Bermano. Human motion diffusion as a generative prior. In The Twelfth International Conference on Learning Representations, 2024a.
Yoni Shafir, Guy Tevet, Roy Kapon, and Amit Haim Bermano. Human motion diffusion as genera- tive prior. In The Twelfth International Conference on Learning Representations. OpenReview.net, 2024b.
12

---

Published as a conference paper at ICLR 2026
Li Siyao, Weijiang Yu, Tianpei Gu, Chunze Lin, Quan Wang, Chen Qian, Chen Change Loy, and Ziwei Liu. Bailando: 3D dance generation by actor-critic GPT with choreographic memory. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 11040-1 1049. IEEE, 2022.
Li Siyao, Tianpei Gu, Zhitao Yang, Zhengyu Lin, Ziwei Liu, Henghui Ding, Lei Yang, and Chen Change Loy. Duolando: Follower GPT with off off-po reinforcement learning for dance accompaniment. In Proceedings of the International Conference on Learning Representations. OpenReview.net, 2024.
Taoran Tang, Jia Jia, and Hanyang Mao. Dance with melody: An lstm-autoencoder approach to music-oriented dance synthesis. In Proceedings of the 26th ACM international conference Multimedia, pp. 1598-1606. ACM, 2018
Guy Tevet, Sigal Raab, Brian Gordon, Yoni Shafir, Daniel Cohen-or, and Amit Haim Bermano. Human motion diffusion model. In The Eleventh International Conference on Learning Represen- tations. OpenReview.net, 2023.
Konstantin Tsakalidis. Choreography: craft and vision: developing and structuring dance for Solo, Duet and groups. Stage Verlag, 2020.
Jonathan Tseng, Rodrigo Castellon, and C. Karen Liu. EDGE: Editable dance generation from music. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 448-4448- 458. IEEE, 2023.
Zhenzhi Wang, Jingbo Wang, Yixuan Li, Dahua Lin, and Bo Dai. Intercontrol: Zero-shot human interaction generation by controlling every joint. NeurIPS, 2024.
I Lixing Xiao, Shunlin Lu, Huaijin Pi, Ke Fan, Liang Pan, Yueer Zhou, Ziyong Feng, Xiaowei Zhou, Sida Peng, and Jingbo Wang. Motionstreamer: Streaming motion generation via diffusion-based autoregressive model in causal latent space. 2025.
Liang Xu, Yizhou Zhou, Yichao Yan, Xin Jin, Wenhan Zhu, Fengyun Rao, Xiaokang Yang, and Wenjun Zeng. Regennet: Towards human action-reaction synthesis. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 1759-1769. IEFE, 2024.
Dongchao Yang, Songxiang Liu, Rongjie Huang, Jinchuan Tian, Chao Weng, and Yuexian Zou. Hifi-codec: Group-residuse vector quantization for high fidelity audio codec. arXiv preprint arXiv:2305.02765. 2023.
Siyue Yao, Mingjie Sun, Bingliang Li, Fengyu Yang. Junle Wang, and Ruimao Zhang. Dance with you: The diversity controllable dancer generation via diffusion models. Proceedings of the ACM International Conference Multimedia, pp. 8504 8514. ACM, 2023.
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3836-38 3847, 2023.
13

---

Published as a conference paper at ICLR 2026
A APPENDIX
In the appendix, we present:
Section A.1: The Use of Large Language Models (LLMs).
Section A.2: Training Hyper-parameters.
Section A.3: More Results of Ablation on HFSQ Representation and Progressive Masking.
Section A.4: More Results of Layer-Decoupled Guidance.
Section A.5: User Study Results.
Section A.6: More Results on Residual Stages R and Scalability Analysis.
Section A.7: Additional Analysis DSW Stride Selection.
Section A.8: Hand Motion Modeling Analysis.
Section A.9: Ablation of Conditioning Signals.
Section A. 10: Generalization via Role Swapping.
Section A.11: Zero-Shot Generalization on Out-of-Distribuus Data.
A.1 THE USE OF LARGE LANGUAGE MODELS
In the preparation of this manuscript and its accompanying code, we utilized Large Language Models (LLMs) as assistive tools. We wish to clarify that the core scientific contributions-incuuunnn the problem formulation, the design of our proposed method, and the experimental analysis are entirely the work of the authors. The use of LLMs was confined the following supporting tasks:
Manuscript Preparation: LLMs were employed for language refinement. This included improving grammatical correctness, rephrasing sentences for better clarity and flow, and ensuring stylistic consistency throughout the paper. All final text was carefully reviewed and edited by the authors to ensure accurately reflects our research and contributions.
Software Development: LLMs assisted in writing auxiliary and boilerplate code. Specific use cases included generating scripts for data preprocessing and visualization, creating utility functions, and debugging isolated code segments. The core algorithmic implementation of our proposed method was developed exclusively by the authors.
In all instances, the LLM served productivity and quality-enhancemi tool. The authors maintain full intellectual responsibility and ownership for all conceptual and experimental aspects of this work.
A.2 TRAINING HYPER-PARAMETERR
Our framework is implemented in PyTorch and trained single NVIDIA RTX L40 GPU. The motion HFSQ is trained for 200 epochs using the Lion optimizer (Chen et al., 2023) with initial learning rate 3e and stepwise learning rate scheduler decays the rate by 0.998 per epoch. For diffusion training, the denoise network trained for 1500 epochs using the Adamw optimizer with initial learning rate -1 and stepwise learning rate scheduler decays the rate by 0.998 per epoch. Batch size is set to 256 across all stages.
A.3 MORE RESULTS OF ABLATION ON HESQ REPRESENTATION AND PROGRESSIVE MASKING
The visualization in Fig. 6 supports the quantitative findings in the main paper. The ablated model using an RVQ-VAF often generates poses that are individually plausible but contextually incorrect within the duet. This leads to clear visual artifacts such as inaccurate relative positioning and distance between the dancers, well failures in fine meninndde hand-to-hand or hand-to-bode contact. This demonstrates that HFSQ's 's hierarchical structure is crucial for effectively representing the complex spatial dynamies of dance interactions.
14

---

Published as a conference paper at ICLR 2026
wio HPSO
React Dancc (Ours Full)
Figure 6: Qualitative comparison for the HFSQ ablation. While our full model maintains correct spatial relationships, the model trained without HFSQ (using RVQ (Q-VAE) fails to capture precise inter-personal dynamics, resulting in incorrect relative distancing and mismatched hand interactions (highlighted in red circles).
As shown Fig. 7, removing the PM strategy introduces severe interaction artifacts. This aligns with the quantitative trade-off noted in the main paper, where the ablated model achieves slightly lower MPJPE but a much worse FID,, Without the regularization provided by PM, the model may reconstruct individual poses more closely but fails learn robust interaction context, This leads to fundamental errors in character orientation relative to the leader and results in physically implausible outcomes, such body part interpenetration and collisions. This highlights that the PM strategy is essential for learning the physical constraints and relational logic of reactive dance.
w/o
React (Ours Full)
Figure 7: Qualitative comparison for the Progressive Masking (PM) ablation. Our full model generates a coherent and physically plausible interaction. In contrast, removing the PM strategy leads critical emrors in interaction logic, such the reactor adopting an incorrect orientation relative the leader, resulting in interpenetration artifacts (highlighted).
A.4 MORE DETAILS ABOUT LAYER-DECOUPLES GUIDANCE
Our Layer-Decouple Classifier-Free Guidance (LDCFG) is designed to overcome the limitation of a single, global guidance weight used in conventional CFG. By assigning independent guidance strengths (Eq. 9) to each hierarchical layer of our HFSQ representation, LDCFG provides fine- grained control over the fidelity-diversin trade-off at different semantic levels.
We conducted quantitative analysis by sampling with different LDCFG weight pairs S [Sevarse $tine,, where Secarse and Sine control the coarse- and fine- ame-ggrined motion scales, respectively. The results presented in Table 4. The data reveals clear trade-off between solo motion fidelity
15

---

Published as a conference paper at ICLR 2026
Table Solo and Duet metrics with diverse Layer-Decouples Classifier-Free Guidance (LDCFG) weights. Best and second-best results are in bold and underline, respectively.
Solo Metries
Interactive Metries
LDCFG Weight (I),(I) (ID,(I) (iv,(>> Div,(-)) MPIPE()) MPIVE(J) BAS(-) (() Div.a(+>) BED(->
Ground Truth
10.86
7.82
0.1791
12.53
0.5308
1.0, 1.0
8.09
10.37
10.74
7.69
153.30
17.39
0.2198
18.15
9.82
0.3470
110, 1.2)
6.17
10.79
7.74
138.68
16.29
13.66
1.0. 1.5
5.77
7.70
10.80
7.75
134.78
15.90
0.2091
13,40
10.65
0.3835
1.2,
5.57
7.63
10.82
7.76
132.99
15.68
0.2031
14.17
10.58
0.3863
1.5
5.62
10.83
7.79
132.20
15,43
18.57
10.14
0.3901
1.5]
6.07
7.62
10.85
7.81
135.27
15.62
0.1961
24.56
9.68
0,3925
and interaction quality. For instance, increasing the guidance weights (e.g., S 1.5) improves reconstruction metrics like MPJPE and MPJVE, but at the cost of harming the interaction quality (FIDed increases significantly to 18.57). Conversely, high guidance on both scales (S = [1.5, 1.5)) improves diversity (Divk. Div,)) but leads to the worst interaction scores (FID of 24.56). Our chosen setting, = [1.2, 1.2] (highlighted), provides an optimal balance, achieving the best FID, and strong reconstruction quality while maintaining competitive interactive metrics. This demonstrates that LDCFG enables a nuanced tuning of realism against interaction coherence.
apply LDCFG for targeted artistic control. Fig. 8 visualizes this capability, showcasing independent guidance applied to the upper body, lower body, and their combination create distinct stylistic motion variations while maintaining plausible interactions.
Furthermore, because representation disentangles body components,
(a) Lower Body Condition
(b) Upper Body Condition
(c) Upper Body, Lower Body Condition
(d) Full Motion Condition
Figure 8: Qualitative visualization of LDCFG's body-part control. We apply decoupled guidance with varying strengths to different body regions. The blue robot the leader. The other colored robots are generated by model, each with different LDCFG setting applied to the (a) lower body, (b) upper body, (c) their combination, or (d) full body global motion demonstrating fine-grained artistic control.
16

---

Published as a conference paper at ICLR 2026
A.5
USER STUDY RESULTS
GestureL5M
EDGE
TCDiff
4.32
InterSen
4.24
I
Ours
4.9
5.42
3.24
2.83
2.62
2,52
2.23
2.0
1.91
I
Motion Naturalness
Music Alignment
Interaction Coordination
Figure 9: User study results. ReactDance consistently outperforms all baselines across all criterions.
A.6 MORE RESULTS ON RESIDUAL STAGES AND SCALABILITY ANALYSIS
In this section, we provide detailed scalability analysis regarding the depth of our Hierarchical Finite Scalar Quantization (HFSQ), specifically the number of residual stages R. The choice of R involves critical trade-off between the representational capacity the tokenizer and the modeling complexity for the subsequent diffusion generator.
We evaluate the performance across R E (1,2,3, in Table 5. The results reveal three key observations:
Reconstruction Fidelity: Increasing from 1 to yields substantial reduction in reconstruction error (MPJPE decreases from 35.53 to 24.32), indicating that the second stage is essential for capturing fine-grained articulation. However, further increasing to or provides diminishing returns (MPJPE only improves marginally to 22.97) while linearly increasing the latent space size.
Generation Quality: Surprisingly, higher stages (R 2 lead to a degradation in generation quality (FID, increases from 5.57 to 6.10). We attribute this to the increased complexity of the latent distribution; as the sequence of residual codes becomes longer, the joint distribution becomes harder for the diffusion model to learn effectively given the training budget.
Computational Cost: The training time increases only marginally with R, rather than scaling proportionally. Specifically, R = 2 incurs negligible overhead compared to 1 (1.00x vs. 0.94x) while offering significantly better motion quality.
Based on this analysis, R represents the optimal performance--fffcienny trade-off, achieving the best balance between reconstruction fidelity, generative realism, and computational efficiency. Consequently, adopt
our default setting.
Table 5: Scalability Analysis of Residual Stages R. While higher R marginally improves reconstruc- tion, R achieves the best generation quality (FID) and the optimal trade-off between performance and cost.
Reconstruction Metries (HFSQ)
Generation Metries (Diffusion)
Training Cost Train Time
Setting
FID,4
MPJPEJ
FID
FID&l
FID,4
FID
R
2.69
35.53
13.67
13.10
10.15
24,50
0.94x
R 2 (Ours)
0.40
24.32
5.20
5.57
7.63
14.17
1.00x
R 3
0.38
24.08
2.35
5.92
7.85
14,50
1.07x
4
0.30
22.97
2.10
6.10
8.02
14.85
1.12x
17

---

Published as a conference paper at ICLR 2026
A.7 ADDITIONAL ANALYSIS ON DSW STRIDE SELECTION
In Section 4.3, we validated the impact of training stride s on interaction coherence. Here, we extend this ablation to include Motion Continuity (measured by Boundary Jitter) and Training Cost (relative training time), providing a justification for our design choice.
We report the comparison across wide range of strides in Table 6. We define Jitter the mean L.2 velocity change at block boundaries (half-radius of 30 frames) to quantify the smoothness of the stitched motions.
Analysis of Motion Continuity, As the stride s decreases (density increases), the model receives more frequent supervision boundary transitions. Consequently, the Jitter score consistently improves (18.08 - 15.19) confirming that the Dense Sliding Window (DSW) mechanism effectively acts as a phase-agnostic kinematic smoother. Notably, sparse strides (s 64) fail to capture these transitions, resulting in severe discontinuities (>> 17.9) and collapsed interaction quality (FID > 39).
achieves the lowest Jitter (15.19) and best ol (14.02), it incurs substantial computational cost (4.00 training time compared to s 4). In contrast, our default setting 15.66, FID 14.17) while maintaining high training efficiency. The marginal gains of & do not justify the quadrupled training overhead. Thus, 4 represents the optimal cerformance-efficiing trade-off for scalable reactive dance generation.
Efficiency vs. Quality Trade-off. While the theoretical dense limit (s
achieves comparable performance (Jitter
Table 6: Full Ablation of Dense Sliding Window Stride (s). Jitter measures boundary discontinuity.
Motion Continuity
Interaction Quality
Training Cost
DSW Stride Setting
Jitter (
FID (L)
Train Time (L)
360 (Sparse)
18.08
146.22
0.01
240 (No Overlap)
17.18
121.65
0.02
64
17.99
39.50
0.06x
16
16.27
22.69
0.25 x
4 (Ours)
15.66
14.17
1.00x
(Extreme Dense)
15.19
14.02
4.00x
A.8 HAND MOTION MODELING ANALYSIS
The DD100 dataset contains inherent high- frequfrryyency noise and jitter in the ground truth hand motions (Noisy DD100 GT Hand Video), and conventional models often overfit to this noise during training, resulting in unstable generation. While baseline methods struggle to generate coherent reactive dances even without hand motions (see Duolando Failure Video), the inclusion of fine-grained hand modeling further exacerbates their performance degradation.
In contrast, our investigation reveals that React|Dance acts as a robust motion denoiser. This capability stems from the quantization mechanism of our Hierarchical Finite Scalar Quantization (HFSQ). By projecting continuous motion into discrete scalar codebooks, HFSQ effectively filters out high- frequency artifacts while preserving meaningful articulation. As shown in Table 7, ReactDance achieves significantly lower Jitter (25.32) compared to the autoregressive baseline Duolando (32.04). Visual comparisons provided in the Hand Modeling Comparison Video.
Table 7: Hand Motion Analysis. ReactDance effectively filters out high-frequency artifacts inherent the dataset, significantly outperforming Duolando in motion stability (lower Jitter).
Method
MPJPE |
Jitter
FID,
Duolando
340.40
32.04
9.37
ReactDance (Ours)
244.23
25.32
8.85
18

---

Published as a conference paper at ICLR 2026
A.9 ABLATION OF CONDITIONING SIGNALS
We investigate the distinct roles of the leader's motion and the musical accompaniment in guiding the generation process. We trained variants of our model by removing each condition individually. The results are summarized in Table 8.
Effect of Leader Motion: Removing the leader's motion causes collapse in interaction quality, as evidenced by the sharp degradation in FID,z (14.2 + 48.8). This confirms that the leader's signal is critical for establishing the structural and spatial framework of the reaction.
Interestingly, removing the music condition leads to slight improvement in MPJPE (reconstruction error) but significant degradation interaction realism (FID 14.2 - 35.4). This phenomenon occurs because, without music, the model becomes a "mechanical tracker", tending to generate safe poses to minimize geometric error. However, loses the nuances required for high-fidelity dance generation, confirming that music is essential for fine-grained interactions.
Effect of Music:
Table Ablation of Conditioning Inputs. Results indicate that the leader's motion is crucial for structural interaction, while music is essential for fine-grained interactive realism.
Solo Metries
Interactive Metries FID
Method
MPJPE
MPIVE 1
BAS +
Ground Truth
0.1791
w/o. Leader Motion
238.50
22.76
48.82
0,2074
w/o. Music
130.27
15.05
35.35
0.1947
ReactDance (Full)
132.99
15.68
14.17
0.2031
A.10 GENERALIZATION VIA ROLE SWAPPING
To verify that ReactDance learns generalized interaction rules rather than memorizing fixed leader- follower pairs, we conducted Role Swapping experiment. Specifically, we inverted input conditions by feeding follower's ' motion as the leader signal and tasked the model to generate the leader's 's part.
As demonstrated in Role Swapping Video, the model successfully generates valid, semantically coherent reactions for the reversed pairs. This confirms that ReactDance captures the underlying physical and choreographic rules of interaction (e.g., synchronization, relative spacing) independent of specific character roles.
A.11 ZERO-SIIE SERO-SHOT GENERALIZATION ON OUT-OR- DATA
To assess the robustness of ReactDance beyond the specific choreography of the training set (DD100), we conducted Zero-Shot evaluation using the FineDance dataset (Li et al., 2023). FineDance contains diverse solo dance genres that disjoint from our training data, serving as rigorous Out-of-Distribttion (OOD) input for the leader.
Despite the significant domain shift in dance styles and musical accompaniment, React|Dance syn- thesizes physically plausible and semantically coherent reactions. This suggests that the model has successfully learned generalized interaction dynamics-such velocity matching. spatial awareness, and rhythmic synchronization-raa rather than merely memorizing dataset-specific motion patterns.
Qualitative demonstrations of this generalization capability are provided following videos:
Spatial Precision: The model maintains precise upper limbs positioning relative to the unseen leader (Video: Upper Limbs Spatial Alignment).
Dynamic Synchronization: The reactor successfully anticipates and matches complex rotational movements (Video: Synchronized Spinning).
Musical Generalization; The model generates appropriate rhythmic responses to unseen musical
styles (Video: Rhythmic Accompaniment).
Kinematic Adaptability: The model handles extreme kinematic variations, adapting to unseen
bending leader poses (Video: Large Pose Adaptation).
19


Reviews:
[{"review_id": "8kK0WzPPW7", "reviewer": "Reviewer_X7nk", "summary": "ReactDance proposes a two-stage framework based on diffusion for long-duration reactive dance generation. The paper designs a hierarchical latent representation called Hierarchical Finite Scalar Quantization (HFSQ), which decouples coarse body posture from fine-grained body dynamics, enhancing spatial detail and controllability. To efficiently and coherently generate ultra-long sequences, the authors introduce Blockwise Local Context (BLC) combined with dense sliding-window training, enabling parallel block sampling to avoid error accumulation. Additionally, independent guidance weights are applied at each layer through Layer-Decoupled Classifier-Free Guidance (LDCFG), offering fine-grained control over different semantic scales.", "questions": "1. From the appendix, does the LDCFG setup imply the HFSQ has only two residual stages (i.e., R=2)? If so, please clarify the generality of your design and report results for other values of R to show scalability.  \n\n2. The reported LDCFG experiments use equal guidance weights (s_1=s_2 = 1.2); how does this demonstrate the benefit of layer‑decoupling?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Hierarchical Finite Scalar Quantization (HFSQ) provides a stable, continuous multi-scale latent that disentangles coarse global posture from fine local articulations, enabling high-fidelity, layerwise control of motion detail.\n\n2. Blockwise Local Context (BLC) provides a stable, continuous multi-scale latent that disentangles coarse global posture from fine local articulations, enabling high-fidelity, layerwise control of motion detail.\n\n3. The paper is well-organized with a clear structure and lucid exposition that make the motivation, method, and technical details easy to follow.  \n\n4. The experiments and comparisons are comprehensive and rigorous, covering multiple baselines, ablations, quantitative metrics, qualitative visualizations, and a user study.", "weaknesses": "1. The HFSQ design does not specify the number of residual stages R, leaving unclear the representational capacity and the coarse–fine trade-off, which hinders reproducibility and complicates tuning of per-stage guidance weights s_r.  \n\n2. The BLC training/inference protocol is underspecified (e.g., whether sliding-window stride m can cross T), so it is unclear how periodic causal masking affects cross‑block continuity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ziCxZeUgPv", "reviewer": "Reviewer_5QeS", "summary": "This work addresses the dance accompaniment problem (or, as the authors term it, reactive dance generation) where the input consists of a lead motion and corresponding music, and the goal is to generate the follower motion.\n\nThe main challenge of this task lies in its spatio-temporal complexity: how to efficiently and accurately represent motion in space while maintaining temporal coherence and preventing error accumulation over time.\n\nTo tackle this, the authors propose a hierarchical Finite Scalar Quantization (FSQ) model combined with a diffusion-based generation framework. The method builds upon FSQ with residual encoding and integrates it into the diffusion process to produce high-quality accompanying motions.\n\nThe paper explores a range of implementation details. For example, it introduces Blockwise Local Context, which investigates how to make diffusion more context-aware, and Layer-Decoupled Classifier-Free Guidance, designed to apply classifier-free guidance to hierarchical SQ targets.\n\nThe proposed approach achieves promissing results. Both quantitatively and qualitatively, as demonstrated in the accompanying videos. The work is also thorough, with extensive discussions, detailed methodology, and comprehensive ablation studies.\n\nIn addition, I feel some aspects of this work are particularly insightful and valuable. The hierarchical representation of motion using FSQ, its impact on motion quality, and the overall FSQ+diffusion generation process provide meaningful contributions that could inspire future research in the community.", "questions": "see above", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Technical Comments:**\n\nOverall, this paper is quite novel in its design and methodology.\n\n\n1. The paper identifies the limitations of FSQ in motion representation space and explores hierarchical and residual variants of FSQ for richer motion encoding.\n\n\n2. Instead of following the conventional Quantize → GPT / autoregressive pipeline, the paper investigates a diffusion model targeting the dequantized FSQ features. This is particularly interesting because it implicitly raises an important question: Can the dequantized FSQ serve as an effective tokenizer for DiT?\nThe authors compare their approach with a 2-layer RVQ baseline, but it would also be valuable to explore other tokenization or encoding schemes (e.g., VAE) to understand their influence on the subsequent diffusion process.\n\n3. The proposed Blockwise Local Context mechanism allows causal modeling within each block while maintaining synchronized blockwise generation. This design is elegant and effectively mitigates the error accumulation problem typically seen in long autoregressive inference.\n\n**Experiments:**\nQuantitatively, the results consistently outperform state-of-the-art baselines. The visual quality is also impressive, and the ablation studies are comprehensive.\n\n**Writing and Presentation:**\nThe overall writing is clear. Although the demo video contains some background noise, it still effectively demonstrates the model’s performance and validity.", "weaknesses": "1. Although the Discussion section provides some interpretation of LDCFG, the explanation remains insufficiently clear. For example, how is  {s_coarse,s_fine} sampled during training? What exactly is defined as the coarse part and what as the fine part?\n\n2. As mentioned in the Strengths section, the authors found that both HFSQ and PM contribute positively to the subsequent diffusion process. However, no further exploration was conducted. For instance, how would a VAE perform as the diffusion encoder/tokenizer? Or how would RVQ + PM function as a tokenizer?\n\n3. The video presentation is acceptable overall but suffers from recording issues, such as noticeable background noise and the absence of background music in some examples.\n\nMinor issue:\n\nLine 49: quotation mark formatting problem.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hWnxP7iSDw", "reviewer": "Reviewer_5N4J", "summary": "The paper introduces a HFSQ+diffusion framework for long-term reactive dance generation and achieves superior performance. Specifically, the paper first proposes a Hierarchical Finite Scalar Quantization, which combines FSQ with a hierarchical structure, to generate the complex motion in a coarse-to-fine manner. Furthermore, to enable the long-term dance generation, the paper proposes a Blockwise Local Context. The BLC generates each subsequence in parallel, while the inter-block continuity is ensured by dense sliding window training.", "questions": "1. The clarification of how the BLC can solve the inter-block continuity in parallel.\n2. The experiment should validate the claim of the fine-grained generation of \"subtle limb dynamics\".", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed HFSQ is technically sound for fine-grained motion generation.\n2. The proposed BLC seems to effectively solve the long-term generation issue with improved sampling efficiency.\n3. The method shows an impressive result according to the quantitative metrics and the video provided in the supplementary.", "weaknesses": "1. It is still unclear to me why the continuity is ensured in parallel inference. Why the model can \" implicitly learns how to naturally begin and end motion phrases from any temporal point\"？ How does this related to the dense sliding window？  Could the author further explain it?\n\n2. To provide a more robust assessment and validate the effectiveness of proposed kinematic loss, the physical plausibility metrics should be added, such as PFC [1].\n3. I think HFSQ is proved to be effective for generating more realistic dance in a corse-to-fine manner, yet the claim that this training manner can \"disentangles coarse body posture from subtle limb dynamics\" has not been fully verified. The HFSQ can certainly produce a more accurate reconstruction motion, but it is not necessarily to be limb dynamics. Experiments has not validated this claim either.\n4. In line 126-127, the author claim that DuetDance's [2] coupled representation limits motion diversity. However, this does not seem to hold true based on the metrics in the original paper. Could the author provide more explanation?\n   \n[1] Jonathan Tseng, Rodrigo Castellon, and C. Karen Liu. EDGE: Editable dance generation from music. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 448–458. IEEE, 2023.  \n[2]  Anindita Ghosh, Bing Zhou, Rishabh Dabral, Jian Wang, Vladislav Golyanik, Christian Theobalt, Philipp Slusallek, and Chuan Guo. Duetgen: Music driven two-person dance generation via hierarchical masked modeling. In Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers, pp. 1–11, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3u5U2BAsHY", "reviewer": "Reviewer_dk6L", "summary": "This paper addresses the task of Reactive Dance Generation (RDG), with the specific goal of synthesizing high-fidelity, coherent, and long-form dance sequences that react to a lead dancer's motion and accompanying music. The authors identify two primary challenges in prior work: (1) modeling fine-grained spatial interactions between dancers, and (2) maintaining temporal coherence over long durations.\nTo tackle these issues, the paper introduces ReactDance, a diffusion-based framework built upon a novel hierarchical latent space. The core contributions of the work are twofold:\nHierarchical Finite Scalar Quantization (HFSQ): The authors propose a new multi-scale motion representation. This autoencoder-based module is designed to disentangle coarse-scale body posture from fine-grained limb dynamics, creating a structured latent space that facilitates hierarchical modeling.\nBlockwise Local Context (BLC): For long sequence generation, the paper puts forward a non-autoregressive sampling strategy. BLC partitions a long sequence into blocks and synthesizes them in parallel. It employs periodic causal masking and positional encodings to ensure local consistency within each block, aiming to mitigate the error accumulation and inefficiency associated with traditional autoregressive methods.\nThe authors evaluate their method on the DD100 paired dance dataset, comparing it against a suite of state-of-the-art baselines. The experiments cover metrics for motion quality, interaction quality, music-dance alignment, and generation efficiency. The reported results indicate that ReactDance achieves superior performance across the majority of these metrics.", "questions": "(1) Could you please provide a layer-wise reconstruction visualization? Specifically, what does the output motion look like when decoded from only the first HFSQ layer, and how does it progressively refine as subsequent layers are added? This is crucial to substantiate your claim of coarse-to-fine semantic disentanglement.\n(2) Given that the model is only trained on 8s clips, how do you argue it can learn long-term choreographic structure beyond just kinematic continuity? Could you provide an analysis of a very long generated sequence (e.g., >2 minutes) to demonstrate its semantic diversity and structural integrity, for instance, by showing it does not fall into repetitive loops? if it works, a convincing explanation is also needed.\n(3) On Generalization and Robustness: How does your model perform on out-of-distribution data? For example, what is the generated reaction if the leader performs a dance style not present in DD100 (e.g., popping) or a non-dance interaction (e.g., a handshake)?\nOn Evaluation and Missing Details:\n(5) Could you provide a quantitative evaluation of interpenetration between the dancers?\n(6) How do you reconcile the \"fine-grained\" claim with the complete omission of hand interactions, which are arguably the most fine-grained and crucial part of partner dance?\n(7) Could you provide an ablation study that disentangles the contributions of the audio and leader motion conditions to the final output?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "(1) The paper demonstrate originality by combining and adapting ideas for the challenging task of long-form reactive dance generation. The application of a hierarchical quantizer (HFSQ), inspired by audio codecs, to motion representation is novel. The proposed Blockwise Local Context (BLC) sampling strategy is an innovative, non-autoregressive approach to generating long sequences, distinct from common iterative refinement or autoregressive methods.\n(2) The proposed ReactDance framework is a complete system that addresses the problem end-to-end. The quantitative results presented in Table 1 shows that the method outperforms state-of-the-art baselines across numerous metrics on the DD100 dataset. \n(3)The paper is well-written, clearly structured, and easy to follow. The authors effectively articulate the two main challenges in the field (fine-grained interaction and long-term coherence) and systematically introduce their proposed solutions (HFSQ and BLC). The diagrams, particularly the pipeline overview in Figure 4, are helpful in understanding the overall architecture.", "weaknesses": "(1) Unsubstantiated Semantic Claims of HFSQ Representation: The paper's core claim that HFSQ \"explicitly disentangles\" motion semantics is a strong assertion that is not only unsubstantiated by evidence but also potentially disconnected from the method's actual implementation.\n    (a) In the Abstract (Lines 020-022), they state it \"...effectively disentangles coarse body posture from subtle limb dynamics...,\" and in the Introduction (Lines 089-090), they claim it \"...explicitly disentangles coarse global movements from fine-grained local details.\" However, the mathematical reality of HFSQ (Eq. 1) is that of a generic cascaded residual quantizer. It is optimized to minimize reconstruction error layer by layer, with no inherent mechanism to ensure that this mathematical decomposition aligns with a human-perceived semantic hierarchy (i.e., posture → limb details). The paper operates on the unproven assumption that low-frequency/high-amplitude errors correspond to \"coarse\" semantics, which may not hold true for complex dance motions.\n    (b) To bridge this gap and validate their claims, a layer-wise reconstruction visualization or valid proof of HFSQ for semantic disentanglement is needed. Without an experiment showing the decoded motion from each progressive layer of the hierarchy, the claims about semantic disentanglement and the resulting \"fine-grained control\" remain speculative assumptions.\n\n(2) The paper's central claim of achieving \"coherent long-form\" generation seems like a major overstatement, as it rests on an overly narrow definition of \"coherence\" and a method (BLC) that is fundamentally incapable of learning global choreographic structure.\n     (a): Loss of Global Context and Narrative: A complete dance performance possesses a clear structure (e.g., introduction, development, climax, conclusion). The BLC mechanism, by design, induces a form of \"global amnesia,\" resetting the temporal context for each block. This prevents the model from perceiving the absolute position of any given segment within the larger performance. Consequently, it is theoretically impossible for the model to generate a dance with a narrative arc. What it produces is a chain of locally-coherent but globally disconnected motion snippets, stitched together. The \"coherence\" achieved is merely low-level kinematic continuity (no drifting or freezing), not the high-level artistic and structural coherence.\n    (b): Unreliable \"Implicit\" Transition Learning: The authors' claim that dense sliding-window training enables the model to \"implicitly\" learn smooth transitions is an overly optimistic and unreliable assumption. The method lacks any explicit mechanism to ensure that the end of Block N is semantically and dynamically compatible with the start of Block N+1. While the model may learn generic start/end poses, this provides no guarantee of a meaningful transition at every arbitrary splice point. It is highly likely that many transitions, while kinematically smooth, are logically broken.\n    (c): Missing Global Structure Analysis: check for pattern repetition or mode collapse in long dance generation is required. The long-term sequence produced by BLC is very likely to be repetitive actions.\n\n(3) The paper's apparent success in long-form coherence might be a byproduct of the highly constraining nature of the conditioning signals (the leader's dense motion), which could, in turn, severely limit the model's generative diversity at a semantic level.\nIn a reactive dance task, the follower's motion is tightly coupled with the leader's on a frame-by-frame basis. This strong constraint simplifies the problem of maintaining kinematic continuity; the model's primary task becomes finding a valid \"following\" state for each frame, which naturally links the sequence together. The impressive coherence might therefore stem more from the problem's inherent constraints than from the architectural sophistication of BLC alone. This leads to a critical concern about diversity. The paper reports Divk and Divg metrics, but I still want to know: given the exact same leader motion, can the model generate fundamentally different but equally plausible reactions? Using a concrete example: given the same leader, can the model generate one sequence where the follower places a hand on the leader's shoulder, and another, distinct sequence where they place the hand on the waist? \n\n(4) The Training Paradigm Severely Limits Generalization: The model is trained on a small and specific dataset (1.95 hours, 10 genres). This raises serious concerns about its robustness and generalization ability. The paper provides no experiments on out-of-distribution data (e.g., different dance styles, non-dance interactions) or cross-dataset validation.\n\n(5) Contradictory Claims and Critical Omissions in Evaluation:\n    (a) The claim of modeling \"fine-grained limb movements\" is directly contradicted by the admission that all hand and finger motions are omitted. In partner dance, hands are the primary medium for physical communication and interaction, making their omission a critical failure for a model focused on fine-grained interaction.\n    (b) For a multi-person motion generation task, interpenetration is a primary failure mode. The paper completely lacks any quantitative metric to evaluate collision or penetration, which is a major oversight in assessing the physical plausibility of the generated interactions.\n    (c) The paper fails to ablate the influence of the two control signals (audio vs. leader motion) and does not test the model's understanding of interaction roles (e.g., by swapping the leader and follower in a pair).\n\n(6) The paper claims that LDCFG enables fine-grained control over different body parts like the upper and lower body. However, the mechanism for this control is explained ambiguously. The ability to control body parts does not stem from an intrinsic property of the HFSQ representation itself, which is a general-purpose quantizer. Instead, it originates from a crucial, explicit design choice in the input representation (Section 3.1), where the reactor's motion is manually split into separate streams (upper-body, lower-body, etc.), each processed by a dedicated HFSQ module. The paper fails to clearly attribute the control capability to this architectural choice, potentially misleading readers into believing that the HFSQ representation magically learns this spatial disentanglement. This lack of clarity should be rectified.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)