Unified Multi-Modal Interactive and Reactive 3D Motion Generation via Rectified Flow
Authors: Prerit Gupta
Shourya Verma
Ananth Grama
Aniket Bera
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-modal
Generative Modeling
Interactive Motion
Reactive Motion
Rectified Flow
RAG
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Unified Multi-Modal Interactive and Reactive 3D Motion Generation via Rectified Flow

Abstract: Generating realistic, context-aware two-person motion conditioned on diverse modalities remains a fundamental challenge for graphics, animation and embodied AI systems. Real-world applications such as VR/AR companions, social robotics and game agents require models capable of producing coordinated interpersonal behavior while flexibly switching between interactive and reactive generation. We introduce DualFlow, the first unified and efficient framework for multi-modal two-person motion generation. DualFlow conditions 3D motion generation on diverse inputs, including text, music, and prior motion sequences. Leveraging rectified flow, it achieves deterministic straight-line sampling paths between noise and data, reducing inference time and mitigating error accumulation common in diffusion-based models. To enhance semantic grounding, DualFlow employs a novel Retrieval-Augmented Generation (RAG) module for two-person motion that retrieves motion exemplars using music features and LLM-based text decompositions of spatial relations, body movements, and rhythmic patterns. We use contrastive rectified flow objective to further sharpen alignment with conditioning signals and add synchronization loss to improve inter-person temporal coordination. Extensive evaluations across interactive, reactive, and multi-modal benchmarks demonstrate that DualFlow consistently improves motion quality, responsiveness, and semantic fidelity. DualFlow achieves state-of-the-art performance in two-person multi-modal motion generation, producing coherent, expressive, and rhythmically synchronized motion.

Published as a conference paper at ICLR 2026
UNIFIED MULTI-MODALL INTERACTIVE & REACTIVE 3D MOTION GENERATION VIA RECTIFIED FLOW
Prerit Gupta" Shourya Verma' Ananth Grama & Aniket Bera
Department of Computer Science Purdue University
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@purdue.edu http:://gperri....ggttaa.......0 httthuooac/duaalloo-aaag
ABSTRACT
Generating realistic, context-aware two-person motion conditioned on diverse modalities remains fundamental challenge for graphics, animation and embodied AI systems. Real-world applications such as VR/RR companions, social robotics and game agents require models capable of producing coordinated interpersonal behavior while flexibly switching between interactive and reactive generation. We introduce DualFlow, the first unified and efficient framework for multi-modal two-person motion generation. DualFlow conditions 3D motion generation on diverse inputs, including text, music, and prior motion sequences. Leveraging rectified flow, it achieves deterministic straight-line sampling paths between noise and data, reducing inference time and mitigating error accumulation common in diffusion-based models. To enhance semantic grounding, DualFlow employs a novel Setrreval-Augmennt Generation (RAG) module for two-person motion that retrieves motion exemplars using music features and LLM-based text decom- positions of spatial relations, body movements, and rhythmic patterns. We use contrastive rectified flow objective to further sharpen alignment with conditioning signals and add synchronization loss to improve inter-person temporal coordina- tion. Extensive evaluations across interactive, reactive, and multi-modal bench- marks demonstrate that DualFlow consistently improves motion quality, respon- siveness, and semantic fidelity. DualFlow achieves state-of-the-ar performance in two-person multi-modal motion generation, producing coherent, expressive, and rhythmically synchronised motion.
INTRODUCTION
Generating realistic, context-aware interactive human motion remains a core challenge in computer graphics and human human-computer interaction (Holden et al., 2016). Synthesizing coordinated multi- person behavior requires capturing mutual responsiveness, physical plausibility, and interpersonal dynamics which is essential for immersive VR/AR, game AI, and human-robot collaboration. Since interactions are driven by multi-modal stimuli (language, music, physical cues), generative systems must integrate these inputs. Real-world embodied agents must also switch between interactive co- ordination with other agents and reactive adaptation to human partners, making flexible multi-task generation crucial. Existing two-person motion approaches treat interactive and reactive settings as separate tasks with incompatible architectures, training objectives, and conditioning signals. Inter- active models (Liang et al. 20244 Ghosh et al.] 2025) focus on bidirectional coordination without handling asymmetric reactive generation, while reactive models Rahman al. (2022); Xu et al.
(204)); Siyao et al. (2024) specialize in predicting reactor motion from actor cues. Current meth- ods support only magle-modality conditioning: text-only (Liang al.) 2024 Xu al. 2024] or music-only (Siyao et al. 22244] Ghosh et al. (0255). No unified model performs both tasks under one architecture while leveraging multi-modal cues.
We introduce DualFlow, the first unified multi-modal rectified flow framework for interactive and reactive two-person motion generation (Fig. []. DualFlow employs cascaded DualFlow Blocks that
Equual contribution.

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Interactive Motion Generation
Text Music
+ M Syrcopoted natural turn diesed holl
GPT-4a
Spattal
Chead tork Dieer
Body
RAG
DualFlow
Hlylte
SoA rapooior
Motion
Contrustive
Reatified Flow
Reactive Motion Generation
Figure I: Our DualFllw model unifies tasks: (a) Interactive Motion Generation, which syn- thesizes synchronized two-person interactions, (b) Reactive Motion Generation, which generates responsive motions for Person B (red) conditioned on Person A's (blue) movements. The generation process is conditioned jointly on text, music, and the retrieved motion samples.
adapt through masking: both branches activate for interactive generation, while only the reactor branch conditions on actor motion for reactive synthesis. This enables task switching without re- training and shared representation learning.
DualFlow incorporates novel RAG adaptation for two-person motion. Unlike single-person RAG modules, our model retrieves semantically relevant samples using J_M-ddcomposed interactive text descriptions (spatial relationships, body movements, rhythm) and music features. Retrieved sam- ples inject through retrieval-based cross-attention in each DualFlow block, grounding generation in interaction-awent exemplars and improving spatial-semantic alignment. DualFlow further employs Contrastive Rectified Flow generation, where contrastive learning sharpens motion embeddings, im- proves inter-person relational consistency, and strengthens motion-condition alignment. Combined with rectified flow sampling (faster convergence, reduced error accumulation), these contrastive ob- jectives enhance diversity, coherence, and semantic fidelity.
Our key contributions are: (1) Unified architecture for interactive and reactive two-person motion generation with seamless task switching. (2) A Cetrieval-Augmennte Generation (RAG) framework for two-person motion generation leveraging music features and interactive text-based descriptions (spatial relationship, body movement, rhythm) decomposed using L.MM guide semantically faith- ful motion synthesis. (3) Contrastive Rectified Flow based generation with added synchronization loss, improving motion quality, semantic alignment and faster sampling. (4) Extensive quantitative, qualitative, and ablation studies on diverse two-person datasets, showing DualFlow generates coher- ent, expressive, and contextually appropriate motions with fewer sampling steps. Importantly, our approach outperforms state-of of-the-art baselines by 2.5% in FID, 76% in R-precision, 3x in Multi- Modal Distance for Interactive task, 1.7% FID, 2.5x in R-precision, 2x Multi-Modal Distance for Reactive task on MDD Dataset requiring only 20 inference steps (2.5x faster) than 50-DDIM standard, establishing new benchmark for multi-person, multi multi-modal motion generation.
RELATED WORK
Two-person Motion Generation. While single-person motion generation has advanced rapidly (Guo al. 2022, Tevet et al. 2022 Petrovich al., 2022; Zhang al. (202)), extending these methods to multi-person settings introduces the additional challenge of modeling coordination
between agents. Early two-person models (Kundu et al., 2020 Xu et al. 2023 Xie al.) 2021) demonstrated feasibility but exhibited limited generalization
weak semantic grounding. To ad- dress data scarcity and modeling complexity, Liang et al. (2024) introduced large-scale interaction dataset with text-conditioned diffusion model, later extended by text-guided variants (Shafir al.)
2024 Yi et al. 2024 Li et al. 2024a). the domain of dance, specialized frameworks explored consicconddtioned lead follower generation (Li et al.) 2024b) Wang al. 2025a; Ghosh et al.) 2025). . Despite these advances, most diffusion- methods remain slow and restricted to single- modality conditioning. For reactive motion generation, early GAN- and transformer-based meth-
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ods (Men et al.) 2022 Rahman al., 2022 Ghosh al. 2024) have recently been extended with text (Xu et al. 2024, Cen et al. 20255 or with joint leader motion and music (Siyao et al.) 2024). However, existing interactive and reactive models are developed separate systems with incom- patible architectures and training objectives, limited multi-modal support and preventing seamless switching between tasks. Our framework, DualFlow, addresses these limitations by unifying inter- active and reactive two-person motion generation within a single transformer-bass rectified flow architecture, jointly conditioned on text, music, and retrieved motion exemplars.
Retrieval-Augmennte Generation (RAG). RAG has significantly improved generative fidelity across language models (Gao et al.) 2023 Guu et al.] 2020 Lewis al., (020). image synthe- sis (Blattmann et al. [he2] Chen et al. 2022 Sheynin et al. 2022). and video generation (He et al.)
2023). Within motion generation, retrieval- based approaches have been applied text-to-motion synthesis (Zhang et al. 2023 Kalakonda et al. 2025 Liao et al., 2024 Petrovich et al.) 2023 [enns] abath al. 2024)) but all existing methods operate exclusively in the single-person setting and do not address interactive multi-person dynamies. DualFlow introduces the first RAG framework for two-person motion generation, retrieving interaction-awar motion exemplars using music features and LLM-based text decompositions capturing spatial relationships, body movements, and rhyth- mic structure. These exemplars are integrated through a retrieval-bait based cross-attention mechanism providing fine-grained semantic grounding crucial for coordinated two-person motion generation.
Diffusion and Flow-based Models. Diffusion- based motion generation models such as MDM (Tevet al. 2022). MotionDiffuse (Zhang al., (024), and MoFusion (Dabral et al.] 2023) have demonstrated strong performance with fewer than hundred denoising steps, but they remain limited to single-person generation. More recent approaches adopt Flow Matching (Lipman al.
2023) to bypass iterative denoising (Hu et al. [2023] Canales Cuba & Gois 2025). Yet these methods face optimization instabilities and scaling difficulties when extended to multi-person motion. Inter- Gen (Liang et al. 2024), TIMotion (Wang et al. (0225)) are diffusion models tailored for two-person generation needing roughly 50 denoising steps for inference. Our framework builds on Rectified Flow (L.u et al. 2022), which introduces deterministic straight-line transport map between noisy and clean samples, yielding simpler training dynamics and significantly faster (20 steps), more stable sampling. DualFlow extends this paradigm with contrastive rectified flow objective that sharpens motion representations and strengthens alignment with multi-modal conditioning signals.
METHODS
3.1 PROBLEM FORMULATION
A two-person motion interaction x E XA x X'g is represented as person A's motion X = (x- N and person B's motion Xb- = [ where paired frames xd = (xr.2() xg) are naturally synchro- nized. For the asymmetric case, person A is the Actor and person the Reactor. The motion space is X' C R N xx3 with N frames and J joints. Music features lie in M C IR Nxdm with dimension dm, and text embeddings in C c Rddd with dimension de.
Interactive Motion Generation. Given text E C and/or music m E .M, the task is to generate synchronized two-person motion (X,, xx) aligned with both modalities: F(c, m) + x Special cases
include text-only (m Φ) (Liang et al. 2024), music-only (c = Φ) (Li et al., 2024b Ghosh et al., 2025), and joint text-music conditioning defined as Text-to-Duet by Gupta et al. (2025).
Reactive Motion Generation. Given the actor's motion Xa E X, text E C, and/or music E M, the goal is to generate the reactor's motion Xb E X' such that (Xa, Xb) are coherent and synchro- nized: G,c,m, Xa) + Xb- Variants include text-only (m = Φ) (Xu al., 2024). music-only (c = Φ) (Siyao et al. 2024). and joint text-music conditioning defined as Text-to- Dance Accompa-
niment by Gupta et al. (2025).
Human Motion Representation. We represent motion in a global coordinate system, where the origin is anchored the root joint of person A. The position of person B is expressed relative to this root, ensuring a unified spatial reference frame for both. Our motion representation is based on the format introduced by Liang al. (2024), and encodes a single frame of an individual's 's motion as
x' = bi..jy J" J.). Each frame includes global joint positions jú E R3N,, global joint velocities
jy R3N, local joint rotations j" -1) in 6D format within root-relative coordinate frame,
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and binary foot contact indicators of I+ that specify ground contact status for each foot joint that time step. To model body joint rotations, we use the SMPL model (Loper et al. 2015) with N₂ 22 joints, resulting in fixed input dimension of 2; R
Motion Pair
Xay Xa
cieed posttioe lacing such
relantio tamo
Pryytinnio secand
Syacopated nakuna km deood
Spotial Relntioncipp
Body Xovement
Klytiim
Jokeltrr Ratealer
CLIP 1/14
Peeson (Blostor)
(Actor)
Trensfomer Encoder
Flow Timestep
Thp
Thp
Tope
Transfomer Bnceder
X
RP
Linor
Lineor
Litoz
Lineor
Linwer
Linar
Linse
Linar
+
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Z
Zym
(0)
(0)
Multi Modal DualFlow blocks
xN
X
Input Mask
Losa function
Luasl
Adseptiv Layer
Resciiv
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Activation
Temport
(a) DualFllo Architecture
Caavid
Z0
Silf
Musit Unooes Aftns
Mixion Unioee Afn
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Figure 2: (a) Our framework takes text (CLIP-L/14), music, and motion sequences from an actor (A) and reactor (B) as inputs. Motion samples retrieved using music features and -ee-decopposed text cues (spatial relationship, body movement, rhythm). These modality-specific latents are pro- cessed cascaded Multi-Modal DualFlow Blocks that model interactive dynamics. Outputs either both actors' motions (interactive) only the reactor's 's motion (reactive) via a masking mech- anism. (b) A DualFlow Block: the interactive setting. both branches operate symmetrically with Motion Cross Attention coordinating joint motion; in the reactive setting, the actor branch is masked and the reactor branch employs Causal Cross Attention module with Look-Ahead L, replacing Mo- tion Cross Attention, to condition on the actor's s motion.
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3.2 MULTI-MODAL MOTION RETRIEVAL
Retrieval Dataset. Direct retrieval from raw text often overlooks the nuanced dimensions of in- teractive human motion, yielding low diversity or biased matches. To address this, we use GPT-4o to decompose each prompt into three focused descriptions (Hurst et al.] 2024), inspired by La- ban Movement Analysis (Laban] 1950) and aligned with the MDD Dataset (Gupta al.) 2025 : (1) Spatial Relationship (proximity, orientation. handholds), (2) Body Movement (actions, body parts, posture), and (3) Rhythm (timing, musicality, stepping). To achieve high-quality and con- sistent decomposition, we design a structured prompting framework for the LLM (details in Ap- pendix). For each category, we build retrieval databases using CLIP (Radford et al. 2021 embed- dings (Ds, DD (D) and music embeddings (DM) from Jukebox (Dhariwal et al. 2020).
Similarity Scoring. We generalize the similarity scoring function introduced by Zhang et al. (2023) for any modality q. For a given query sample p with soddaliy-specifii feature embedding fg. and candidate database motion sample x; with embedding f/ and motion length 4, the similarity score sy is computed as:
14--pl mmx
af = (1%
(1)
where (:.) is cosine similarity and the exponential term penalizes mismatch across motion length with sensitivity X, allowing retrievals that are semantically aligned and temporally compatible. Us- retrieve top-k matches from each database for every sample, yielding sets
ing this scoring, (RS R. R. R as shown in Fig. 2 The retrieved sets collectively offer a diverse yet semanti- cally relevant collection of motion exemplars, which are later used to guide generation.
3.3 MODEL ARCHITECTURE
Conditioning latents. The text description d is encoded using a pretrained CLIP model (Radford jet al. (021) followed by a transformer encoder, then linearly projected and fused with time-step embeddings to form the text latent z,z Similarly, the music input m is processed by pretrained Jukebox encoder (Dhariwal et al., 2220)), linearly transformed, and passed through a transformer en- coder to obtain the music latent Z. For retrieval-based conditioning, we use four retrieved motion sets (R. R.B. RR .RM) corresponding to spatial, body, rhythm, and music cues. Positional encod- ings and a shared linear projection map these samples to the motion latent space, and the resulting features are concatenated into the aggregated retrieval latent zR.
Model Pipeline. Motion inputs x" and x sampled for time step l. are first projected through in- dividual linear layers, followed by the addition of positional encodings, resulting initial motion latents {zu,) z
" }. They are fed into the main pipeline consisting of N cascaded DualFlow blocks.
The first block takes the initial motion latents {z((), ((0)) as input. Each subsequent block (j + 1)
takes the outputs from the previous block {zl?) .28 2,60)) and produces updated latents (z(++1) 2b (-)),,
where j E 10. N - 1}. All blocks are jointly conditioned on the multi-modal context (Za, Zm, 2R) The output from the last block, {x4-1 --,,,,,--1 xxx gives the denoised motion.
DualFlow Block. Each DualFlow block refines motion representations through temporally-aware and context-conditioned operations. It begins with a multi-scale temporal convolution module with varying kernel sizes to capture motion patterns at different time resolutions, followed by a GELU activation (Hendrycks & Gimpell [202)). Branch outputs are adaptively fused using learnable gat- ing weights Tk- The representation then passes through Self-Attention layer to model internal temporal dependencies. followed by structured sequence of
layers: (1) Music Cross-Artention to align motion with music latent Z,,. (2) Motion Cross-Attention to capture inter- person interaction which gets replaced by Casual Cross-Aftention with Look-Ahead during reactive setting and (3) Retrieval Cross-Attention to semantically guide generation using retrieved exemplars. All modules use residual connections for stability, and the text latent z, is injected via LayerNorm conditioning. Each block thus integrates temporal structure, musical thythm, and semantic guidance from retrieval. Please refer to Appendix for detailed description of each module.
Task settings. In interactive setting, both x", and xÿ are sampled for time step f as input. In reactive setting, only reactor's 's motion xs sampled, while actor's motion x; is masked on the input side and used for conditioning. To enable anticipatory reactor response, the Motion Cross-attens Pross-attention is switched with Causal Cross Attention Layer having a Lhok-Ahead parameter L.. It uses upper
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triangular mask such that reactor's motion attends to past and only L future frames of actor's 's motion (Fig 2 This look-ahead mechanism ensures temporally aligned and context-award -warre generation.
3.4 CONTRASTIVE RECTIFIED FLOw
To generate realistic and semantically grounded duet motions, we build upon the Rectified Flow Matching framework (Liu et al. (0022) and augment it with a contrastive learning objective inspired by Contrastive Flow Matching Stoica et al. (2025). Unlike traditional diffusion models that rely on stochastic denoising, rectified flow formulates the generation process as a deterministic Ordinary Differential Equation (ODE) that transports a noise sample toward a data sample along a straight- line path in motion space. Given a ground truth motion sample X and noise sample ~ WO I), the interpolated state at time E [0, 1] is defined as: X = (1 -1- t)E + ixg. and V; = Xo E, where x, lies along the linear path from noise at t = 0 to data Xo at 1, and is the constant velocity vector guiding the transport. During inference, we integrate forward from from (0, to obtain the generated motion. We train dependennddnt neural velocity field Vo (x,,, t, c) to approximate vt. conditioned on a multimodal context c (d, m, R. R.P. R, R,M), ), which includes the text description d, music segment m, and retrieved motion sets. This context encoded using cross attention layers in DualFlow Block. The flow loss Lnaw is obtained by minimizing the squared error between the predicted and target velocity:
0 to = 1 starting
Laww
Ex
[|vo(xi, 4, (xo - =)]l
(2)
To encourage semantic alignment, we introduce a tripler contrastive loss that enforces proximity in velocity space for semantically similar prompts with d(;;)) denoting cosine distance:
Cniplet = E [max (0, ((, d(v, ) +m]]
(3)
For each batch, randomly select an anchor sample whose predicted velocity is denoted as = (x,(x,,, We compute the cosine similarity between this anchor and all remaining sam- ples in the batch. Positive samples v+ defined velocities belonging to motions with high semantic or structural affinity to the anchor such as those sharing the same movement style, exhibit- ing similar textual descriptors or aligning in rhythmic structure. Negative samples v correspond to motions that differ substantially in style or exhibit low text similarity (> 0.6). This sampling strategy leverages the hierarchical structure of our RAG module to construct meaningful triplets that emphasize semantically relevant distinctions. We use margin of m 0.2 and set the triplet loss weight to Amplee = 0.1. We define contrastive flow loss LCRF that combines both losses:
LCrF Lnow + Cuiplet
Here, Aurppes balances reconstruction and semantic aligmment objective.
3.5 REGULARIZATION LOSSES
Geometric Losses. We adopt the common geometric losses for human motion such as foot contact loss Ltoot and joint velocity loss Lvel from MDM Tevet et al. (2022) and bone length loss CBL from InterGen Liang et al. (2024). The geometric loss is defined as:
Lgoo
Lroat + Avel + CBL
(5)
where the hyper-parameters \vel.ABI. are appropriately calibrated to fix the importance of each term.
Interaction Losses. We adapt joint distance map loss LDM and relative orientation loss Cro from InterGen Liang et al. (2024) that allows close interactions when dancers should be in contact as well as maintain proper facing directions and body alignments. To further strengthen inter-person coor- dination during duet generation, we introduce a synchronization loss Layne that explicitly enforces spatial relational coherence between the two person. The loss weights pairwise inter-person joint distances using anatomically informed and task- importance terms:
Lsyne
Σ waliis (2))) (1d,(1..1)) (2) d..(11 31.d2
where J2) and (2) denote the predicted and ground Euclidean distances between joint pairs across the two person. The distance-based weight wa()i,ja) assigns higher importance

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to joint pairs that are naturally closer during interaction:
(a()).j2) = (-a (4
Complementarily, (ii. j2) captures the anatomical & functional relevance of different body parts:
if j1, E Thands;
Wus
if j1.J2 Juppper:
wy(h1,82)
(8)
WI:
if j1, FTower,
Wsmalls
otherwise.
Here, Tnands (hands, wrists), Jupper (shoulders, elbows, torso). and Jlower (hips, knees, feet) denote anatomically defined joint groups. Together, these weighting terms encourage the model to preserve high-frequency synchrony while maintaining the global relational structure across the two bodies.
The interaction loss Limer is obtained as:
Cinter LDM CRO + Asyne
where the hyper- paremeerrmeeerss ARO and Asyae are fixed based on importance of each term. For reactive setting, ground-trui truth actor's motion is used for all Interaction Losses.
Total Loss. The complete training objective combines all components through balanced weighting:
Leotal LCRF + Axoo + Xinter
(10)
where the hyperparameters >gee and Vimer meticulously selected to regulate the magnitude of their corresponding terms.
RESULTS
Datasets. We train and evaluate DualFlow on three widely used two-person motion datasets spanning cext-tommotion, music-to-dance, and multi-modal duet generation: (1) InterHuman- AS (Xu al., 2024). asymmetric extension of InterHuman (Liang al. (024) with actor- reactor labels, over 50K interaction clips across 11 action types (e.g., handshake, hug) and paired SMPL-X Pavlakos et al. (2019) sequences for modeling fine-grained interpersonal dynamics. (2) DD100 Siyao et al. 2024). featuring 100 duet dance routines (e.g., salsa, hip-hop, waltz) with high-resolution motion capture, paired music, and manually annotated dance structure for rhythm and style alignment.
(3) MDD (Gupta et al. 2025), large-scale multi-modal duet dance dataset with 10.3 hours of marker-based capture and 10K+ text. annotations covering spatial relationships, choreography, movement quality, and music synchronization. Together, these datasets enable robust learning and evaluation of both interactive-reactive motion generation across multiple modalities.
Implementation Details. DualFlow consists of 20 cascaded blocks with 8 attention heads and dropout of 0.1. Both motion and conditioning inputs are projected into a 512-dimensional latent space, and each block's feedforward layer is set to size 1024. We use 4800-d Jukebox (Dhariwal ctt al.] (220)) features for music and 768-d CLIP (ViT-L/14) (Radford et al. (021) text embeddings. All cross-attention layers adopt Flash attention for faster processing. The stride values for the par- allel convolution layers used are 7, 11 and 21. The model is trained with Contrastive Rectified Flow using 200 integration steps and cosine scheduler. Training uses Adam with Ir 2x 2x10 weight decay 2x10-5 1000 warm-up steps, batch size 32, for 5000 epochs. In the reactive setting, we use 10-frame look-ahead. For classifier-free guidance, both modalities are masked 10% of the time, and text/music individually 20%. All hyperparameters were selected empirically on held-out validation set.
Evaluation Metries. We evaluate models using metrics adapted from text-to-motion (Liang et al. 2024] and music-to-motion (Siyao et al. 2024): Frechet Inception Distance (FID): Distributional similarity between ground truth and generated motions; Multimodal Distance (MM Dist): Text- motion alignment via feature distance; R- R-Precision: Text-motion alignment through retrieval accu- racies within batch; Diversity: Variety of generated motions regardless of conditions; Multimodal- ity (MModality): Diversity of generated motions under identical conditioning; Beat Echo Degree (BED): Synchronization index of the both person's 's generated motion; -lighmentt Score (BAS): Alignment between inflection points in motion and musical beats and Average Inference Time per Sentence (AITS) (Dai et al. 2024)

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4.1 QUANTITATIVE METRICS
Text & Music condition Motion Generation on MDD. We evaluate DualFlow on MDD, InterFuuaan-AS, and DI1100 using standard text-motion and music-motion metrics. As shown in Table D DualFlow consistently outperforms baselines across most metrics for duet and reactive tasks. In the interactive task, DualFlow (Both) achieves the highest R-Precision @ (0.513) and low- est MMDist (0.513), indicating strong alignment with multimodal inputs. DualFlow (Text) records the best Beat-Align Score (BAS) 0.215. While InterGen (Text) attains the best FID (0.405) and Diversity (1.405), DualFlow (Both) follows closely with FID of 0.415 and Diversity score of 1.307. For the reactive task, DualFllw (Both) leads all R-Precision scores, FID (0. (0.686), MMDist (1.056), and shows strong BAS (0.228). Although DuoLando (Both) has slightly higher BED (0.395), DualFlow remains competitive at 0.215.
Table 1: Duet Generation results MDD dataset with both text and music modalities. Bold for best, underline for second best. Methods
R-Precisiont
FID,
MMDist,
Diversity
MModalf
BED
BAST
Top
Top 2
Top
Ground Truth
0.231
0.398
0.522
0.065
0.077
1.387
0.327
0.170
Duet Task
MDM(Text)
0.082
0.124
0.192
1,420
2.133
1,216
0.811
0.211
0.186
MDM(Music)
0.041
0.102
0.135
2.241
2.471
1.192
0.411
0.210
0.192
MDM(Both)
0,061
0.108
0.163
1.739
2.244
1.235
0.787
0.194
0.190
InterGen(Text)
0.113
0.223
0.305
0.405
1.462
1,405
1,231
0.422
0.194
InterGen(Music)
0.023
0.067
0.088
2.014
2.526
1.300
1.768
0.364
0.163
InterGen(Both)
0.105
0.206
0.302
0.426
1.532
1.380
1.352
0.385
0.185
(TelFl
0.211
0.365
0.492
0.657
0.521
1.239
1.569
0.288
0.215
(uusii
0.172
0.308
0.452
1.244
1.319
1.109
0.308
0.180
(Bath)ow(Bath)
0.185
0.373
0,513
0.415
0.513
1.392
1.467
0.286
0.179
Reactive Task
0.047
0.121
0.182
1.538
2,811
1.422
0.311
0.195
DuoLando(Music)
0.069
0.141
0.202
0.721
2,633
1.390
0.305
0.216
QuoLando(Both)
0.078
0.156
0.219
0.698
2.113
1.371
0.395
0.224
DualFlow(Text)
0.143
0.284
0.450
0.741
1.365
1.379
1.667
0.229
0.228
Dusie)ow(Music)
0.135
0.260
0.397
0.750
1.672
1,460
1.976
0.195
0.202
(Both)ow(Both)
0.189
0.341
0.471
0.686
1.056
1.203
1.473
0.215
0.226
Text-conditioned Motion Generation on InterHunan-AS, Table[]shows DualFlow significantly outperforms InterGen on R-Precision (Top-1: 0.437, Top-3: 0.68T), with much lower MMDist (0.394) and the highest multimodality score (2.729). While InterGen has slightly better FID (5.918 vs. 6.296), DualFlow offers better semantic and multimodal alignment. In the reactive task, we train model with L=0 removing access actor's intention (completely causal) defined Uncon- strained (UC) for fair comparison with ReGenNet(UC)) DualFlow(UC) surpasses ReGenNet(UC) in R- R-Precision@3 @3 (0.572 vs. 0.407), MMDist (6.314 vs. 6.860), Diversity (5.449 vs. 5.214) and Multimodality (2.502 vs. 2.391).
Reactive Motion Generation on DD100. Table highlights DualFlow's performance across all metrics for reactive motion task. It achieves the best FID; (19.22). FID, (28.85), and FID (5.57), with strong diversity and rhythmic scores (Div;: 11.01, BAS: 0.211). While Duolando leads in BED (0.285), DualFlow follows closely at 0.276, showing generative fidelity and collaborative modeling.
Computational Complexity. Figure reports FID as a function of inference steps for DualFlow and InterGen. While InterCien requires more than 50 DDIM steps to reach high-quality performance, DualFlow achieves better FID with only 20 Rectified Flow (RF) steps. For a 10-second sequence at 30 FPS, the Average Inference Time per Sentence (AITS) an RTX 5090 GPU is 1.92s for InterGen (50 DDIM steps) and 1.24s for DualFlow (20 RF steps), demonstrating improved efficiency under identical hardware and sequence length.
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Interactive Two-person Generation results condi- tioned on text modality for the InterHuman-AS dataset.
Table 2:
Figure 3: User study results
User Study
Methods
R-Precisionf
FID
MMDist
Divere++
MModal
Top 1
Top
Top
80
Ground Truth
0452
0.610
0.701
0.273
3,755
7,918
60
Duet Task
0.515
5,108
7.387
40
DuslFllw
0.437
0.55%
0.681
79796
4.394
7,116
2.729
20
Le))
0.402
2.265
6,860
5.214
2.391
DualFlow(UC)
0.381
0.493
0.572
2,581
6.314
5.449
2.502
OMBS VS.
Ours
Ours
Ours VS.
0.49
0.549
Table 3: Reactive Motion Generation results conditioned on text modality for the DD100 dataset.
Figure 4: FID vs. Steps
Solo Metries
Interactive Metries
Rhythmic
vs Inference Steps
Methods
FID,J
FID,I
Dive!
Diy,
FID.al
Div.a"
BED(1)
BAS(()
Ground Truth
6.56
6.37
11.31
7.61
3:41
12.35
0,5308
0,1839
Bailando
78,52
36.19
11.15
7.92
6643.31
$2.50
0.1831
0.1930
EDGE
69.14
44.58
8.62
6.35
$894.45
60.62
0.1822
0,1875
Doolando
25.30
33.52
10.92
7.97
9.97
14.02
0.2858
0.2046
DualFlow
19.22
28.85
11.01
7.35
5.57
19.52
0.2767
0.2113
4.2 QUALITATIVE EVALUATION
Fig. 5 shows Qualitative Comparison for two samples from MDD Dataset. While samples gen- erated from both text and music condition maasd InterGen and DualFlow models follow the text prompt, the motion quality of InterGen has reduced motion quality circled, where the hands are flipping and the distance is increased. We also conduct a user study to qualitatively evaluate the motion sequences generated by our DualFlow framework in comparison with baseline methods on both tasks from the MDD dataset (details in Appendix). As shown in Fig 3 DualFlow outperforms the baseline methods across most comparisons, demonstrating superior alignment with both text and music, as well as high-quality motion generation.
4.3 ABLATION STUDY
We perform an ablation study on both the tasks (Table 4 to assess the impact of key DualFlow com- ponents. We compare the full model against four variants: (1) replacing Causal Look-Ahead (CLA) Attention with regular cross-attention (only for reactive setting). (2) removing RAG by replacing Retrieved Causal Attention with self-attention, (3) removing the triplet loss Liriplet, and (4) substi- tuting high-level Jukebox features with Mel-spectrograms. Results show clear performance drops across most metries, highlighting the importance of anticipatory modeling, retrieval grounding, and rich audio features for high-quality reactive motion generation. Please refer to Appendix for more ablation results.
CONCLUSION
We introduced DualFlow, a unified rectified flow-based framework for efficient and expressive two- person 3D motion generation, supporting both interactive and reactive settings with text, music, and retrieved motion exemplars. Leveraging rectified flow enables faster sampling and lower latency than diffusion- based methods. Extensive evaluations MDD, InterHuman-AS. and DD100 show superior performance in duet generation and reactive motion. DualFlow advances multi-modal two- person motion synthesis, opening new opportunities for immersive avatar interaction, intelligent

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Published as a conference paper at ICLR 2026
Interactive Motion Generation
Reactive Motion Generation
Ground Truth
Ground Truth
InterGen
Duolando
DualFlow
DualFlow
in dioies system, quicing the parillal syslam crosk.
Start in Fan Posilion, transitoring into Alemana, fallowed New Yorkers.
Figure 5: Comparing DualFlow with InterGen (interactive) and DuoLando (reactive) against ground truth MDD Dataset. Black circles mark regions where baselines lose contact produce dis- tortions. InterGen shows artifacts like unnatural hand spacing, body interpenetration. and skipping the Alemana (follower's inside turn), while DuoLando shows incorrect leg initiation and head oni- entation. In contrast, DualFlow generates smooth, text-aligned choreography and coherent partner responses closely matching the ground truth. Supplementary video provides detailed visualizations.
Table Ablation Study on MDD dataset (both text & music).
Methods
R-Precision?
FIDJ
MMDist,
Diverse
MModalf
BED
BASt
Top
Top 2
Top 3
Ground Truth
0.231
0.398
0.522
0.065
0.077
1.387
0.327
0.170
Interactive Task
(wwlFoow((w/o RAG)
0.179
0.356
0.498
0.622
0.626
1.502
1.224
0.254
0.162
DualFlow(wlo Ciplee)
0.158
0.297
0.412
0.783
0.818
1.433
0.844
0,291
0.169
DualFlow w/o Cyyne)
0.182
0.369
0.509
0.472
0.590
1.224
1.340
0,277
SaalFlow(Spectrae
0.172
0.321
0,477
0.647
0.633
1,383
1.114
0.255
DualFlow(Jukeboss
0.185
0.373
0.513
0.415
0.513
L,392
1.467
0.286
0.179
Reactive Task
Flo/ CL.A)
0.172
0.31
0.338
0.849
0.831
1.137
1.385
0.247
0.142
Dow//ooo((woo RAG)
0.192
0.352
0.479
0.714
0.933
1.270
1.466
0.233
0.193
DualFlow(wi) (wo Eripte)
0.153
0.292
0.308
0.885
1.328
1.664
1.007
0,204
0.186
DualFlow Lyyc)
0.166
0.311
0.453
0.774
1.112
1.429
1.233
0.235
0.202
DualFlow (Spectrl)
0.162
0.301
0.468
0.721
0.965
1.261
1.401
0.255
0.162
(uklFlowwJukeboo)
0.189
0.341
0.471
0.686
1.203
1.473
0.215
0.226
choreography, and responsive digital humans. Future work will explore improved interactive gener- ation with newer flow-matching methods, real-time motion editing, and few-shot adaptation to novel styles and languages.
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A LLM DISCLOSURE
LLMs were only used to polish the text and proof read the paper for grammatical emros. They were not used to generate any metries or citations.
B REPRODUCIBILITY
Full code for this project along with the trained checkpoints for all tasks will be made open source and publicly available upon paper acceptance.
C LLM-BASED DECOMPOSITION
C.I PROMPT DESIGN
We design structured prompting framework for the L.M,, which is detailed as follows:
1. System prompt:
We instruct the model with the following directive:
"As professional dance movement analyst, please break down the given textual descrip- tion of a duet dancing movement for {genre} into three focused descriptions: (1) Spatial Relationships: physical positioning, orientation, handhold (2) Body Movement: key ges- ures, actions, specific body part movements (3) Rhythm: tempo, timing, rhythmic dancing style and stepping. Please refer to the provided documents for guidance."
2. Few-shot Examples: We provide curated set of genre-specific examples (3 per genre) illustrating how input descriptions are manually decomposed into the three components. These examples crafted by analyzing diverse subset of textual annotations in the MDD dataset and annotating their corresponding focused descriptions through expert re- view.
3. Reference Guidelines: To promote interpretive consistency, supply supporting doc- ument containing structured definitions and keyword clusters describing typical language and semantic categories associated with each duet motion aspect.
C.2 GENERATED FOCUSED DESCRIPTIONS
To enhance semantic grounding during retrieval, we leverage Large Language Model (LLM) to decompose free-form textual prompts into structured, movement-relevant subcomponents. Drawing inspiration from Laban Movement Analysis (LMA),, we extract three focused descriptions: Spatial Relationship, Body Movement, and Rhythum. This decomposition allows the system to perform more targeted motion retrieval by aligning each aspect of the prompt with corresponding motion features. By translating ambiguous abstract user descriptions into focused representations, the objective for the LLM- based refinement to improve both retrieval precision and downstream motion generation quality. Table shows some of the examples for the focused textual descriptions for text prompts for the MDD Dataset.
C.3 VALIDATION OF BAM-BASED SEMANTIC DECOMPOSITION
To verify that the LLM-generated spatial, body-movement, and rhythm descriptors accurately re- flect the original numan-writter annotations, we randomly sampled 30 descriptions from MDD and InterHuman-AA and manually compared each decomposed attribute against the ground-trutt text. Using our tuned GPT-4o prompt (Section C. two annotators independently evaluated consistency, correctness, and completeness, scoring each attribute on 5-point scale (1 = incorrect, 5 = fully correct) based on consistency, correctness, and completeness. The decompositions showed high fi- delity to the original descriptions, with accuracies of 96.1%% for spatial relationships, 98.3% for body movement, and 86.9% for rhythm (overall 93.8%). We observed that in a few cases the LLM intro- duced thythm-related terms not explicitly present in the original text leading to lower validation accuracy. However, the use of music-derived features in our RAG module can help in naturally cor- recting such deviations by grounding rhythmic information. Overall, the results confirm that the
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LLM reliably produces semantically aligned decompositions suitable for guiding retrieval in the RAG module.
Table 5: Examples of input text decomposed into three fine-grained, semantically focused descrip-
tions using LLM for MDD Dataset.
Text Description
Spatial Relationship dancers are in an Open position with Hand-to- hand connection. The leader switches the hand hold from left to right, maintaining a strong frame. They are fac- ing each other during the transition.
Body Movement
Rhythm The movement is execuled fast tempo. with the triple spin occurring in quick succession, maintaining a continuous flow.
The leader switches hand hold from left right. lead- ing the follower into triple spin, maintaining a strong frame and connection.
The leader uses strong frame guide the follower into triple spin. The fol- lower's arms und torso are actively involved in the spin- ning motion. with medium energy.
The dancers perform Jive S Spanish Arms, maintaining strong frame and connec- tion, with the follower exe- cuting a controlled turn.
The dancers are in a Closed position, facing cach other with a strong Hand-to- hand connection. leader maintains a firm frume, guiding the follower through the movement.
The leader maintains steady posture, using arms and shoulders The follower performs controlled turn, involving smooth rotation of the torso and arms, with medium energy.
The movement is exccuted at fast tempo, characteris- of Jive, with continu- ous and lively rhythm, en- suring the turn is searneessl integrated into the dance se- quence.
guide.
The
From separated position. the leader draws the fol- lower into Clused Hand Hold, and they rotate clock- wise together.
The dancers transition from a sepurated position to a Closed position with a Hand-to-hand connection. They are facing each other they move into this position.
The leader initiates draw- ing motion, pulling the fol- lower lowards him. Both dancers engage in a rotat- ing movement, turning their bodics clockwise together.
The rotation is performed at medium tempo, with continuous and fluid motion as they move sync with each other.
The leader brings the fol- lower back with a circular motion, leading head roll with his left hand, connect- ing it with forward body roll for the follower. They then perform a basic step.
The dancers are in an Open position, with the leader fac- ing the follower. They main- tain Hand-to-head connec- tion as the leader guides the follower's 's head roll.
The leader uses his left hand to guide a head roll, involv- ing the follower's 's head and neck. The follower transi- tions into a forward body roll, engaging the shoulders and torso. Both then per- form basic step, involv- ing coordinated leg and foot movements.
The sequence begins with a medium- circular mo- tion. transitioning into fluid head and body roll. The basic step follows a stcady, continuous tempo, maintaining rhythmic con- sistency.
The lead pulls the follow towards him, taking three steps. while the follow also takes three steps towards the lead. Both hunds of both dancers are now connected.
The dancers are in a Closed position, facing each other. They huve Hand-to-hand connection with both hands engaged.
The lend and follow are both taking three steps towards each other. The movement involves the legs and feet. with a medium energy as they close the distance.
The steps are laken at medium tempo, with each step evenly spaced, creating continuous and synchro- nized rhythm between the dancers.
D MODEL ARCHITECTURE DETAILS
The proposed framework for duet and reactive motion generation employs a rectified flow matching approach. Our model utilizes transformer-base architectures with multi-scale temporal modeling and attention mechanisms, supporting optional text and music conditioning. The following section discusses about specific modules used in detail.
D.1 DUALFLOW BLOCK.
The DualFlow block applies multi-scale temporal convolutions with learnable gating:
3
r GHLL (T) e (1,2,3)
(5)) (1) + nh (1)
2
Each block applies a sequence of self- and cross-attention layers with residual connections and LayerNorm conditioning using the text latent zd- Let IN(., 24) denote LayerNorm with text-
QK
conditioned shift/scale, and Attn K. V) softmax Va V. The transformations applied are
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Self- Attentionn (equation Music Cross Attention (equation 12
Motion Cross Attention (equa-
tion 13 Retrieval Cross Attention (equation 14), and Feedforward (FFN) Layer (equation 15
20
10 + Altn( Q = WQ LN(2) (111
(Z)) za), K
W LN(2)) za), V
WV
(2
za)
(11)
20 (1.2)
za (1.1)) Attn - 2 LN (1,1), .zz),,
K .za)
W Zmn
WV Zm
(12)
2 2(130
(122) + Attn Q = a LN (1:2) Za), K K= W (1:2)
V = W z (12)))
(13)
2 (344)
Via
(j.3)
+ Altu Q W LN (Z. (1:3) 2a), K= W 2n. V = WV ZR)
(14)
2/ (j+1) = 2.1444) FFNN(L za)).
(15)
with symmetric updates for 2
D.2 INTERACTIVE SETTING
The flow dynamics are defined as:
x(t) xx (t); va(x(t), t, (x(,,x((), c); (o),
The final motion latents z and 2
(N) are mapped to velocity fields
Vo.a Linear(z(
Vo,b = Lincar(z, (NN))
(16)
concatenated
Vo Vo,a) E Bxxxx521
(17)
D.3
REACTIVE SETTING
For reactive motion generation, model generates the reactor's motion x conditioned on the actor's 's fixed motion xa, with the flow dynamics defined as:
x(t) [x.] x)(),, va(x(1), = [O; Vi, (,),,,
The Motion Cross Attention gets replaced by Causal Cross Attention in the DualFlow block for this
262 setting. The final reactor latent 2/ (N) is mapped the velocity field Vo,reactor Linear L (2, (N)). and
the output is Vg = 00; Vo,renctor] R BxTx521 During inference, the initial state x0) [Xai /x]] where 2₁ ~ I).
D.4 CAUSAL CROSS ATTENTION WITH LOOK-AHEAN
The Causal Cross Attention module enables the reactor to condition on the actor's motion while preserving temporal causality and allowing limited future anticipation. For reactor motion latent 26 (1.2) and fixed actor motion latent 2, from DualFlow block j, construct query, key, and value matrices as Q = zi (j.2) WQ. K = W and V Zu Wv, where We. WK. and Wv e Lxdd, are learned projection matrices. The causal mask with look-ahead parameter L uses an upper triangular mask such that reactor's motion attends to past and only L future frames of the actor's 's motion, implemented as M.j = 1 if j s i L and M.,, = 0 otherwise. The attention computation follows:
CausalCrossAttenntion Q K, V) = softmax
(QK" Vd
O M (1 M) (-o0)
(-oo)) v
where denotes element- wise multiplication. This formulation ensures temporally aligned and context-aware reactive generation, enabling natural reactive responses that align with the actor's intended trajectory without violating temporal consistency.
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D.5 MODEL PARAMETERS
Loss Weighting Values We assign higher weights to geometric losses for velocity (Avel 30) and foot contact (Aioa 30), moderate weight for bone length consistency (ABL. = 10), and emphasize inter-dancer synchronization (Asyne 5). Affinity and distance are equally weighted (ADM 3), while orientation receives minimal weight (ARO 0.01). These settings ensure anatomically plausible, temporally smooth, and well-coordinated duet motions.
E QUANTITATIVE EVALUATION
We further conduct ablations to study model design choices in Table. 6 (I) replacing the three temporally scaled parallel convolutions with a single convolution, (2) reducing the number of trans- former blocks 10 and 15 (from 20), (3) lowering the latent dimension to 128 and 256 (from 1024) and (4) changing the Look-Ahead parameter to and 20. These variants consistently show per- formance drops across most metrics, highlighting the benefit of the full architecture. Performance decrease in different settings shows the importance of parallel temporal Convs, using 20 blocks, 515 Latent dimension and Look-Ahead parameter I. = 10 frames. Here, Bold indicates the best result and Underline indicates the second best result.
Table 6: Ablation study results for Reactive Setting the MDD dataset
Methods
R- R-Precision
FID,
MMDist
Diversity
MModal
BED
BAST
Top
Top 2
Top 3
Ground Truth
0.231
0.398
0.522
0.065
0.077
1,387
0.327
0.170
DualFlow (one conv)
0.172
0.31
0.338
0.595
0.582
1.288
1.385
0.266
0.142
DualFlow (10 blocks)
0.160
0.313
0.452
0.683
0.654
1.215
1.222
0.259
0.159
DualFlow (15 blocks)
0.175
0.357
0,521
0.482
0.627
1.211
1.402
0,270
0.163
DualFlow (128 latent)
0.108
0.284
0.414
0.966
0,834
1.277
1.091
0.273
0.141
Flal (256 latent)
0.168
0.342
0.468
0.642
0.681
1.245
1.328
0.291
0.163
DualFlow (L=0)
0.162
0.322
0.455
0.574
0.663
1.292
1.274
0.241
0.152
DualFlow (L=20)
0.181
0.366
0.507
0.497
0.542
1.322
0.258
0.167
DualFlow
0.185
0.373
0.513
0,415
0.513
1.307
1.467
0.286
0.179
Ablation for RAG. We also perform ablations to critically evaluate the role of searieval-augmented components across both the settings driving DualFlow's 's performance in Table. 7 For the cases where different retrieval components are ablated, value of k is set to be 5. For text-decompose setting of RAG, we directly perform retrieval on original text descriptions and music features in order to understand the benefit from text decomposition.
In the interactive setting, removing any individual retricval cue consistently degrades semantic align- ment and motion quality, with the largest drops observed when all retrieval components are removed. Increasing the number of retrieved samples shows a clear sweet spot where k == achieves the best R- Precision, FID, and Multi-modality scores, indicating that moderately diverse retrieved context helps the model ground its generation without introducing noise. Interestingly, k= 3 already provides substantial boost over no retrieval, but larger retrieval depth 7) offers diminishing returns and slightly worse fidelity, suggesting over-saturation of context. Using no textual decomposition set- ting provides similar results removing Music-based retrieval but having retrieval on decomposed text components.
In contrast, the reactive setting exhibits different trend. Because the follower must respond tightly to the leader's 's motion in real time, excessive retrieval diversity can introduce temporal drift. It can be seen that k= provides the strongest semantic alignment, outperforming both lower (k = 1) and higher (k= 5,7) retrieval depths. Additionally, removing music-based retrieval surprisingly improves R- R-Precision and MM-Distan Distance, suggesting that in tightly synchronized partner interactions, leader motion cues dominate
rhythmic cues for determining the follower's behavior. Using textual decomposition RAG setting performs better than text-retrieval ablated version but performs more comparable text rhythm component ablated version.
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Table Ablation Study on RAG in DualFlow on the MDD dataset
Methods
R-Precision
FIDI
MMDist)
Diverse-
MModalf
BED
BAST
Top
Top 2
Top
Ground Truth
0.231
0.398
0.522
0.077
1.387
0.170
Interactive Task
w/o RAG (R,". .R. RRf R.
0.179
0.356
0.498
0.622
0.626
1.502
1.224
0.254
0.162
w/o Text-based Retrieval (R R,",, R()
0.181
0.36]
0.503
0.541
0.574
1.441
1.351
0.263
0.171
wla 185
wlo R.
0.182
0,364
0.506
0.520
0.559
1.422
1.419
0.272
0.172
w/o R!
0.181
0.362
0,504
0.512
0.553
1.416
1.441
0.267
0.177
w/o Music-based Retrieval (R.")
0.183
0.368
0,509
0.498
0,541
1.406
1.452
0.268
0.164
RAG (no text-dcoopose)
0.183
0.352
0,501
0.508
0,552
1,409
1.444
0.287
0.178
w RAG (k=1)
0,181
0,360
0.503
0.449
0.535
1,381
1,437
0.279
0.176
RAG (k=3)
0.184
1.386
0.291
0.178
w RAG (k=5)
0.185
0.373
0,513
0.415
0.513
1.392
1.467
0.286
0.179
RAG (k=7)
0.183
0.369
Reactive Task
w/o RAG (RR RR R R.M)
0.192
0.352
0.479
0.714
0.933
1.270
1,466
0.233
0.193
wll Text based Retrieval (nt. a,, n())
0,181
0.334
0.451
0.752
0.984
1.196
1.312
0.221
0.217
w/o R-
0.182
1.243
0.246
0.224
w/o R
0.182
0.322
0,451
0.699
0,948
1,255
1.442
0.239
0.198
wla R.
0,186
0.334
0.468
0.697
0.932
1.249
1.451
0.231
0,208
w/o Music-hased Retrieval (RM)
w RAG (no text decompose)
0.185
0,336
0,473
0.696
0,933
1.252
1.442
0.221
0.208
w RAG (k=1)
0,190
0.348
0.457
0.707
0.978
1,223
1.469
0.221
0.209
RAG (k=3)
0,19%
0,367
0.483
0.693
0,962
1.217
1.471
0.224
0.212
w RAG (k=5)
189
0,341
0.471
0.686
1,056
1.203
1.473
0.215
0.226
RAG (k=7)
0.188
0.336
0.459
0.699
0.989
1.229
1.470
0.218
0.223
Table 8: Ablation Study on Synchronization Loss on the MDD dataset.
Methods
R-Precision?
FIDI
MMDist,
Diverse->
MModalf
BED 1 BAS
Top
Top 2
Top
Ground Truth
0.231
0.398
0.522
0.065
0.077
1.387
0.327
0.170
Interactive Task
DualFlow(wfu Coyoc)
0.182
0.369
0.509
0.472
0.590
1.224
1.340
0.277
0.182
DualFlow(w Cyyn w/o un))
0.181
0.365
0.502
0.465
0.592
1.318
1.322
0.268
0.163
DualFlow(w Lyne w/o wy))
0.184
0.372
0.511
0.432
0.538
1.385
1.435
0.292
0.180
DualFlow Lxyr))
0.185
0.373
0.513
0.415
0.513
1.392
1.467
0.286
0.179
Reactive Task
DualFlow (w/) Coyne)
0.166
0.311
0.453
1.112
1.429
1.233
0.235
0.202
DualFlow(w Coyne w/o wag)
0.168
0.314
0.459
0.763
1.101
1.381
1.260
0.231
0.194
DualFlow(w Cyyne w/o my))
0.181
0.334
0.467
0.712
1.064
1.312
1.431
0.212
0.214
DualFlow
0.189
0.341
0.471
0.686
1.056
1.203
1.473
0.215
0.226
Ablation on Synchronization Loss. Table. shows further ablation analysis on the proposed Syn- chronization Loss. It can be seen that having Lsyne plays crucial role in improving both semantic alignment and inter-person coordination for duet motion generation. Removing the loss entirely leads clear degradation across all metrics in both interactive and reactive settings, with notably higher FID & MMDist and reduced R-Precision. The distance weighting term wa and the anatomi- cal weighting term w/ contribute complementary benefits. Omitting wa harms spatial coherence and leads to greater overall performance degradation, whereas omitting wy primarily reduces semantic consistency and relational fidelity reflected in lower BED, BAS, and MModal, and thus performs slightly worse than the complete version. The full formulation consistently achieves the strongest performance, yielding the best balance of retrieval alignment (R-Precision), motion realism (FID), Diversity, Multimodality, and inter-person synchronization. These results validate that both weight- ing components are necessary and that Lsyne meaningfully strengthens DualFlow's ability to model coordinated two-person motion.
Model Parameters Comparison. The adapted InterGen moddel-augmentrr with an additional music-attention layer to support both motion and music conditioning ceaattioinnn-cooniinss 224M trainable pa- rameters. InterGen's architecture packs two sub-blocks (each comprising two attention layers) into single block, yielding total of blocks, i.e., 8 x 2 sub-blocks x attention layers per sub-
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block (after adding music attention). resulting in 48 attention layers overall. In contrast, DualFlow employs 20 blocks, each containing four attention layers, amounting to 80 attention layers and a total of 456M trainable parameters. The increased capacity in DualFlow primarily arises from the added retrieeal-augmented generation (RAG) module, which introduces additional attention layers and projection components necessary for multi-moda retrieval integration.
F QUALITATIVE EVALUATION
User Study Details. A total of 24 participants were recruited for the study. Each participant is shown 15 pairs of rendered videos (3 per experiment), with each video lasting less than 10 seconds. Each pair consists of one motion sequence generated by DualFlow and the other by either a baseline method the ground truth (when available). To ensure unbiased evaluation, the order of videos within each pair is randomized, and no method labels are revealed. For each video pair, participants are asked answer three key questions: (I) Which motion better aligns semantically with the textual description? ? (2) Which motion is better synchronized with the musical beats? (3) Which motion has higher overall quality (e.g., naturalness, smoothness etc)? Fig 6 shows the User Study Form we
used.
Fig. 6 Gillustrates the User Study Form presented to participants during the human evaluation study. Clear and detailed guidelines were provided at the beginning of the form, explaining the evaluation criteria. Participants were then asked to watch two videos: one containing motion from either a Baseline model the Ground Truth, and the other generated using our DualFlow model. The identity of each video (i.e., whether was from the DualFlow model or the comparison method) was not disclosed to the participants. For each experimental condition, participants viewed and evaluated three distinct pairs of videos.
User Study For DualFlow Motion Generation
Dear Patiiciann,
you reding particppate stardy!
In thes uul you will be shown two motion sequenees for each oom con Your to evalutte these euquences by anewengg three questions bapes on defiment sepects of mot on quaitty The sequenoes are brrm 3 ofterent experments namreyy Text Alignmeet IT, Musical Synchranbationn [M] Dveral Mutton Quality kill You'il be dhown samplee from eech iepermennt
11. Which motion more accurately reffects the meening of aocompanying tostul deseription?
follncing
you
Sequancer
mreening
discciptices
Sequence
The motion cloody reflectu the actiona emodions, scanario io describod.
The mozning disarly communkcnted thoough the body movements.
The motion commanually approp fore and logisally consistort with description.
Mi. Which mation butter synclroriaed dhydim backgiound music?
Musical Synohoriizzdor: ofthe ttheimusie?
symchrostzre
rivthin
Key movements occur in syne muniall bents and sccento.
Sequence
The nythem and pecing of the motion match the tempo of the munoo motion expressee changes mussccll energy, such intersity
Soquence
mood
Quemi Mallen qualits Which masion looke mewe catursi and whondly piewning revertt
Trenebiore betowenn posse are smmoch and continuman.
motion tooks more naturol wasesty pleasing
Move mente lollow realsticc bei wembie tejectorien.
motion vinualy coherent, expressive, and mecthetccall pheasing
Sequence 1
Ploase anawes Brougl ifully based on your perception, Your evalustiona will be valuablle rewarec.
Sequaree22
you ogain your partcipation
Figure 6: User Study Google Form
G LIMITATIONS AND FUTURE WORK
In this section, we discuss the limitations of the DualFlow model along with several observed failure cases followed by potential avenues for improvement. (1) The effectiveness of RAG-based mo-
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tion alignment is dependent the quality and relevance of the retrieved samples. In cases where the input text, leader motion, or music cues are ambiguous or underspecified, the RAG module may retrieve semantically mismatched neighbors. This semantic retrieval misalignment can stylistic drift or generate motions that deviate from the intended interaction attributes, particularly for prompts involving abstract descriptions uncommon dance style/movement. (2) In the active setting, DualFlow occasionally struggles to maintain precise physical coordination between partners. We observe minor hand-hand or torso-torso penetrations during close-contact sequences or under rapid leader movements, likely due to the absence of explicit modeling of contact-based physical constraints. (3) Since retrieval operates over short, localized motion segments, directly gen- erating long sequences can accumulate temporal drift, leading weakened structural consistency or off-beat rhythmic alignment over extended durations.
The above limitations point to several promising directions for future work. Improving retrieval quality through learned semantic re-ranking, cross-modal retrieval scoring, or uncertainty-awart retrieval could reduce misalignment and make the system more robust ambiguous input cues. Incorporating contact-based physical constraints as loss function may help enforce more accu- rate hand and body coordination in close-contact motions. Finally, addressing long-term drift may benefit from introducing hierarchical temporal modeling, where high-level rhythmic or structural constraints guide long-range consistency, while DualFlow refines short-term details. Broadening the retrieval corpus to incorporate more diverse styles and partner interaction patterns may further enhance robustness. Together, these directions offer path toward more physically grounded, mantically aligned and temporally coherent two-person motion generation.
20


Reviews:
[{"review_id": "LxbW1C0ZU6", "reviewer": "Reviewer_Wd16", "summary": "This paper introduces a novel framework DualFlow for synchronized two-persoon 3D motion generation. DualFlow unifies the tasks of interactive motion generation and reactive motion generation within a single rectified flow framework and a Retrieval-Augmented Generation module for semantic grounding from text and music. The authors also adopt contrastive learning for better motion quality and inter-person coordination. Experiments show the effectiveness of the proposed frame.", "questions": "Refer to weaknesses, and it would be better to show some failure cases.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The unified transformer-based architecture for both interactive and reactive two-person motion generation is practical and novel.\n\n2. The RAG module, guided by LLM-decomposed text cues (spatial relationship, body movement, rhythm) and music features, provides semantically rich conditioning that is well-motivated and improves generation quality and diversity. \nIt's a good application. \n\n3. The adoption of Rectified Flow Matching is technically sound and is more effective than diffusion.\n\n4. The paper is well-written and clearly structured. The architecture and methodology are described in sufficient detail, it would be reproducible if the codes and data were released.\n\n5. The experiments are comprehensive on multiple two-person motion datasets (MDD, InterHuman-AS, DD100) and show better performance than baselines. The user study is helpful.", "weaknesses": "1. The overall ablation over RAG is limited, and the effectiveness of each retrieved feature (RS, RB, RR, RM) should be more precisely evaluated. \n\n2. How do you set the Look-Ahead parameter L? There should be an ablation study to evaluate the effectiveness of L, and analyze how this parameter affects the performance of reactive motion generation.", "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": "dgO73tM3PN", "reviewer": "Reviewer_VZU8", "summary": "This paper proposes DualFlow, a unified rectified flow-based framework for multi-modal 3D two-person motion generation, which supports both interactive and reactive motion generation tasks and can take text, music, and prior motion sequences as input conditions. Experiments demonstrate that DualFlow performes well on the MDD, InterHuman-AS, and DD100 datasets.", "questions": "1. In Table 4, the Top3 of DualFlow (Spectral) is 477,which may be wrong.\n2. The quantitative results of ReGenNet in Table 2 is under unconstrained setting, i.e., to generate instant reactions without knowing the intention of the actors. Thus the comparison of ReGenNet is not fair.\n3. Two reference papers are not existed. It is recommended that the authors carefully review the entire manuscript to ensure correctness.\n(1) Yuxuan Cao, Jiawei Ren, et al. Diffmotion: Conditional motion generation with diffusion models. In CVPR, 2023.\n(2) Yilun Duan, Joshua B Tenenbaum, et al. Multi-modal motion generation for embodied ai via transformers. arXiv preprint arXiv:2204.08718, 2022.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "DualFlow seamlessly integrates interactive and reactive two-person 3D motion generation. The incorporation of Retrieval-Augmented Generation (RAG) with music features and LLM-based text decomposition effectively strengthens semantic grounding. Besides, leveraging contrastive flow matching improves motion quality, diversity, and alignment with conditioning signals.", "weaknesses": "1. The motivation is weak. Two-person motion generation is not a new task. But the author emphasize the limitations of single-person motion generation methods. \n2. The technical contributions are limited. I didn't find anything that really impressed me in this article. This paper is a combination of RAG, Contrastive Flow Matching. The authors are advised to claim why using RAG and what advantages RAG has in practical applications, since the motion generation doesn't have a large source of corpus that can be retrieved from.", "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": "bpjRJckHlb", "reviewer": "Reviewer_sChN", "summary": "This paper proposes a framework for human-human interaction/reaction generation conditioned on text descriptions and music. The framework integrates multiple existing techniques, such as Rectified Flow and Retrieval-Augmented Generation. It demonstrates notable improvements over baselines across multiple datasets.", "questions": "1. Could you provide more details on how the positive and negative velocity samples are obtained?\n\n2. InterGen [7] uses 8 blocks with 2 attention layers per block, while the proposed method uses 20 blocks with 4 attention layers per block. Does this imply that the proposed method has significantly more parameters than InterGen? Please provide a comparison of parameter counts.\n\n\n\n**References:**\n\n[1] Tevet G, Raab S, Gordon B, et al. Human motion diffusion model[J]. arXiv preprint arXiv:2209.14916, 2022.\n\n[2] Dai W, Chen L H, Wang J, et al. Motionlcm: Real-time controllable motion generation via latent consistency model[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024: 390-408.\n\n[3] Huang Y, Yang H, Luo C, et al. Stablemofusion: Towards robust and efficient diffusion-based motion generation framework[C]//Proceedings of the 32nd ACM International Conference on Multimedia. 2024: 224-232.\n\n[4] Hong S, Kim C, Yoon S, et al. SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 7158-7168.\n\n[5] Bae J, Hwang I, Lee Y Y, et al. Less is more: Improving motion diffusion models with sparse keyframes[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025: 11069-11078.\n\n[6] Wang Y, Wang S, Zhang J, et al. TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 7169-7178.\n\n[7] Liang H, Zhang W, Li W, et al. Intergen: Diffusion-based multi-human motion generation under complex interactions[J]. International Journal of Computer Vision, 2024, 132(9): 3463-3483.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The figures and tables are clear and visually appealing, with an overall well-structured and comprehensible presentation.\n\n2. The paper presents an engineering system of considerable complexity, integrating multiple relevant techniques.\n\n3. The quantitative improvements over baselines are substantial.", "weaknesses": "1. **Outdated motivation regarding computational efficiency.** The paper claims that diffusion-based approaches like Motion Diffusion Model (MDM) [1] \"remain limited by high computational cost\" and \"weak real-time reactivity\" (Lines 55-57), and \"require hundreds of denoising steps, making real-time use impractical\" (Line 107). The proposed method, built on Rectified Flow, requires 200 steps to demonstrate efficiency. However, as early as 2024, the authors of MDM released MDM-50steps in their GitHub repository, which requires only 50 denoising steps without degrading FID and other metrics. Current state-of-the-art models commonly use 50 or fewer denoising steps, such as [2,3,4,5,6]. This significantly weakens the claimed advantage of the proposed approach.\n\n2. **Insufficient motivation for adopting Rectified Flow.** A main contribution of the paper is the introduction of Rectified Flow, motivated by the claim that diffusion-based approaches have \"constraints [that] hinder their applicability in interactive systems such as virtual avatars, social robotics, and dance simulation, where low-latency and seamless coordination are essential.\" Given the point above, this motivation appears questionable and may not hold.\n\n3. **Lack of rigorous quantitative evidence for efficiency claims.** In Section 4.2, the paper supports its claim of \"faster sampling and reduced latency\" primarily by showing fewer inference steps compared to InterGen. This is insufficient. Given differences in network architecture and parameter count, a more rigorous evaluation should include Average Inference Time per Sentence (AITS) [6] or similar metrics to substantiate the efficiency claims.\n\n4. **Limited validation and impact of RAG module.** The paper lacks validation of whether the LLM-generated descriptions (spatial relationship, body movement, rhythm) are consistent with actual motion content. Additionally, the ablation study (Table 4) shows that RAG provides only modest improvements, with some metrics in the reactive task showing marginal degradation. The practical value of this added complexity requires more thorough investigation.\n\n5. **Concerns about evaluation reliability.** The evaluators achieve only 0.231 R-Prec@1 on ground truth, which is relatively low. While the paper includes user studies, it would be beneficial to discuss the limitations of these automatic evaluators and why ground truth performance is weak, helping readers better interpret the reported improvements.\n\n6. **Limited methodological novelty.** The proposed framework is largely a combination of existing techniques. The key component (Rectified Flow) lacks strong motivation as discussed above, and the RAG module shows limited improvements. The paper would benefit from stronger empirical validation or clearer articulation of why this particular combination is necessary.", "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": "R4i18OSp2s", "reviewer": "Reviewer_9pa5", "summary": "This paper presents a unified framework for two-person motion generation from texts or/and music. DualFlow employs a retrieval-augmented generation module for semantic alignment. It also uses a contrastive rectified flow to achieve good sampling quality. Experiments on three datasets, InterHuman-AS, DD100, and MDD all shows DualFlow outperforms most baseline methods, especially in terms of the semantic alignment, showing the effectiveness of the proposed RAG, and contrastive learning object.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ A new SOTA performance is achieved with the proposed method, with cutting-edge techniques, including RAG, contrastive rectified flow. It will serve as a better and stronger baseline in the field of two-person motion generation.\n+ Experiments are extensive. Three benchmarks and ablation studies all shows the effectiveness of the proposed method.", "weaknesses": "- The paper looks very much like an \"updated and modern version\" of motion diffusion model, with some fancy new techniques from broader diffusion community to boost the performance, but is short of insights in motion generation itself, thus makes me feel lack of novelty.\n- I did not see many insights in the \"two-person\" motion generation. Joint modeling of two-person motions and the synchronization loss are pretty standard in two-person motion generation. RAG and contrastive rectified flow are very general method and can be applied on single-person motion generation.\n\nTypo:\n- Line 295: L_{sync} => L_{inter}", "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)