Deep Compositional Phase Diffusion for Long Motion Sequence Generation
Authors: Ho Yin Au
Jie Chen
Junkun Jiang
Jingyu Xiang
Venue: NeurIPS 2025 oral ()
Keywords: Motion Generation
Phase Autoencoder
Long Term Motion Sequence Generation
Motion Inbetweening
Primary area: deep_learning
TL;DR: The proposed Compositional Phase Diffusion framework consistently generates semantically aligned multi-clip motion with smooth transitions by using latent-phase diffusion modules (SPDM and TPDM) to preserve phase continuity and enable inbetweening.

Abstract: Recent research on motion generation has shown significant progress in generating semantically aligned motion with singular semantics. However, when employing these models to create composite sequences containing multiple semantically generated motion clips, they often struggle to preserve the continuity of motion dynamics at the transition boundaries between clips, resulting in awkward transitions and abrupt artifacts. To address these challenges, we present Compositional Phase Diffusion, which leverages the Semantic Phase Diffusion Module (SPDM) and Transitional Phase Diffusion Module (TPDM) to progressively incorporate semantic guidance and phase details from adjacent motion clips into the diffusion process. Specifically, SPDM and TPDM operate within the latent motion frequency domain established by the pre-trained Action-Centric Motion Phase Autoencoder (ACT-PAE). This allows them to learn semantically important and transition-aware phase information from variable-length motion clips during training. Experimental results demonstrate the competitive performance of our proposed framework in generating compositional motion sequences that align semantically with the input conditions, while preserving phase transitional continuity between preceding and succeeding motion clips. Additionally, motion inbetweening task is made possible by keeping the phase parameter of the input motion sequences fixed throughout the diffusion process, showcasing the potential for extending the proposed framework to accommodate various application scenarios. Codes are available at
https://github.com/asdryau/TransPhase.

Deep Compositional Phase Diffusion for Long Motion Sequence Generation
Ho Yin Au
Jie Chen"
Hong Kong Baptist University
Hong Kong Baptist University thenjie@comy comp. hkbu. edu.
cshyau@comp hkbu. edu. .kk
Junkun Jiang
Jingyu Xiang
Hong Kong Baptist University cs csjkjiang@coom hkbu.. eduu hk
Hong Kong Baptist University cs jejxing@ccomp hkbu. edu. hk
dodivii Hall
Crowingg forord 1541
TMUL Swwaat Gall
WWlik Frowort (MOT
(a) Compositioall Motion Generation
(b) Compositional Motion Inbetweening
Wall
Mrowheed [9v1]
[Lme (lc))
Long- lern Motion Generation
Figure 1: Our Compositional Phase Diffusion framework produces high-quality composite motion sequences with smooth transitions and semantic alignment. (a) Compositional generation involves synthesizing multiple motion segments varying lengths simultaneously. ensuring smooth transitions between segments. (b) Motion inbetweening allows users to select segments (in blue and yellow) and create conditional or unconditional bridging motions. (c) Long-term motion generation is achieved by scaling the framework with additional modules, enabling the parallel denoising of larger number of motion segments. The rainbow color indicates time progression.
Abstract
Recent research motion generation has shown significant progress in generating semantically aligned motion with singular semantics. However, when employ- ing these models to create composite sequences containing multiple semantically generated motion clips, they often struggle to preserve the continuity of motion dynamics the transition boundaries between clips, resulting in awkward transi- tions and abrupt artifacts. To address these challenges, we present Compositional Phase Diffusion, which leverages the Semantic Phase Diffusion Module (SPDM) and Transitional Phase Diffusion Module (TPDM) to progressively incorporate semantic guidance and phase details from adjacent motion clips into the diffusion process. Specifically, SPDM and TPDM operate within the latent motion frequency domain established by the pre-trained Action-Centric Motion Phase Autoencoder
"Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

(ACT-PAE). This allows them to learn semantically important and transition- aware phase information from variable-length motion clips during training. Experimental results demonstrate the competitive performance of our proposed framework in generating compositional motion sequences that align semantically with the input conditions, while preserving phase transitional continuity between preceding and succeeding motion clips. Additionally, motion inbetweening task is made possible by keeping the phase parameter of the input motion sequences fixed through- out the diffusion process, showcasing the potential for extending the proposed framework to accommodate various application scenarios. Codes are available at https://github.com/aaae www.wooowo///o//////../oo Saarddryyu//rassPPai
Introduction
Deep dearning-basel human motion generation holds significant potential for creating virtual hu- manoid animations and enhancing robotics applications. With more advanced modeling tech- niques and more motion data being captured S 7. motion generation models are evolving rapidly and can be adapted to a variety of multimodal generation tasks. For example, text-to-motion generation allows animators to produce character animations by specifying semantic handle variable-length motion segments with singular semantics. However, when these models are applied to long-term com- positional motion generation tasks, where they must generate K motion segments sequentially for K instructions, they often struggle with smooth transitions between segments. Recently, there has been growing focus long-term compositional generation tasks [12] [3] 14), driven by the availability of BABEL-TEACH 44 [2] dataset with text annotations for pairs of temporally connected motion segments, which aids motion models in learning transitions. Methods such priorMDM [13] use the learned transition knowledge to create transitional segments that smooth out pose differences between those generated by MDM [8) However, these approaches often overlook the intrinsic kinematics of each segment, resulting in artifacts like over-smoothing or abrupt stops in transitions.
contexts using text prompts. Current motion generation models |
6 9 10
To generate motion clips aligned with specific semantic contexts and ensure smooth transitions, we introduce the Compositional Phase Diffusion framework. This framework simultaneously creates multiple motion clips from sequential semantic instructions, using denoised information from adjacent clips to enhance transition compatibility. The Compositional Phase Diffusion framework consists of three main components: Action-Centric Periodic Autoencoder (ACT-PAE), Semantic Phase Diffusion Module (SPDM), and Transitional Phase Diffusion Module (TPDM). ACT-PAE, which builds upon DeepPhase [15]. encodes each variable-lenglo motion segment into unified phase manifold. SPDM and TPDM then iteratively denoise phase parameters by incorporating semantic instructions and neighbouring phase signals. This approach effectively models the intrinsic dynamics of each motion segment within the latent motion frequency domain, ensuring both semantic alignment and smooth transitions. Additionally, the framework is scalable, allowing for an arbitrary number of modules to denoise multiple motion segments in parallel. This capability highlights its flexibility and scalability in generating motion sequences of varying lengths and facilitating motion inbetweening tasks.
Extensive experimental results demonstrate that the Compositional Phase Diffusion framework excels in both long-term compositional motion generation and motion inbetweening tasks, attributed to the semantic and transition- aware diffusion process. The key contributions are as follows:
We introduce the Compositional Phase Diffusion framework, a scalable and efficient solution for various motion generation tasks. This framework can process an arbitrary number of motion segments of varying lengths simultaneously by leveraging parallel module execution, ensuring smooth and coherent transitions between clips.
Our framework incorporates three key components: the Action-Centric Periodic Autoen- coder (ACT-PAB), the Semantic Phase Diffusion Module (SPDM), and the Transitional Phase Diffusion Module (TPDM). By operating within unified phase latent space estab- lished by ACT-PAF, SPDM and TPDM collaboratively denoise motion representations while preserving semantic phase information and aligning transitional dynamics.
Extensive experiments validate the effectiveness of our framework, demonstrating significant improvements in long-term compositional motion generation and motion inbetweening tasks, showcasing its ability to produce high-quality, contextually relevant animations.

---

2 Related work
incorporate explicit phase inputs, such as foot contact during walking, to achieve smooth motion extrapolation and transition. DeepPhase [TS] further extends this concept by developing a Periodic Autoencoder (PAE) that encodes motion segments into phase latent parameters, i.e., frequency (F), amplitude (A), offset (B), and phase shift (S). These parameters help generate periodic motion patterns and smooth transitions, minimizing artifacts like over-smoothing and sudden stops. Building upon PAE, PhaseBetweener [19] and RSMT 20] tackle motion inbetweening tasks by autoregressively generating motion frames and phase parameters. Meanwhile, DiffusionPhase [21] adopts the MLD [22] framework to denoise the periodic latents based on input text and conditioned pose. However, the fixed-length convolution scheme of PAE leads to instability in training objectives, variable-length motions encoded into a varying number of phase latent codes.
Motion Phase Modeling. Pioneering approaches [6 17
Text-to-Motion Generation. Several methods have been utilized diffusion models 2 single text prompt, including MDM [8]. MLD [22]. MotionDiffuse [[] and Diffusion Phase [21]. Notably, MDM ( applies the Diffusion Model to raw pose sequences conditioned on text encoded by CLIP [23]. Building upon MDM, PhysDiff (24] and GMD (25] have been developed to enhance physical plausibility and trajectory control in the generated motion. However, due to the limited segment lengths of datasets like HumanMLL.D [6] and BABEL [4), with maximum frames of 196
3 with
and 250, respectively, these models struggle to generate longer motion sequences.
Learning-based Motion Inbetweening is achieved through two main approaches. 1) Autoregressive frame generation: Motion frames are sequentially generated to connect segment boundaries 27 Methods like DiffusionPhase [9] and RSMT (20) further incorporate motion phase modeling for smoother, phase-aware transitions. 2) Segment interval infilling: Transitional segments of specified
length are created to bridge segment boundaries 228] 29). Methods like CMB (30) and MDM
extend this by integrating semantic conditions into the inbetweening motion generation process.
Long Motion Sequence Generation can be approached two ways: sequential generation and par- allel generation. Sequential generation methods such as TEACH [12, PCMDM [14] M2D2M [I], and InfiniMotion (32] generate motion segments one after another in an autoregressive manner. Analogous to traditional motion graph based approaches 33
36), these methods require that the generated segments not only align with the current input semantics, but also transition smoothly from previously generated segments. For parallel generation, priorMDM [13] generates semantic motion segments independently and then synthesizes blending transitional segments using a diffusion model. Note that the frameworks above typically model transitions in the raw motion space, which may lead to slight discontinuities at the segment boundaries. To address this, motion inbetweening techniques are usually employed to smooth the transition boundaries. For example, TEACH [I2 uses spherical linear interpolation (SLERP) to create motion frames connecting boundary poses.
34
35
3 Compositional Phase Diffusion
We propose three key components for the framework: the Action-Centrie Periodic Autoencoder (ACT-PAE), the Transitional Phase Diffusion Module (TPDM), and the Semantic Phase Diffusion Module (SPDM). ACT-PAE creates motion latent manifold that captures important semantic and transition- aware phase information for each motion segment X E R. and represent them as set of latent variables
[F, A. B, S). Leveraging such ACT-PAE latent space, TPDMs refine phase latents of the current segment using the phase dynamics information from adjacent motions, while SPDM incorporates semantic information into the diffusion process. Details of these components will be covered in Sec. 31
With these innovative elements, we adapt the Compositional Phase Diffusion framework to various motion generation tasks. For the compositional motion generation and motion inbetweening tasks, SPDMs and TPDMs gradually integrate semantic information and phase dynamics information from adjacent segments throughout the denoising process of sequentially connected segments. For the long-teni motion generation task, the phase dynamics of a motion segment will progressively propagate bidirectionally along the timeline during the denoising process. This promotes mutual phase dynamics adjustment between segments, increases their transition-awarennss. and thereby enhances overall motion consistency. By blending series of transition-awant motion segments, we create a cohesive motion sequence composed of series of semantically meaningful segments and seamless transitions in between. Further details are provided in Sec. 3.2
3

---

Transformer
TPDM: Transitional Phose
Difusion Module
denolinng Eie token
Transformer
Diflusion Module
00000000000000000
Parom 1010 tnkeetiver
Framie leve Sakenterr
Param takemieer
Framc lowel Sollorace
toeerizer
Frume toieefie
Encoder
S 17 5016
S CT 5/-18
Conditioning
STVF-IIT- S11-0
Text
Step to right
(a) Detail of the Transitional Phase Diffusion Module (TPDM)
(b) Detail of the Semantic Phase Diffusion Module (SPDM)
Figure 2: Module detail of TPDM and SPDM. (a) The TPDM uses the clean phase latents from the adjacent segment Pe to denoise the current motion phase P making the current denoising motion align with the motion dynamies of the adjacent motion segment. (b) The SPDM utilizes the text embedding C from CLIP to denoise the motion phase parameters of the current denoising motion.
3.1 Key Components
3.1.1 ACT-PAE: Action-Centrie Periodic Autoencoder
Our ACT-PAE builds upon the transformer-basee motion autoencoder architecture from ACTOR 37 ACT-PAE encoder first processes input motion E R x of N frames into four phase parameters A, R Unlike PAE [13], which processes motion using convolution scheme and derives phase parameters from the FFT results, the ACT-PAE encoder directly processes variable- motion using a transformer and predicts their phase parameters. To enforce latent space periodicity, these parameters are parameterized into a periodic signal Q E R&N using the following equation:
Q A (F (T - S)) B.
(1)
Here represents the time difference of each frame in the motion relative to the center of the motion segment. The sin function parameterizes (T into periodic sine wave, transforming frequency and phase shift information into a sinusoidal basis. This representation allows ACT-PAE to capture the underlying phase dynamics of the motion effectively. Finally, the ACT-PAE decoder takes Q to predict the motion X. The entire ACT-PAE is trained with L2 loss.
The main advantage of ACT-PAE lies in its ability to capture unified phase dynamics within semanti- cally meaningful motion (e.g.. Xp and X2). Its architecture handles variable- length motion input, which eliminates the need for fixed-window motion slicing and thus preserves complete semantic and transition-awan phase information in phase latents. By doing so, ACT-PAR standardizes the training objective for the subsequent motion diffusion modules more effectively than the fixed-window pro- cess used in PAE, which results in an undetermined number of phase latents. Importantly, we have changed the sinusoidal positional embedding module PE and the time window T to accommodate variable-length motion encoding. For example, T can be parameterized for normalized action pro- gression (-1 1 to 1 across N frames), or actual time duration (- to PE and T adjustments will be provided in the Appendix.
N across N frames). Details of
3.1.2 SPDM: Semantic Phase Diffusion Module
SPDM is designed to denoise phase parameters so that the corresponding decoded motion segment is aligned to the semantic condition. In text-to-motion settings, SPDM employs the pre-trained (3 to encode the input text conditions into embedding vector Cp- as shown in Fig. b). This embedding guides the denoising process of the phase parameters Pk which are encoded by ACT-PAE, for the semantically conditioned motion Xp via PO (ss Cp. P!.). Here, indicates the denoising time step. Note that the input phase parameters P are parameterized as both param-level tokens F. A, B, S) and frame-level tokens which constitute the periodic signal Q created using Equation [ These frame-level tokens explicitly outline the spatio-temporal motion context in the phase parameters, assisting SPDM in monitoring the current semantic context during the phase parameter denoising process. Finally, a self-attention transformer is employed derive semantically denooseed parameters Po
CLIP- ViT-B/32

---

Compushional Texts
Diflosing stop h:1
Motoo Segments
SPOM
Phase Miknn
Stop the
N, Text Condition
TPUN,
Proparanee to
X, Consitiin
Rabee both prms
Difkaing stop
TPOM,
Phase Midin
ACT-PAF
Transtionngg Medion Xe
TPION
Propepace gtep I-]
Stending
TPOM,
SPOW
Micnn
Docoder
Difluing V-1
Phecediigg Medioo Xp
Succeeding
Denclsing step
Motion XL
Blended Motion Result
Figure 3: Illustration of the phase diffusion pipeline for the compositional motion generation task. SPDMs and TPDMs guide the denoising of motion segments through semantic information and the phase dynamics information from adjacent motions, respectively. The denoised results are combined via phase mixing and either diffused back to step l 1 or fed into the ACT-PAE decoder at the final step to produce motion segments, which are then linearly blended to create a long sequence.
3.1.3 TPDM: Transitional Phase Diffusion Module
TDPM is designed to denoise phase parameters such that the resulting decoded motions are transition- ally aligned with adjacent motions. Depending on the specific application scenario, these adjacent motions may come from either the forward backward direction, which will be explained in Sec. 3.2
Fig. 2 a) illustrates the TPDM architecture, which leverages the clean phase parameters of the forward, preceding motion Pf to denoise the phase parameters of the transitioning motion P. Fr, Pt P)). f indicates that the denoising process is conditioned by the phase dynamics of the forward, preceding motion. Similar to the SPDM design, both param-level tokens and frame- level tokens are computed for P The motion context provided by the frame-level tokens assists the TPDM in ensuring alignment of the motion dynamics during the denoising process. Finally, cross-attention transformer [I] processes all the P and PO tokens to predict phase noise ttf
As can be seen in Fig. 33 there least two TPDM modules involved in compositional motion generation: ,PDM, utilizes preceding motion phase Po to denoise P Pe, Fr, (k, P. PD) and TPDM, utilizes succeeding motion phase P instead: PO Fr, (k, P2). These two modules work together to ensure that dynamic coherence is maintained in both forward and backward directions throughout the composited long sequence.
SPDM and TPDMs are implemented as e-models, and their training procedures follow those of traditional diffusion frameworks [25] 3 Details for SPDM and TPDM are provided in the Appendix.
3.2 Applications
3.2.1
Compositional Motion Pair Generation
The compositional motion pair generation task focuses on creating two sequentially connected motion segments, Xp and Xg. To ensure a smooth transition while maintaining semantic alignment, we develop compositional motion diffusion pipeline that progressively incorporates the semantic information and the phase dynamics information from adjacent segments in the diffusion process. This phase dynamics information exchange enhances phase alignment between Xp and X and facilitates the creation of an intermediate transition segment x, which is linearly blended into the output to further smooth the segment boundary.
The pipeline detail is shown in Fig. 3 and described in Algorithm T in the Appendix. During the denoising step k. SPDM semantically denoises the phase latents P and Pk for X, and X, based on their respective semantic conditions. TPDM, and TPDM, then estimate po ph Po ty. PD and po by combining and mixing information from temporally adjacent phase latents (i.e., P P and P.) from the earlier denoising step k + 1. For instance, Pk is denoised with pd and pd from step 1,
We define the concept of the transition motion X. to be the segment covering exactly the second half of X,
and the first half of X..
5

---

Inpur Mations and Ganvitional Text
TYEN,
Plase
Aell-aa Cecoelt
[ R-
Deccedod Matton Sogricess
Phose
- Oiflunng
X, Jost
Stnh P
Conditin
TYUN,
SPOW
Mixoon Na,
Modondd
Propugone
Uneur Burdra
TPON,
Preze
Difloinng step b-l
Procading
lsbetweendig
Suaceeding Motioa,,
Mooia X,
M X,
Figure 4: The phase diffusion pipeline for the motion inbetweening tasks. The inbetweening motion X; along with two transitional motions Xs, and Xt, are simultancously generated. These motions are subsequently linear blended to form the final inbetweening output.
while the resulting Pe from step helps denoising P and Pk-1 in step 1, demonstrating the exchange of phase dynamics throughout diffusion process.
The predictions from both SPDM and TPDM are then combined using the Phase Mixing Equation:
p +
pd
+1
(2)
2
The variable r is defined as r ( )s for semantic conditioned motion segments (i.e., Xp and X,), and 1 otherwise (i.e., X,). Note that the value of for semantically denoised segments is determined by the ratio of total steps K and current denoising step k. ensuring transition compatibility early and enriching semantic details progressively. Finally, DDIMScheduler (3] FD is utilized to estimate the clean phase latent and the next step phase latent based on the mixed phase latent.
3.2.2 Motion Inbetweening
The motion inbetweening task aims generate inbetweening motion X,, which is of specified length to bridge the gap between two separated motions [Xp. X.). The pipeline for the task is illustrated in Fig. The process begins by encoding these segments into latent codes Pp and P with the ACT-PAE encoder, then uses TPDMs to guide the generation of inbetweening motion X, and two transitioning motions Xt, and Xt,- An optional SPDM can be incorporated for X,, modifying the task from unconditional (UMIB) to conditional (CMIB). Additionally, the pipeline demonstrates the flexibility and scalability of the Compositional Phase Diffusion framework by enabling the compositional generation of more motion segments of varying lengths through parallel processing with an increasing number of modules.
3.2.3 Long-term Motion Generation
Long-term motion sequence generation extends beyond short-term compositional motion pair genera- tion by producing much longer continuous motion, composed of hundreds or thousands of motion segments. While short-term tasks focus on semanties and transitions within a few segments, long-temn generation involves monitoring kinetic dynamics, which can impact motion over extended sequences and potentially disrupt motion realism and physical plausibility. To adapt our compositional motion framework for long-term generation, we can unroll it to process each segment with the [TPDM f. SPDM, triplet and denoise them based on semantics and adjacent phase conditions. By rearranging and batching the input for each module, the denoising process of all segments can be done in parallel, making the overall denoising time independent of the number of segments.
The bidirectional TPDM mechanism in our framework ensures that phase information propagates progressively throughout the sequences, rather than being confined to specific local segments. This mitigates the risk of substantial phase dynamics misalignments between adjacent semantic segments and simplifies the adjustments required by transition segments. Unlike existing methods that struggle with handling substantial differences in motion phase dynamics between segments, which often result in the loss of smooth transitions or semantic alignment, our model continuously refines both motion phase dynamics and text alignment to preserve long-term motion integrity.

---

44EL
Rigid Transition with Foot Stiding atst.thruge Tarm
Ulrraliitii Transition with Allered Roll Atgic to Root
Sascoré and Progresabve Transttions
(a) priorMDM
(b) TEACH
(c) Ours
Figure 5: Compositional motion pair result visualization for [wallk(24s), down(3.6s)) The motion
frames are colored from red to purple in rainbow gradient to represent the progression of time. Note that priorMDM exhibits an unrealistic, sudden turn during the sit-down action, which is reflected in its low FID score. TEACH's result includes footing skating in the walk motion. In contrast, our framework generates a fluid walking motion that transitions smoothly into a sit-down action.
Table 1: Quantitative results for Compositional Motion Pair Generation on the BABEL- -EACH test set. Bold and underline indicates the best and the second-best result.
Comp. Motion Pair
Motion Realism - FIDI
Text Aligument MMD
Smt.
Tm.
Overall
Smt.
Tm.
Overall
MDM-30
1.084
2.526
1.146
3.793
6.429
4.923
MLD-30
13.88
15.20
14.25
8.478
7.407
7.632
TEACH
12
0.941
2.375
1.041
3.185
7.479
4.821
PCMDM
14
1.056
1,898
0.837
4.548
5.423
PriorMDM
13
1.148
2,580
0.839
3.732
7.399
5.025
Ours
0.736
1.807
0,782
3,509
6,545
4.711
4 Experiments
4.1
Implementation and Evaluation Details
4.1.1
Training and Evaluation Dataset
We use the BABEL-TEAS TEACH dataset 4 [2] for training and evaluation, as it provides annotated subsequence pairs essential for long-term motion generation
facilitating the learning of transitions between subsequences. These annotated pairs derived from decomposing fine-gra ane-graieed text subsequence annotations from BABEL [4]. For example, a sequence such as [walk, sit down, stand up, move arms] is split into pairs like [walk, sit down), [sit down, stand up], and [stand up. move arms].
Following the data processing pipeline outlined in recent long-term motion generation 12 13 14),
we set the minimum and maximum lengths for each subsequence as 45 and 250 frames, respectively. The pipeline then groups the textually annotated subsequences into pairs. Note that any overlapping offsets identified by the pipeline above are redistributed among the annotated motion subsequences. As result, the training dataset contains 4370 subsequence pairs, while the testing dataset includes 1582 subsequence pairs. Moreover, we follow PCMDM [E] and priorMDM [3] to transform the motion data into HumanML33 [6] format. Initially, the root trajectory is represented by only 4 out of 263 parameters, omitting essential root orientation details. Therefore, we supplement a 6D rotation (88 for the root, increasing the total parameters to E 269. Note that all models being compared are trained on the same dataset and representation to ensure a fair comparison.
Remark on Dataset. To the best of our knowledge, BABEL-TEACH is currently the only dataset that provides subsequence pair annotations. Long-term motion generation models, such as TEACH (12) PCMDM [14], and our own model, require data samples in the form of continuous subsequence pairs to effectively learn transitions between sequences. Motion datasets annotated in other formats or modalities cannot be efficiently utilized either for training or for fair evaluation of our task.
4.1.2 Evaluation Metrics
We assess the results of compositional motion pair generation, long-term motion generation, and conditional motion inbetweening based on two key aspects: Fréchet Inception Distance (FID) for Motion Realism and Multimodal Distance (MMD) for Text Alignment, following the T2M 6
7

---

Table 2: Quantitative results for Long-term Motion Generation on the BABEL-TEACH test with a single extended text sequence of 3,164 texts (302,298 frames, 168 minutes). Bold and underline indicates the best and the second-best result.
Long-term
Motion Realism
FIDI
Text Alignment
MMD,
Smt.
Tm.
Overall
Smt.
Tm.
Overall
MDM-30 8
1.094
2.051
1.365
3.877
6.411
4,958
22
17.02
8.423
7.407
7.690
TEACH
12
0,785
1.645
1,780
3.175
5.483
4.944
PCMDM
14
1.088
0.934
0.876
4.188
5.721
5.156
PriorMDM
13
2.288
1.067
1,536
4.299
5.536
5,060
Ours
0.773
0.909
0.847
3.642
5,389
4.849
evaluation protocol utilized in PCMDM [4] and priorMDM 13 for long-term motion assessment. We exclude R- R-precision (R-prec.) because it overlaps with Multimodal Distance (MMD) and omit Diversity (Div.) due to unclear role evaluating motion performance. For clarity, we segment the generated motions into semantic and transitional parts and evaluate the aforementioned metrics across three groups: 1) Semantic (Smt.), which focuses solely on the semantic segments, 2) Transition (Trn.), which assesses only the transitional segments, and 3) Overall, which evaluates both semantic and transitional segments together. For the contextual aligmment of transitional segments, we use the text from both the preceding and succeeding motions, assuming that the transition should retain the semantic information from overlapping segments.
For unconditional motion inbetweening, we assess the Transition Realism by using 1.2 losses and NPSS 33, described 27 19,, by comparing them to the ground truth inbetweening motion. Specifically, we focus on L. losses for joint velocity (1.2- Vel) and 6D rotation (38 (L22RRo66) to provide direct and explicit evaluation of human motion in the HumanML3D [6] format. Additionally, we assess Transition Smoothness using root mean squared jerk [40] (RMS-Jerk' over joint rotations. Detailed descriptions of these evaluation metries will be included the supplementary material.
4.2
Compositional Motion Generation Performance Evaluation
4.2.1
Compositional Motion Pair Generation
The compositional motion pair experiment follows the setup illustrated on the left in Fig. with the objective of generating motions Xp. Xt, and X, based on the corresponding text condition pairs (Cp. C.). We compare the performance of our method with long motion generation models, including TEACH [2] PCMDM 14), and priorMDM [3] among with single text-conditioned models MDM-30 g and MLD-30 22 as baselines. In single text-conditioned models, additional frames are generated for preceding and succeeding motions create a 30-frame overlapping region, which is then blended linearly to form smooth transitions. The evaluation employs the FID and MMD metrics across three groups: 1) Semantic (Xp: X.). 2) Transition (X+), and 3) Overall (Xp. Xt, Xs). The experiment results are summarized in Tab. | showing that our model produces realistic motions and achieves the best overall FID and MMD scores. Strong overall performance demonstrates the potential to generate high-quality compositional motions with natural transitions. Although TEACH shows strong contextual alignment in semantic segments, its lower Overall FID score indicates a compromise in motion quality, leading to motion artifacts such as changes in the root roll angle, as shown in Fig.
The performance comparison with MDM-30 highlights the effectiveness of SPDM in single-sequence text-to-motion generation. Since only a very short blending window applied between segments, evaluating Smt. FID and Smt. MMD for MDM-30 is essentially equivalent to evaluating two independently generated motion sequences, effectively reflecting the single-sequence text-to-motion generation scenario. Therefore, the superior performance of our method on Smi. FID and Smt. MMD compared to MDM-30 underscores the competitive performance of SPDM in the standalone text-to-motion generation task.
Remark.
4.2.2 Long-term Motion Generation
To assess the long-term motion generation performance, we combine all text conditions from the testing dataset into single extended text sequence of 3, 164 texts, and apply comparison models to
8

---

(a) priorMM
(b) RSMT
Ours
Figure 6: Visualization of the UMIB with 120 transition frames: preceding motion in blue, transition- ing motion green, and succeeding motion in yellow.
Quantitative results for Unconditional Motion Inbetweening (UMIB) on BABEL- TEACH [12] test set. We report the performance under settings of transition lengths at 60, 120, and 180 frames. Bold and underline indicates the best and the second-best result.
Table 3:
UMIB
Transition Realism
Transition Smonthness
L2-Vel
L2-Rot6D +
NPSS
RMS-Jerk
Length
60
120
180
60
120
180
60
120
180
60
120
180
RMIB
27
0.0353
0.0307
0.0297
0.3510
0.3797
0.3789
2.0908
5.7334
9.1055
1,9216
1.3794
1.1009
RSMT
20
0.0345
0.0273
0.0246
0.2151
0,2438
0.2654
0.8552
2.8986
4.836
1.6435
1.4670
1.3249
CMB
0.0200
0.0172
0.0175
0.2194
0.2209
0.2289
0,4085
1.0278
1.6723
2.0658
1.9885
1.9298
MDM
0.0302
0.0275
0.0319
0.2961
0.3309
0,3099
1.0369
3.2291
4.1440
1.9283
2.1205
2.0688
PriorMDM
0.0151
0.0144
0.2398
0.2455
0.2479
0.5640
1.2107
1.7469
0.4058
0.4495
0.2803
Ours
0.0101
0.0102
0.0125
0.2124
0.2205
0.2220
0.3651
0.9296
1.6308
0.0963
0.1054
0.1213
generate long motion across 302,298 frames (168 minutes). As shown in Tab. 2 experiment results indicate that our method achieves competitive performance in both motion realism (Overall FID) and text alignment (Overall MMD) even in long motion generation scenarios. This demonstrates the superiority of our phase modeling approach for transition aware motion generation, compared to other methods that model transitions directly in the raw motion space. Among the other compared methods, TEACH continues to struggle with poor transition generation, resulting in a high Trn. FID. PriorMDM and PCMDM face challenges in generating realistic semantic segments that align with the text input. Note that MDM and MI.D show similar performance in this task as in compositional motion pair generation, mainly because the transitions between subsequences are created through blending rather than being generated by machine learning models.
4.3 Motion Inbetweening Performance Evaluation
The unconditional motion inbetweening (UMIB) experiment follows setup similar Fig. 4 where a specific number of frames around the transition boundary of testing motion pairs are masked evaluate various methods for reconstructing the masked motion content. We assess the Vel, L2-Ro16D, and NPSS metrics by comparing the generated segments with the actual masked content, while the RMS-Jerk metries evaluate motion smoothness. The effectiveness of motion inbetweening is analyzed and compared with autoregressive frame prediction methods, RMIB (7 and RSMT (20), as well as interval infilling methods, CMB (30), MDM [ and priorMDM inbetweening length settings, all within the training motion length range of [45, 250]. The results are shown in Tab. 3 demonstrating the superior performance of our proposed framework across all inbetweening length settings. Additionally, Fig. Fig.Gillun illlstrates the smoothness and realism of our generated results, as reflected in the metric values. In contrast, the results generated by priorMDM tends to exhibit hyperactivity by producing random motion content unrelated to the adjacent motion, which negatively impacts the overall inbetweening performance. Lastly, RMST results reveal failure to connect the succeeding motions. This highlights the limitations of autoregressive processing with fixedwwinow phase latents, which also justifies both priorMDM and our method for managing variable-length motion as cohesive entity.
B across three different

---

In addition to the UMIB experiment, we also conducted the conditional motion inbetweening (CMIB) experiment assess the effectiivnesss of conditioning the inbetweening region with text context. The results of this experiment are detailed in the Appendix.
4.4 Ablation Studies and User Studies
We assess the effects of our proposed modules and recommended hyperparameters compositional motion generation and motion inbetweening tasks. Firstly, the integration of frame-level tokens within SPDM and TPDM significantly enhances their performance in denoising param-level tokens. Secondly, we assess the phase mixing parameter setting, revealing that ( )3 is optimal for semantic conditional scenarios, while r = 1 is best for unconditional scenarios. Moreover, the user study, our approach attains the highest scores for motion realism and smoothness. Further details of the ablation studies will be provided in the supplementary material.
Conclusion and Future Work
We present the Transitional Phase Diffusion Module (TPDM) and the Semantic Phase Diffusion Module (SPDM), which operate within the periodic latent space generated by the Action-Centric Periodic Autoencoder. These modules inject semantic guidance and neighbouring phase information into the motion denoising process, enabling the generation of semantically meaningful motion clips with smooth transitions. The proposed Compositional Phase Diffusion pipeline, which incorporates both the TPDM and SPDM modules, can adapted for compositional motion generation and motion inbetweening tasks. Its flexibility to handle multiple motion segments simultaneously enhances its capability to tackle complex motion sequencing tasks. Extensive experiments and evaluations have showcased the framework's effectiveness in compositional motion generation and motion inbetween- ing tasks. Further exploration of these frameworks holds promise for the development of advanced notion-generation techniques in the future. As framework applies compositional diffusion in motion generation using a basic phase mixing technique, potential performance improvement may be achievable by incorporating advanced methods like acore-based or potential-based diffusion. Addi- tionally, incorporating learnable parameters or an adaptive mechanism for phase mixing could further enhance results. However, implementing such features is challenging and requires more detailed data modelling and complex architectures. Future research will focus on adjusting the architecture and data representation to incorporate these advanced diffusion techniques.
References
[1] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention all you need. Advances in neural information processing systems, 30, 2017.
[2] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, (166840-6511, 2020.
[3] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502. 2020.
[4] Abhinanda R Punnakkal, Arjun Chandrasekaran, Nikos Athanasiou, Alejandra Quiros-Ramine and Michael Black. Babel: Bodies, action and behavior with english labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 722-731, 2021.
[5] Junkun Jiang, Jie Chen, and Yike Guo. A dual-masked auto-encoder for robust motion capture with spatial-temporal skeletal token completion. In Proceedings of the 30th ACM International Conference on Multimedia, pages 3123-5131, 2022.
[6] Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng. Generating diverse and natural 3d human motions from text. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5152-5161, 2022.
[7] Junkun Jiang, Jie Chen, Ho Yin Au, Mingyuan Chen, Wei Xue, and Yike Guo. Every angle is worth a second glance: Mining kinematic skeletal structures from multi-view joint cloud. IEEE Transactions on Visualization and Computer Graphics, 2025.
10

---

[8] 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 Repre- sentations, 2023.
[9] Chuan Guo, Xinxin Zuo, Sen Wang, and Li Cheng. Tm2t:
Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts. In European Conference on Computer Vision, pages 580-597. Springer, 2022.
[10] Jihoon Kim, Jiseob Kim, and Sungjoon Choi. Flame: Free-form language- based motion synthesis & editing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 8255-8263, 2023.
[11] Mingyuan Zhang. Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu. Motiondiffuse: Text-driven human motion generation with diffusion model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
[12] Nikos Athanasiou, Mathis Petrovich, Michael Black, and Gül Varol. Teach: Temporal action composition for 3d humans. In 2022 International Conference on 3D Vision (3DV), pages 414-423. IEEE, 2022.
[13] 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, 2024.
[14] Zhao Yang. Bing Su, and Ji-Rong Wen. Synthesizing long-term human motions with diffusion models via coherent sampling. In Proceedings of the 31st ACM International Conference on Multimedia, pages 3954-3964, 2023.
[15] Sebastian Starke, Ian Mason, and Taku Komura. Deepphase: Periodic autoencoders for learning motion phase manifolds. ACM Transactions on Graphics (TOG), 41(4):1-13, 2022.
[16] Daniel Holden, Taku Komura, and Jun Saito. Phase-functioned neural networks for character control. ACM Transactions on Graphics (TOG), 36(4):1-13, 2017.
[17] Sebastian Starke, Yiwei Zhao, Taku Komura, and Kazi Zaman. Local motion phases for learning multi-contact character movements. ACM Transactions on Graphics (TOG), 39())44-1, 2020,
[18] Ian Mason, Sebastian Starke, and Taku Komura. Real-time style modelling of human locomotion via feature-wise transformations and local motion phases. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 5(1):1-18 2022.
[19] Paul Starke, Sebastian Starke, Taku Komura, and Frank Steinicke. Motion in in-betweening with phase manifolds. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 603):1-17,200 2023.
[20] Xiangjun Tang, Linjun Wu, He Wang, Bo Hu, Xu Gong, Yuchen Liao, Songnan Li, Qilong Kou, and Xiaogang Jin. Rsmt: Real-time stylized motion transition for characters. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1 1-10, 2023.
[21] Weilin Wan, Yiming Huang, Shutong Wu, Taku Komura, Wenping Wang. Dinesh Jayaraman, and Lingjie Liu. Diffusionphase: Motion diffusion in frequency domain. arXiv preprint arXiv:2312.044 040066 2023.
[22] Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao Chen, and Gang Yu. Executing your commands via motion diffusion in latent space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18000-18010, 2023.
[23] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-84 8763. PMLR, 2021.
[24] Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz. Physdiff: Physics-guided human motion diffusion model. In Proceedings of the IEFF/CVF international conference on computer vision, pages 16010-16021, 2023.

---

[25] Korrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakom, and Siyu Tang. Guided motion diffusion for controllable human motion synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2151-2162, 2023.
[26] Félix G Harvey and Christopher Pal. Recurrent transition networks for character locomotion. In SIGGRAPH Asia 2018 Technical Briefs, pages 1-4. 2018.
[27] Félix G Harvey, Mike Yurick, Derek Nowrouzezzahrai, and Christopher Pal. Robust motion in- in-betweening. ACM Transactions Graphics (TOG), 39 2020.
[28] Manuel Kaufmann. Emre Aksan, Jie Song, Fabrizio Pece, Remo Ziegler, and Otmar Hilliges. Convolutional autoencoders for human motion infilling. In 2020 International Conference on 3D Vision (3DV), pages 918-927. IEEE, 2020.
[29] Alejandro Hernande7, Jurgen Gall, and Francesc Moreno-Noguer, Human motion prediction via spatio-temporal inpainting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 71 7134-7143, 2019.
[30] Jihoon Kim, Tachyun Byun, Seungyoun Shin, Jungdam Won, and Sungjoon Choi. Conditional motion in- tn-betweening. Pattern Recognition, 132:108894, 2022.
[31] Seunggeun Chi, Hyung-gun Chi, Hengbo Ma, Nakul Agarwal, Faizan Siddiqui, Karthik Ramani, and Kwonjoon Lee. M2d2m: Multi-motion generation from text with discrete diffusion models. In European Conference Computer Vision, pages 18-36. Springer, 2024.
[32] Zeyu Zhang, Akide Liu, Qi Chen, Feng Chen, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang. Infinimotion: Mamba boosts memory in transformer for arbitrary long motion generation. arXiv preprint arXiv:2407.10661, 2024.
[33] Lucas Kovar, Michael Gleicher, and Frédéric Pighin. Motion graphs. ACM Transactions on Graphics, 21(3):473-482, 2002.
[34] Jianyuan Min and Jinxiang Chai. Motion graphs++: compact generative model for semantic motion analysis and synthesis. ACM Transactions on Graphics 31 (6):1-12, 2012.
[35] Ho Yin Au, Jie Chen, Junkun Jiang, and Yike Guo. Choreograph: Music-conditioned automatic dance choreography
a style and tempo consistent dynamic graph. In Proceedings of the 30th ACM International Conference on Multimedia, pages 3917-3925, 2022.
[36] Ho Yin Au, Jie Chen, Junkun Jiang, and Yike Guo. Rechoreonet: Repertoire- bepertoire-based dance re- choreography with condiiicndi temporal and style clues. Machine Intelligence Research, pages 1-11, 2024.
[37] Mathis Petrovich, Michael J Black, and Gül Varol. Action-conditioned 3d human motion synthesis with transformer vac. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10985-10995, 2021.
[38] Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li. On the continuity of rotation representations in neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5745-5753, 2019.
[39] Anand Gopalakrishnan, Ankur Mali, Dan Kifer, Lee Giles, and Alexander Ororbia. neural temporal model for human motion prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12116 12125, 2019.
[40] Raymond Young and Ronald Marteniuk. Acquisition of multi-articular kicking task: Jerk analysis demonstrates movements do not become smoother with learning. Human Movement Science, 1665):677-701, 1997.
12

---

NeurIPS Paper Checklist
1. Claims
Question:
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction outline the key challenges addressed, contri- butions, and a summary of the experimental findings.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
We include the limitation of work in Section 5
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
13

---

Justification:
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proof' can either appear the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification: We have described the experimental details and have properly cited the open- source assets and materials necessary for reproducing the experiments. The code will be made publicly available upon acceptance.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, it may be necessary either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, or other means that appropriate to the research performed.
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome deseribe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
14

---

Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: used the public dataset, and we plan to publicly release our code once status of the paper is confirmed.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips)) cc/
pub / gu des Codeeuuome ss issionn onPol cy) for more details.
While we encourage the release of code and data, we understand that this might be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
http:scccpppcccccccc..c..cocccpa Seteeidde//ooeeuuubaeeea Submo assoimssli as ciiioooolll ooyyl for more details.
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments should state which ones are omitted from the script and why.
reproducible, they
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: See Section 4.1 and Appendix A
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to a level of detail that is necessary appreciate the results and make sense of them.
The full details can provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification: Due to the limited computational resources, we conducted each experiment only once.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
15

---

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation or the standard error of the mean.
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables figures symmetric error bars that would yield results that are out of range (c.g. negative emror rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification: See Appendix and supplementary material.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't 't make it into the paper).
9. Code ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics / http://wwrr..rcooooooccccccccaad
Answer: [Yes]
Justification:
We fully adhere to the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., if there is special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: See Appendix
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
16

---

The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, scraped datasets)?
Question:
Answer: [NA]
Justification: The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g.. code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
The paper properly credits the creators or original owners of the assets used, such as code, data, and models. We also have correctly cited the relevant literature.
Justification:
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
assets are released, the license, copyright information, and terms of use the package should be provided. For popular datasets, paapersw's www.osocittcceeeoeo com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
17

---

If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: will disclose the code after the confirmation of the status of the paper, but currently no new assets is available.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moodl as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Question:
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, not include any information that would break anonymity (if applicable), such the institution conducting the review.
18

---

16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non- non-standard components.
cc/ Please refer to our LLM policy (https: : /ttess:///urriiie http///urriscccoonnfeeeeeee ccc/oonfeeences/////oooooooocsse 2025/
LLM) for what should should not be described.
19

---

A Technical Details of Compositional Phase Diffusion
A.1 Compositional Motion Generation Algorithm
Algorithm implementation details of the phase diffusion pipeline for the compositional motion generation task in Sec. 3.2.1 The detail is also illustrated in Fig.
Algorithm 1 Compositional Phase Diffusion on generating sequence composed by 2 semantic conditioned segments
Require: forward TPDM (T, backward TPDM Fr, ()) SPDM Fs()), DiffusiinSSheeduler Fo(.) PKK PKK P [0, P..Pa s: P. < PP PK P ,K, P. for from K to do
sample latent for Xp. Xs, Xt clean latent placeholder
predict phase latent noise using SPDM and TPDMs
Pp + Fs(k, Cp. P
P
+ Fr, (k. Pe. P
ph
P Fr, (k. Pt p0) Pd f- Fr, (k: P P
Pe < FFr, (k. P. P.). P f- Fs( Ca, PA)
perform phase mixing as in Eq
Pe < K ) pb + K
P Pc
Drp = (#)3
P ph t
DT. = 1 bTg = (1)
P - ( pop + (1
k )² p
K
#sstimate phase latent at both step ks and step 0
Ph11 p (P P..
P Pl + (P P k 1)
PH P + (P P.K 1)
end for
return PL, P P
A.2 Adjustment to T and PE
As discussed Sec. 3.I.I we have refined the sinusoidal positional embedding PE and the time window T to support motion autoencoding with variable lengths.
Positional embedding PF is crucial for accurately representing time progression in motion encoding and generation. Traditional sinusoidal positional embeddings PE only reflect the time progression signal from the leading frame of motion leading frame of motion X. In contrast, our composite positional embedding Comp-PE creates duplicates of the positional embedding shifted to the middle and ending frames. These duplicates are stacked channel-wise, enhancing the model's awareness of sequential action progression from three key locations and improving semantic understanding.
On the other hand, the time window T E R NxQ essential for transforming the fixed-size parameters F, A. B, S into the variable length periodic signal Q following Equation [ in the main paper. In traditional PAES [15] (2) I' defined
fixed-length time window ranging from -1 to across 121 frames. When extending 7 to adapt to variable lengths, two variants arise: (norm'T) parameterizes the time window from and (frameTT) parameterizes the time window from N to N to align with the actual time duration. In this work, we employ a mixed linear parameterization (mixT), where & channels within the time window I are parameterized using frame'T, while the other half is parameterized using norm'..
over the frame count N to correspond with normalized action progression,
Note that the time window parameterizations imbalance in the transitioning motion X Using the settings in Fig. 3 of the main paper as an example, norm'T parameterize the X, left frame range [t.,, ttml as [-1, 0]. and the X, right frame range [tem, [ta] as [o, 1]. Similarly, frameTT parameterize the X, right frame range [tpm, tto] as [0, Lte Lem] align with the actual time duration.
implemented piecewise address the length
"X, represents the segment covering the second half Xp and the first half of X,. For example, if Xp is 2 seconds and X, is 8 seconds, Xx will span 5 seconds, covering the last second of X, and the first 4 seconds of X.. Note that the middle frame of X, is defined at the transition boundary; when Xp and X. have unequal lengths, this middle frame offist from the center of Xt.
20

---

A.3 Details of SPDM and TPDM
As discussed Sec. 3.1.2 and Sec. 3.1.3 the main paper, our diffusion models are designed as e-model [25], which is trained using the { loss (formulated as II1) to the diffusion noise (<p. ca which was scheduled to diffuse the clean phase latent of the motion segments to [P P P. respectively. Note that the estimation of diffused latent the next diffusion step and the clean phase latent (pred original originllsampple is also supported by the commonly used DDIM diffusion scheduler Ph- PO c FD(PP P p -)).
SPDM is trained using semantically annotated motion segments in the BABEL-TEACH [12 training dataset, which are the preceding motion Xp and succeeding motion X in each motion subse- quence pair, each associated with text annotations Cp and Cs. The training loss of SPDM on each subsequence pair is illustrated follows:
Ls [s Pp. - splh + (k, C. c//
On the other hand, TPDM is trained based the neighbouring information in each motion subse- quence pair. Specifically, we can obtain 2 transitional segment pair (Xp, Xx). (X,, X,) for each motion data tuple (Xp, Xt, Xg). Then, TPDM, , and TPDM, trained on each transitional segment pair to denoise motion phase parameters using neighbouring phase information from either the forward backward direction. The training loss of TPDMs on each subsequence pair are illustrated as follows:
LT (k, P. Pp) - eelli + || (k. P PD) falli.
CT. (k. P.. P()) <plli F.. (k. P!. 4411.
A.4 Implementation Details
We apply the emphasis projection with c= 15, as demonstrated in GMD [25]. to incorporate root trajectory information into the motion representation. Also, models designed based phase latent size Q = 512, which serves as both the latent dimension for all diffusion modules and the number of periodic signals in ACT-PAE. For the diffusion step setting in SPDM and TPDM, DDIM 3 is utilized for 1000 training steps and 100 inference steps.
B Conditional Motion Inbetweening Evaluation
(a) CMB
(b) priorMDM
(c) OuIS
Figure 7: Visualization of the CMIB with 120 transition boundary frames conditioned with bend arms up: preceding motion in blue, transitioning motion in green, and succeeding motion in yellow.
As shown in Fig. our framework can integrate SPDM into the denoising process of inbetweening segments to enable conditional motion inbetweening. We use the same testing setup as in uncondi- tional motion inbetweening, focusing the evaluation on the inbetweening region. We evaluate our framework against CMB [30] MDM [8]. and priorMDM [3]]. The results, presented in Tab. 4 demonstrate that our method excels at producing natural inbetweening motion while adapting to input text semantics. As illustrated in Fig. 7 our framework creates smooth inbetweening motion that corresponds well with the input text condition bend arms up. contrast, both priorMDM and CMB show hyperactivity, resulting in abrupt inbetweening motion that does not align with the text.
21

---

Table 4: Quantitative results for Conditional Motion Inbetweening (CMIB) on the BABEL- TEACH [12] test set. We report the performance of various methods under the settings of transition lengths 60, 120, and 180 frames. Bold and underline indicates the best and the second-best result.
CMIB
Motion Realism
Text Alignment
Smt. FID !
MMD
Length
60
120
180
60
120
180
CMB
0.693
1.382
2.765
7.420
7.544
7.561
MDM
14882
7.609
7.658
PriorMDM
13
1.613
1.392
5.699
7.761
8.075
7.544
Ours
0.389
0.679
2.152
7.213
6.871
7.206
C Impact Statements
The exploration and application of phase latent spaces in this work contribute to the advancement of deep learning by offering new methodologies for signal processing and multimedia generation. has no negative impact on society as the focus is on technological improvement rather than datasets that could be sensitive have privacy implications.
22


Reviews:
[{"review_id": "AeSnMOGI8G", "reviewer": "Reviewer_muqa", "summary": "In this paper, the authors propose Compositional Phase Diffusion, a framework for generating long, semantically coherent motion sequences with smooth transitions. The method introduces Semantic Phase Diffusion Module (SPDM) and Transitional Phase Diffusion Module (TPDM)—which operate within a latent phase space learned by a transformer-based Action-Centric Periodic Autoencoder (ACT-PAE). By encoding motions as periodic signals, the framework enables semantically guided denoising and dynamic coherence between motion segments. SPDM integrates text-conditioned semantics, while TPDM ensures smooth transitions by diffusing phase dynamics from neighboring segments. The framework supports parallel processing of variable-length motion clips, allowing for scalable compositional motion generation and motion inbetweening. Experiments on the BABEL-TEACH dataset demonstrate state of art performance in long motion generation.", "questions": "Since the TPDM relies solely on the $\\mathbf{P}$ representations of neighboring motions without explicit semantic input, there is a concern that the generated transition may inadvertently alter the preceding or succeeding motions during the final linear blending step. Could this lead to partial loss or distortion of intended actions, thereby resulting in inconsistencies with the input text?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "#### **Strengths**\n\n1. The proposed method achieves state-of-the-art performance across multiple tasks, including Compositional Motion Pair Generation, Long-term Motion Generation, and Unconditional Motion In-between.\n2. The authors conduct comprehensive ablation studies that effectively validate the design and effectiveness of the proposed architecture.\n\n#### **Weaknesses**\n\n1. The authors do not convincingly explain why phase modeling should be preferred over learned motion space diffusion alone. While the method builds upon PAE and related works, the motivation for combining phase modeling could be better justified, particularly in contrast to simpler raw motion diffusion modeling or VAE-based methods.\n2. The paper lacks comparisons with more recent baseline methods, such as FlowMDM[1], which may offer stronger performance or complementary insights, especially in long-term motion generation and transition quality.\n3. Despite emphasis on the proposed framework's parallel denoising capability and scalability, the paper does not provide any empirical analysis on inference efficiency or generation latency.\n4. The user study relies on feedback from only 11 judges, which constitutes a rather limited sample size and raises concerns about the statistical reliability and potential bias in the subjective evaluation results.\n\n#### Minor\n1. Line#100: The notation $mathbf{P} = [\\mathbf{F}, \\mathbf{A}, \\mathbf{B}, \\mathbf{S}]$ is introduced without sufficient explanation or definition.\n2. The notation $\\mathbf{P}_{\\mathbf{tf}}$ used in the caption of Figure 2 is inconsistent with that in line#156.\n\n[1] Barquero, German, Sergio Escalera, and Cristina Palmero. \"Seamless human motion composition with blended positional encodings.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024.", "was_revised": true, "final_justification": "My final recommendation is Accept. The authors' rebuttal successfully resolved my main concerns.", "rebuttal": "Thank you for your thoughtful comments. We will thoughtfully address all of your suggestions and concerns in our revised submission.\n\n> [Weakness 1] The authors do not convincingly explain why phase modeling should be preferred over learned motion space diffusion alone. While the method builds upon PAE and related works, the motivation for combining phase modeling could be better justified, particularly in contrast to simpler raw motion diffusion modeling or VAE-based methods.\n\nWe evaluated our method against PCMDM and PriorMDM, which are established baselines for learned motion space diffusion, as well as MLD, a representative VAE-based approach for motion generation, given the lack of more recent VAE-based methods for long-term motion generation. Notably, our SPDM (Sec 3.1.2) and TPDM (Sec 3.1.3) architectures require both param-level tokens (F,A,B,S) and frame-level tokens (Q), available only in phase-modeled space, making direct adaptation to motion space diffusion or VAE-based frameworks challenging. Experimental results demonstrate that our method consistently outperforms these baselines, indicating that phase modeling achieves superior performance over motion space diffusion in both compositional generation and motion inbetweening tasks.\nFurthermore, the motivation for employing phase modeling instead of raw motion diffusion is discussed in the Introduction (Sec. 1), specifically the need to capture the intrinsic kinematics of each segment to enable smooth transitions. We acknowledge that our previous description was unclear, as it referenced PriorMDM and our method without explicitly distinguishing between raw motion diffusion and phase modeling. We will revise the manuscript to enhance clarity on this point.\n\n> [Weakness 2] The paper lacks comparisons with more recent baseline methods, such as FlowMDM[1], which may offer stronger performance or complementary insights, especially in long-term motion generation and transition quality.\n\nIn response to the reviewer’s suggestion, we performed additional experiments using FlowMDM. Due to the limited time available during the rebuttal period, we utilized the publicly provided model weights and inference pipeline to adapt FlowMDM for the compositional motion pair generation task. The results, presented in the table below, demonstrate the superior performance of our method compared to FlowMDM. Note that PCCAT in FlowMDM is trained to learn semantic alignment directly on pairs of subsequences, having significantly increased training complexity and a more complicated inference pipeline compared to our SPDM approach, where alignment is learned on individual subsequences.\n\n| Compositional Motion Pair Generation  \\| | Smt FID ↓ | Trn FID ↓ | Overall FID ↓ | Smt MMD ↓ | Trn MMD ↓ | Overall MMD ↓ |\n| ---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| FlowMDM  \\| | 1.619 | 7.978 | 4.102 | 7.695 | 7.674 | 7.031|\n| Ours  \\| | **0.736** | **1.807** | **0.782** | **3.509** | **6.545** | **4.711** |\n\n> [Weakness 3] Despite emphasis on the proposed framework's parallel denoising capability and scalability, the paper does not provide any empirical analysis on inference efficiency or generation latency.\n\nWe observed that the original description provides only limited information regarding parallelism, which makes it challenging to clearly understand the generation time and inference efficiency. To clarify this, we will add the following empirical analysis to the supplementary materials:\nWe empirically measured inference time for long-term motion generation. For scenarios with 2 subsequences, the total inference time is approximately 1.705 seconds, while for 10 subsequences, it is about 2.015 seconds. The SPDM and TPDM models require approximately 0.010 and 0.015 seconds per inference, respectively. Since subsequences are stacked along the batch dimension and processed in parallel by SPDM and TPDM, the model inference time per loop remains around 0.015 seconds, independent of the number of subsequences. In contrast, the phase mixing and rearrangement of the inferred subsequences require 0.002 seconds for 2 subsequences and 0.005 seconds for 10 subsequences.\nAfter 100 diffusion loops, the total diffusion stage takes roughly 1.7 seconds for 2 subsequences and 2.0 seconds for 10 subsequences. For the final motion decoding step, decoding times are 0.005 seconds for 2 subsequences and 0.015 seconds for 14 subsequences. In contrast to SPDM and TPDM inference, the subsequence rearrangement and linear blending steps do scale with the number of subsequences, as linear blending across overlapping regions cannot be efficiently parallelized.\n\n> [Weakness 4] The user study relies on feedback from only 11 judges, which constitutes a rather limited sample size and raises concerns about the statistical reliability and potential bias in the subjective evaluation results.\n\nTo enhance statistical reliability, we expanded the user study by recruiting an additional 19 judges during the rebuttal period, resulting in a total of 30 participants. The updated user study results, shown on the right side of the table below, are consistent with our previous findings.\n\n| Compositional Motion Pair Generation | Motion Naturalness (Previous) ↑ | Semantic Alignment (Previous) ↑ | Transition Smoothness (Previous) ↑ | Motion Naturalness (Updated) ↑ | Semantic Alignment (Updated) ↑ | Transition Smoothness (Updated) ↑ |\n| ---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| TEACH  \\| | 3.0 | 2.5 | 2.9 | 2.5 | 2.5 | 2.7 |\n| priorMDM \\| | 3.1 | 3.3 | 3.3 | 3.0 | 3.3 | 3.1 |\n| Ours  \\| | **4.2** | **4.0** | **4.3** | **4.1** | **3.9** | **4.1** |\n\n| Unconditional Motion Inbetweening | Motion Naturalness (Previous) ↑ | Transition Smoothness (Previous) ↑ | Motion Naturalness (Updated) ↑ | Transition Smoothness (Updated) ↑ |\n| ---: | :---: |  :---: | :---: | :---: |\n| TEACH  \\| | 3.5 | 3.3 | 3.0 | 2.9 |\n| priorMDM \\| | 3.1 | 3.0 | 2.9 | 3.1 |\n| Ours  \\| | **3.7** | **3.9** | **3.8** | **3.9** |\n\n| Conditional Motion Inbetweening | Motion Naturalness (Previous) ↑ | Semantic Alignment (Previous) ↑ | Transition Smoothness (Previous) ↑ | Motion Naturalness (Updated) ↑ | Semantic Alignment (Updated) ↑ | Transition Smoothness (Updated) ↑ |\n| ---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| TEACH  \\| | 2.1 | 2.7 | 2.3 | 1.8 | 2.3 | 1.9 |\n| priorMDM \\| | 2.8 | 3.6 | 3.3 | 3.0 | 3.4 | 3.0 |\n| Ours  \\| | **4.3** | **3.9** | **4.1** | **4.2** | **4.0** | **3.9** | \n\n> [Question 1] Since the TPDM relies solely on the \n representations of neighboring motions without explicit semantic input, there is a concern that the generated transition may inadvertently alter the preceding or succeeding motions during the final linear blending step. Could this lead to partial loss or distortion of intended actions, thereby resulting in inconsistencies with the input text?\n\nWithout linear blending, motion discontinuity artifact such as sudden jump emerge, leading to an increase in Trn FID. Additionally, the absence of linear blending results in a slight decrease in Smt FID, while Smt MMD remains largely unchanged. This findings are consistent with the priorMDM results for reduced transition lengths, as shown in Supplementary Table 7. This suggests that transitional segments generated by machine learning models have less impact on motion semantics compared to those produced by hard-coded signal processing methods such as spherical linear interpolation used in TEACH.\n\n| compositional motion pair generation  \\| | Smt FID ↓ | Trn FID ↓ | Overall FID ↓ | Smt MMD ↓ | Trn MMD ↓ | Overall MMD ↓ |\n| ---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| w/o linear blend  \\| | **0.729** | 2.295 | 1.061 | 3.570 | **5.782** | 4.970 |\n| Ours  \\| | 0.736 | **1.807** | **0.782** | **3.509** | 6.545 | **4.711** |"}, {"review_id": "Nt8iyCuaNJ", "reviewer": "Reviewer_rdSH", "summary": "This paper introduces \"Compositional Phase Diffusion,\" a novel framework for generating long, composite 3D human motions from a series of text prompts. The core contribution is a method for creating smooth, kinematically plausible transitions between different motion segments, a common failure point for existing models. The framework operates in a learned latent phase space designed to capture the periodic dynamics of human motion. It consists of three main components: \n\n1. An Action-Centric Periodic Autoencoder that encodes variable-length motions into a unified phase representation\n2. A Semantic Phase Diffusion Module for text-conditional generation\n3. A Transitional Phase Diffusion Module, which is the key innovation. The TPDM ensures smooth transitions by having adjacent motion clips mutually influence each other's dynamics throughout the parallel diffusion process.\n\nThe authors demonstrate state-of-the-art performance on compositional generation and motion inbetweening tasks on the BABEL-TEACH dataset.", "questions": "1. Please provide an ablation study evaluating performance without the final linear blending step. Given that the core of your method is designed to ensure phase continuity, is this blending still a critical component for hiding artifacts, or is it more of a final polish?\n\n2. The transition motion $X_t$ is defined as covering the second half of $X_p$ and the first half of $X_s$, This seems intuitive when they have similar lengths. How is this handled when their lengths are highly dissimilar (e.g., a 2-second action followed by a 8-second action)? Or is there a upper limit for the time window?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n\n1. The core idea of performing motion composition via diffusion in a learned phase space is noval. The TPDM module, which co-generates and dynamically aligns adjacent clips, is a more principled approach to creating smooth transitions than the post-hoc blending techniques used by prior work.\n\n2. The framework is technically sound. The ACT-PAE is a well-motivated Transformer-based improvement over prior convolutional phase autoencoders, enabling the use of variable-length motion clips.\n\n3. The paper is well-written and clearly structured.\n\n# Weaknesses\n\n1. The framework's complexity is a minor drawback. It consists of three separately trained transformer models (ACT-PAE, SPDM, TPDM), the inference time (~2s per subsequence pair) is acceptable, but not fast.\n\n2. The pipeline concludes with a simple linear blending step to smooth the final decoded segments, which raises questions about the TPDM's effectiveness. If the phase-space alignment were successful, this step should be unnecessary. I would like to know \"how bad\" the results will be without it.", "was_revised": true, "final_justification": "I appreciate the authors’ rebuttal.\n\nThe authors answered my questions regarding the linear blending and motion transition, so I will keep my score.", "rebuttal": "Thanks for your constructive feedback. We carefully consider your comments and address your concerns in our response.\n\n> [Question 1] Please provide an ablation study evaluating performance without the final linear blending step. Given that the core of your method is designed to ensure phase continuity, is this blending still a critical component for hiding artifacts, or is it more of a final polish?\n\nLinear blending is employed to combine the transitioning segment generated by TPDM with the semantic-conditioned segments from SPDM within the overlapping region. However, since the semantic-conditioned segments in our setup do not overlap, linear blending cannot be applied, resulting in motion discontinuity artifacts such as sudden jumps. Therefore, incorporating linear blending with the transition segment is crucial for eliminating abrupt transitions between motion segments.\nThe ablation results without linear blending are shown in the table below. The presence of motion discontinuity artifacts leads to an increase in Trn FID. Furthermore, the absence of linear blending causes a slight decrease in Smt FID, while Smt MMD remains largely unchanged. These observations are consistent with results from priorMDM studies with reduced transition lengths, where transitional segments are generated by machine learning models rather than hard-coded signal processing methods, as detailed in Supplementary Table 7.\n\n| compositional motion pair generation  \\| | Smt FID ↓ | Trn FID ↓ | Overall FID ↓ | Smt MMD ↓ | Trn MMD ↓ | Overall MMD ↓ |\n| ---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| w/o linear blend  \\| | **0.729** | 2.295 | 1.061 | 3.570 | **5.782** | 4.970 |\n| Ours  \\| | 0.736 | **1.807** | **0.782** | **3.509** | 6.545 | **4.711** |\n\n> [Question 2] The transition motion $X_t$ is defined as covering the second half of $X_p$ and the first half of $X_s$, This seems intuitive when they have similar lengths. How is this handled when their lengths are highly dissimilar (e.g., a 2-second action followed by a 8-second action)? Or is there a upper limit for the time window?\n\nThe transition motion $X_t$ covers the second half of $X_p$ and the first half of $X_s$, even when their lengths diﬀer signiﬁcantly. For example, if $X_p$ is 2 seconds and $X_s$ is 8 seconds, $X_t$ will span 5 seconds, covering the last 1 second of $X_p$ and the ﬁrst 4 seconds of $X_s$. Note that the maximum length of $X_t$ remains 8 seconds (the same as maximum motion length), which occurs when both $X_p$ and $X_s$ are 8 seconds long."}, {"review_id": "H33DKCXBNG", "reviewer": "Reviewer_hcJS", "summary": "This paper introduces a novel framework named \"Deep Compositional Phase Diffusion\" to address the challenge of creating long motion sequences with different types of actions, while ensuring the transitions between them are not only smooth but also dynamically plausible and semantically coherent. This work proposes a two-stage alternating framework to solve this problem. It decomposes the motion generation process into two sub-tasks: 1) semantic content generation, and 2) transitional motion modeling, and proposes two modules, the Semantic Phase Diffusion Module (SPDM) and Transitional Phase Diffusion Module (TPDM) to solve these two tasks. Moreover, this work also shifts the diffusion process from the traditional kinematic data space to a learned \"phase latent space\" using the pre-trained Action-Centric Motion Phase Autoencoder (ACT-PAE).", "questions": "Though this work is quite interesting and the experiments seems to be promising, I have to stay \"Borderline accept\" currently. The authors should answer the following questions:\n1. The ACT-PAE is the cornerstone of the entire framework. How does it perform on highly aperiodic, non-cyclic motions (e.g., stumbling, complex object interaction, modern dance)? Have you quantified its reconstruction error on such motions and compared it with more standard VAE-based autoencoders?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The Phase-Space Diffusion Paradigm: This method uses ACT-PAE to decompose motion into parameters describing its core dynamics (frequency, amplitude, etc.), which is similar to the field of signal processing, transforming a time-domain signal to the frequency domain. The use of ACT-PAE allows the subsequent diffusion modules (SPDM and TPDM) to learn on a more abstract and semantically meaningful representation.\n2. Decoupled Control: The design of the explicit separation of the tasks of semantic content generation (handled by SPDM) and transitional continuity assurance (handled by TPDM) is another strength. By assigning these two goals to specialized modules, the framework effectively mitigates interference between tasks, allowing each module to be optimized for its specific objective.\n\nWeaknesses:\n1. Justification and Generalizability of the Phase Representation: A core premise of the framework is that a periodic phase representation is universally superior for human motion. However, a large number of human actions are inherently non-cyclic and transient. Whether this method has optimal generalizability for all types of motion is a question worth exploring.\n2. Potential for Error Propagation: The quality of the final output is critically dependent on the fidelity of the initial ACT-PAE encoding. The ACT-PAE is trained by the reconstruction loss. However, reconstructing the motion well does not guarantee that the resulting latent phase space is optimal for the downstream diffusion task. The authors should discuss this in the revison.", "was_revised": true, "final_justification": "The rebuttal solved most of my concerns, and I've raised my score.", "rebuttal": "Thank you for your constructive feedback. We will provide detailed responses to your questions.\n\n> [Weakness 1] Justification and Generalizability of the Phase Representation: A core premise of the framework is that a periodic phase representation is universally superior for human motion. However, a large number of human actions are inherently non-cyclic and transient. Whether this method has optimal generalizability for all types of motion is a question worth exploring.\n\nWe would like to clarify the core assumption of our framework. Our approach is based on the use of periodic phase representation to improve the smoothness of transitions between generated motion segments, rather than claiming universal superiority in modeling human motion. The concern regarding modeling different motion segments with periodic phase representation is analogous to the use of FFT in image space, where even aperiodic signals can be represented as combinations of phase signals across various frequency bands. Moreover, in our framework, the distribution of frequency in the phase channels is learned rather than fixed, as in FFT, which provides greater flexibility for modeling aperiodic signals. A remaining challenge of the periodic phase representation is accurately modeling subtle motion patterns, such as minor movements related to body balance or object contact, which contribute minimally to the reconstruction loss during ACT-PAE training. This limitation, due to reduced expressiveness, is discussed in the Limitation section (Supplementary Section 5).\n\n> [Weakness 2] Potential for Error Propagation: The quality of the final output is critically dependent on the fidelity of the initial ACT-PAE encoding. The ACT-PAE is trained by the reconstruction loss. However, reconstructing the motion well does not guarantee that the resulting latent phase space is optimal for the downstream diffusion task. The authors should discuss this in the revison.\n\nRegarding the issue of error propagation, our experiments presented in the main paper demonstrate that our model outperforms other approaches, including those based on raw motion diffusion modeling (e.g., PCMDM, PriorMDM) as well as VAE-based methods (e.g., MLD). All experiments were conducted within the context of downstream diffusion tasks, such as Compositional Motion Pair Generation, Long-term Motion Generation, and Motion Inbetweening. The results consistently highlight the advantages of ACT-PAE encoding in these scenarios.\n\n> [Question 1] The ACT-PAE is the cornerstone of the entire framework. How does it perform on highly aperiodic, non-cyclic motions (e.g., stumbling, complex object interaction, modern dance)? Have you quantified its reconstruction error on such motions and compared it with more standard VAE-based autoencoders?\n\nRegarding concerns about reconstruction error between the VAE and our proposed ACT-PAE, we observe that while the VAE achieves lower reconstruction error during training, its reconstruction error is higher during testing. Our experimental results further indicate that the testing reconstruction error of the encoding method shows a stronger correlation with performance on downstream diffusion tasks than the training reconstruction error."}, {"review_id": "u38ErRV1bQ", "reviewer": "Reviewer_QAf8", "summary": "This paper proposes a novel framework called Compositional Phase Diffusion for generating long motion sequences with smooth transitions and semantic alignment. The framework addresses the challenge of awkward transitions between motion segments in existing motion generation models by introducing two key modules: the Semantic Phase Diffusion Module (SPDM) and the Transitional Phase Diffusion Module (TPDM). These modules operate within the latent space of a pre-trained Action-Centric Motion Phase Autoencoder (ACT-PAE) to incorporate semantic guidance and phase details from adjacent motion clips during the diffusion process. The framework can handle an arbitrary number of motion segments of varying lengths simultaneously, ensuring smooth and coherent transitions between clips. The paper demonstrates the effectiveness of the proposed framework through extensive experiments on tasks such as compositional motion generation and motion inbetweening. The experimental results show significant improvements in motion realism and contextual alignment, outperforming several state-of-the-art methods. The framework's flexibility and scalability are also highlighted, as it can accommodate various application scenarios.", "questions": "1. How would the proposed framework perform on other motion datasets, such as HumanML3D or AMASS? Are there any dataset-specific factors that might affect the framework's performance? How are the performances compared to recent motion generation methods like MoMask? \n\n2. How were the phase mixe parameter values determined? Could a learnable parameter or adaptive mechanism for r further improve motion quality and transition smoothness?\n\n3. The motion inbetweening experiments focus on relatively short transition lengths (60-180 frames). How would the framework perform for longer inbetweening tasks, such as generating transitions spanning several seconds or more?", "limitations": "What is the limitations of phase-based method? Does it perform better on locomotions rather than other motions?", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths: \n\n1. The proposed Compositional Phase Diffusion framework introduces a novel approach to long-term motion generation by combining semantic guidance and phase dynamics information from adjacent motion segments. This effectively addresses the issue of awkward transitions between motion clips, which is a significant challenge in existing methods.\n\n2. The paper presents comprehensive experimental evaluations on the BABEL-TEACH dataset, demonstrating the framework's superiority over other state-of-the-art methods in terms of motion realism (FID) and contextual alignment (MMD). For example, in compositional motion pair generation tasks, the proposed method achieves an overall FID score of 0.782 and an overall MMD score of 4.711, outperforming priorMDM, TEACH, and other methods.\n\n3. The framework's ability to process multiple motion segments of varying lengths simultaneously is a notable advantage. This flexibility allows it to handle complex motion sequencing tasks and facilitates motion inbetweening tasks by denoising multiple motion segments in parallel.\n\n4. The proposed framework provides a scalable solution for various motion generation tasks, including long-term compositional generation and motion inbetweening. By introducing the ACT-PAE, SPDM, and TPDM components, the framework offers new insights into motion phase modeling and diffusion-based motion generation, advancing the field of human motion synthesis.\n\n\nWeaknesses: \n\n1. The paper relies on the BABEL-TEACH dataset for training and evaluation, which may limit the generalizability of the proposed method. The authors acknowledge that BABEL-TEACH is the only dataset providing subsequence pair annotations, but this could restrict the applicability of the framework to other motion datasets.\n\n2. The paper proposes a phase mixing parameter r to balance semantic denoising and transitional denoising, but it does not thoroughly explore the impact of different r values on motion quality and transition smoothness. A more comprehensive ablation study on this parameter would strengthen the paper.\n\n3. The paper primarily uses FID and MMD for evaluation, but these metrics may not fully capture the nuances of motion quality, particularly aspects like motion dynamics and physical plausibility. Incorporating additional metrics, such as joint velocity errors or root mean squared jerk, could provide a more comprehensive evaluation.", "was_revised": true, "final_justification": "After reading the rebuttal, most of my concerns are addressed. It would be better if the authors could provide some comparisons to general text2motion methods (e.g., MoMask/DartControl) to demonstrate the superiority of the proposed methods, even in some restricted conditions. This would probably provide a new direction to the researchers in this field to have knowledge of how more annotations could help tackle the general task of text2motion.", "rebuttal": "Thank you for your valuable feedback. We will address the concerns regarding the identified weaknesses and respond to your questions accordingly.\n\n> [Weaknesses 1] The paper relies on the BABEL-TEACH dataset for training and evaluation, which may limit the generalizability of the proposed method. The authors acknowledge that BABEL-TEACH is the only dataset providing subsequence pair annotations, but this could restrict the applicability of the framework to other motion datasets.\n\nThis weakness is addressed in Supplementary Section 5.2. We believe that developing datasets of subsequence motion pairs annotated across different modalities could further expand the potential applications of our approach.\n\n>  [Weaknesses 2] The paper proposes a phase mixing parameter r to balance semantic denoising and transitional denoising, but it does not thoroughly explore the impact of different r values on motion quality and transition smoothness. A more comprehensive ablation study on this parameter would strengthen the paper.\n\nAblation studies on different phase mixing parameters are provided in Supplementary Section 3.5. The impact of these parameters on motion quality is presented using the FID metric in Supplementary Table 5, while their influence on transition smoothness is evaluated using the RMS-Jerk and NPSS metrics in Supplementary Table 6. \n\n>  [Weaknesses 3] The paper primarily uses FID and MMD for evaluation, but these metrics may not fully capture the nuances of motion quality, particularly aspects like motion dynamics and physical plausibility. Incorporating additional metrics, such as joint velocity errors or root mean squared jerk, could provide a more comprehensive evaluation.\n\nWe would like to clarify that root mean squared jerk (RMS-Jerk) has been included as an evaluation metric for the Motion Inbetweening experiment, as reported in both the main paper and the supplementary materials.\n\n>  [Questions 1] How would the proposed framework perform on other motion datasets, such as HumanML3D or AMASS? Are there any dataset-specific factors that might affect the framework's performance? How are the performances compared to recent motion generation methods like MoMask?\n\nOur proposed framework requires datasets with subsequence pair annotations for training, which are not available in HumanML3D and AMASS. Consequently, all comparative evaluations are conducted solely on the BABEL-TEACH dataset, which limits our ability to explore dataset-specific factors. \nRegarding text-based long motion generation methods such as MoMask, their application and experimental settings differ significantly from ours. Notably, MoMask does not allow for specification of motion duration for individual segments, whereas our framework requires each conditioned segment to have a specified duration. This fundamental difference renders direct comparison infeasible and potentially unfair. Therefore, these text-based long motion generation methods are not included in our experimental comparisons.\n\n> [Questions 2] The paper proposes a phase mixing parameter r to balance semantic denoising and transitional denoising, but it does not thoroughly explore the impact of different r values on motion quality and transition smoothness. A more comprehensive ablation study on this parameter would strengthen the paper.\n\nThe phase mixing parameters were selected based on the ablation study presented in Supplementary Section 3.5. Although these parameters are fixed during inference in our current framework, it is possible that introducing learnable parameters or an adaptive mechanism could further enhance performance. However, implementing such features would likely require an additional training stage and a more complex inference pipeline, which is beyond the scope of this work. We plan to add a paragraph in the future work section to discuss this potential direction. \n\n> [Questions 3] The motion inbetweening experiments focus on relatively short transition lengths (60-180 frames). How would the framework perform for longer inbetweening tasks, such as generating transitions spanning several seconds or more?\n\nThe duration of the inbetweening segment in the motion inbetweening experiment is determined by the minimum and maximum motion lengths set during the training process. For reference, 180 frames at 30 FPS corresponds to 6 seconds, which is already considered a long segment. While it is technically feasible to generate longer inbetweening segments, the testing dataset contains few sequences exceeding 180 frames, making evaluations for longer segments statistically unreliable (e.g., the FID metric returns NaN for the CMIB experiment at 240 frames). \n\n> [Limitations] What is the limitations of phase-based method? Does it perform better on locomotions rather than other motions?\n\nThe limitations of our proposed phase-based method are discussed in Supplementary Section 5. Specifically, the smoothness and periodic regularization incorporated in our approach may reduce model expressiveness and present challenges for tasks that require modeling subtle motion patterns, particularly those that contribute minimally to the reconstruction loss during ACT-PAE training. Additionally, we did not observe significant differences in model performance across various motion types, such as locomotion or aperiodic motion."}]

Author rebuttal:

Meta-review: During the review process, the reviewers raise some concerns regarding the preference of phase modeling over learned motion space diffusion alone, the lack of comparisons with more recent baseline methods, the evaluation metrics, the use of a simple linear blending step for final smoothing, motion transition, etc. Most of these issues were adequately addressed in the rebuttal and acknowledged by the reviewers. Ultimately, this paper received uniformly positive scores, and all reviewers recommend its acceptance in light of the significance of the research direction and the strength of its contributions.

The ACs consider the generation of compositional and transitional motions to be a key area of research. Specifically, the proposed Compositional Phase Diffusion framework introduces a scalable and novel approach to long-term compositional motion generation that improves transitions, outperforms state-of-the-art methods in motion realism and contextual alignment, and demonstrates consistent advantages across multiple motion synthesis tasks. The framework’s decoupled SPDM and TPDM modules reduce task interference and, together with parallel processing of multiple motion segments, enable flexible sequencing and smoother, semantically consistent transitions compared to hard-coded methods. Considering the strength of its contributions, the AC recommends acceptance as an oral presentation. The decision was discussed and confirmed with the SAC.

Authors should incorporate the feedback into the final version, and include comparisons with general text-to-motion methods to demonstrate how additional annotations can contribute to addressing the broader text-to-motion task.
Decision: Accept (oral)