Title: Deep Compositional Phase Diffusion for Long Motion Sequence Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Motion Generation

Phase Autoencoder

Long Term Motion Sequence Generation

Motion 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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nWhat is the limitations of phase-based method? Does it perform better on locomotions rather than other motions?", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nThough 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n# 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS AND WEAKNESSES:\n#### **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.\n\nQUESTIONS:\nSince 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}