Title: SnapMoGen: Human Motion Generation from Expressive Texts
Venue: NeurIPS 2025
Accepted: yes
Keywords: Human Motion Generation
Abstract: Text-to-motion generation has experienced remarkable progress in recent years. However, current approaches remain limited to synthesizing motion from short or general text prompts, primarily due to dataset constraints. This limitation undermines fine-grained controllability and generalization to unseen prompts. In this paper, we introduce SnapMoGen, a new text-motion dataset featuring high-quality motion capture data paired with accurate, \textit{expressive} textual annotations. The dataset comprises 20K motion clips totaling 44 hours, accompanied by 122 detailed textual descriptions averaging 48 words per description (vs. 12 words of HumanML3D). Importantly, these motion clips preserve original temporal continuity as they were in long sequences, facilitating research in long-term motion generation and blending. We also improve upon previous generative masked modeling approaches. Our model, MoMask++, transforms motion into \textbf{multi-scale} token sequences that better exploit the token capacity, and learns to generate all tokens using a single generative masked transformer. MoMask++ achieves state-of-the-art performance on both HumanML3D and OmniMotion benchmarks. Additionally, we demonstrate the ability to process casual user prompts by employing an LLM to reformat inputs to align with the expressivity and narration style of SnapMoGen.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces OmniMotion, a new human motion dataset and generation framework. It presents a large scale collection of continuous motion capture sequences paired with rich, paragraph length textual descriptions that cover action details, style, and context. The paper also proposes MoMask++, a multi-scale residual vector quantization and unified masked Transformer approach for tokenization and parallel generation of motion sequences. Experiments demonstrate state-of-the-art performance on benchmarks and ablation studies identify optimal quantization and model configurations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n(1) The paper delivers a large-scale, richly annotated human motion dataset.\n(2) Proposed MoMask++ model that fuses multi scale RVQ and a unified masked Transformer to reduce token usage while improving reconstruction quality.\n(3) Leverages LLM-based prompt augmentation both to enrich dataset annotations and to harmonize inference inputs with training style.\n(4) Presents extensive benchmark comparisons and ablation studies that clearly demonstrate the dataset’s value and the model’s performance gains.\n\nWeaknesses:\n(1) During dataset generation, augmented captions rely mostly on simple synonym swaps and lack deeper structural or semantic variation\n(2) The shift from MoMask to RVQ, while effective, represents a modest, incremental improvement over existing quantization approaches.\n(3) Inference appears to depend on LLM-expanded, detailed prompts—raising questions about the model’s ability to generate diversity results.\n\nQUESTIONS:\n(1) The paper relies on LLM‐based caption augmentation, yet the examples suggest mostly simple synonym swaps. Could the authors include an ablation study that compares training with and without these augmented captions to quantify its actual impact on model performance? If such an experiment shows a clear gain in generation quality or retrieval metrics, my confidence in the dataset’s contribution would increase.\n\n(2) During inference, prompts are likewise expanded via an LLM to match the training style. How do you verify that these expanded prompts faithfully capture the user’s intent without introducing bias? Have you measured whether the added detail reduces motion diversity? Providing some visualization on diversity vs. prompt length would help clarify the model’s robustness and could strengthen the paper.\n\nLIMITATIONS:\nPlease add a discussion of the limitations, for example, whether LLM-annotated text is truly expressive or merely a longer, semantically equivalent restatement.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles the problem of human motion synthesis from text. The authors propose a new motion dataset called OmniMotion which consists of around 20k motion sequence each with 6 detailed textual description. Different from preivous motion datasets whose text description is brief and general, OmniMotion provides highly expressive text descriptions for each motion enables the potential of fine-grain control in motion synthesis. Building upon the SOTA method MoMask, the authors further introduce MoMask++. The main contribution of MoMask++ is the multi-scale motion quantization which enables better motion reconstruction with less tokens.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- The overall quality of the writting is good. The arguments are clear and valid. \n- The motivations of the new dataset as well as the multi-scale motion quantization are convincing.\n- The highly detailed text description provided by the new dataset can be very helpful to the future reseach of text-to-motion.\n\n**Weakness**\n- The technical contribution is limited. The proposed MoMask++ is similar to the MoMask pipeline with differences on residual quantization and text encoding. And the only contribution is the multi-scale residual quantization. There is no novelty on the text encoding since it simply uses an existing model. \n- The performance of the method on HumanML3D dataset is either marginally better or even worse comparing to MoMask. While on OmniMotion dataset, the proposed method is consistently better. Are there any explainations on this? Is it because the T5-based text encoder better leverages the highly detailed descrition of OmniMotion dataset? If so, what is the benefits of the multi-scale residual quantization in text-to-motion?\n\nQUESTIONS:\nL202, how does the CFG used in an auto-regressive model?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MoMask++, a multi-scale masked transformer for text-to-motion generation, alongside the OmniMotion dataset with detailed text descriptions. While the work shows solid technical execution and experimental effort, several concerns regarding novelty and methodology limit its contribution.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The OmniMotion dataset with extended text descriptions fills a practical gap and could benefit the community.\n2. The paper is well-structured and easy to follow, with comprehensive experiments and useful ablation studies.\n3. The LLM-based prompt rewriting feature shows thoughtful consideration for real-world usability.\n4. The multi-scale residual quantization framework demonstrates some improvements.  \n\nMain Concerns:\n1. Limited Novelty. This is the main concern with the paper. The core contribution appears to be primarily an engineering combination of existing approaches. While the multi-scale extension of MoMask is reasonable, similar multi-scale strategies have been widely explored in computer vision and motion generation (e.g. MoFusion, T2M-GPT) and cross-scale attention is fairly standard. The improvements shown in Tables 2 and 3 are relatively modest compared to MoMask, which doesn't strongly validate the method's effectiveness.\n\n2. Methods.\na. The paper employs summation for multi-scale information fusion, but it remains unclear whether this is the optimal approach. The use of equal-weight summation may lead to overemphasis of low-frequency information (global patterns) while underweighting high-frequency information (local details). A more detailed analysis of the post-fusion features is needed.\nb. When concatenating sequences of very different lengths (e.g., 2 vs 80 tokens), the attention mechanism might naturally favor longer sequences.  Some discussion of this issue would strengthen the paper. Also, It's unclear how positional encodings work after multi-scale concatenation, which seems important for preserving temporal relationships.\n\n3. Dataset: Figure 1 shows the text annotations for motion, which include a series of continuous action descriptions. However, these are merely descriptions of consecutive sub-action contents, lacking alignment with time steps. Therefore, we cannot determine the speed or pace of each sub-action from these descriptions. I believe supplementing this information would be beneficial for text-to-motion generation.\n\n4. Minor Issues\na. The foot-ground contact issues mentioned in limitations suggest room for improvement in basic motion quality.\nb. In Figure 2, the Motion VQ code in (a) and the Motion embedding in (c) look too similar, which could cause confusion. A more distinguishable visualization approach should be used.\n\nQUESTIONS:\nQuestions to Address\n\nNovelty: How does your multi-scale approach fundamentally differ from prior works like MoFusion or T2M-GPT, beyond standard cross-scale attention? Can you better justify the novelty given the modest performance improvement over MoMask?\n\nMulti-Scale Fusion: Why was summation chosen for fusing multi-scale features? Have you explored alternative, weighted fusion methods to avoid over-emphasizing low-frequency information? What analysis can you provide on the post-fusion features?\n\nAttention & Positional Encoding: How does the attention mechanism handle concatenated sequences of very different lengths (e.g., 2 vs. 80 tokens)? How do positional encodings function after this multi-scale concatenation to preserve temporal order?\n\nDataset Timestamps: Could you enhance the dataset by aligning text descriptions with timestamps to allow for motion speed and pace control?\n\nMotion Quality: How do you plan to address the foot-ground contact issues mentioned in the limitations?\n\nVisualization: Can you revise Figure 2 to make the visualizations for \"Motion VQ code\" and \"Motion embedding\" more distinct to avoid confusion?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
The authors propose a new text2mtoion dataset and revisit the Momask model to get better results. The dataset is useful and meaningful. It comprises 20K motion clips totaling 44 hours, accompanied by 122 detailed textual descriptions averaging 48 words per description (vs. 12 words of HumanML3D).

STRENGTHS AND WEAKNESSES:
Strengths:
1. Dataset is good. The t2m field nees more high quality data.
2. The authors provides extensive experiments results and visualization results, which are not observed before.
3. The evaluation process is standardized and meaningful. 

Weaknesses:
1. The paper do not propose a new generation model or architecture, but an incremental improvment on the existing method.
2. The rewriting process is intuitive, since 2/3 of the texts are generated by LLM. And the generated texts have it own bias. I do not think this should be highlighted in the abstract and it is just a trick everybody knows. 
3. Although the dataset is useful, the paper do not provide any new insight for me. It is more like an engineering project. Thus, I will give a boarderline this time.

QUESTIONS:
1. How the author handle the halluciation problem when only using the text as the prompt?

LIMITATIONS:
N/A
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03