Title: Absolute Coordinates Make Motion Generation Easy
Venue: NeurIPS 2025
Accepted: no
Keywords: Text-to-Human Motion Generation

Diffusion Model

Human Motion Representation
Abstract: State-of-the-art text-to-motion generation models rely on the kinematic-aware, local-relative motion representation popularized by HumanML3D, which encodes motion relative to the pelvis and to the previous frame with built-in redundancy.
While this design simplifies training for earlier generation models, it introduces critical limitations for diffusion models and hinders applicability to downstream tasks.
In this work, we revisit the motion representation and propose a radically simplified and long-abandoned alternative for text-to-motion generation: absolute joint coordinates in global space.
Through systematic analysis of design choices, we show that this formulation achieves significantly higher motion fidelity, improved text alignment, and strong scalability, even with a simple Transformer backbone and no auxiliary kinematic-aware losses.
Moreover, our formulation naturally supports downstream tasks such as text-driven motion control and temporal/spatial editing without additional task-specific reengineering and costly classifier guidance generation from control signals.
Finally, we demonstrate promising generalization to directly generate SMPL-H mesh vertices in motion from text, laying a strong foundation for future research and motion-related applications.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper challenges the prevailing paradigm in text-to-motion generation, which heavily relies on the local-relative, kinematic-aware motion representations popularized by the HumanML3D dataset. The authors argue that this standard representation, while beneficial for earlier models, imposes critical limitations on modern diffusion-based approaches and complicates downstream applications. The central contribution is a return to a \"radically simplified and long-abandoned\" alternative: representing human motion using absolute joint coordinates in a global 3D space.  \nThe proposed method, the Absolute Coordinates Motion Diffusion Model (ACMDM), is built upon a simple Transformer backbone. The paper's focus is not on architectural novelty but on a systematic investigation of the design choices that enable this simplified representation to succeed. Through extensive ablation studies, the authors demonstrate that a specific combination of techniques is key: using absolute coordinates with AdaLN conditioning for text and timestep embeddings, and adopting a velocity (v) prediction objective for the diffusion process. The use of a motion autoencoder to operate in a latent space is also shown to further improve performance.  \nThe authors claim three primary contributions. First, that this simplified framework achieves state-of-the-art performance on standard text-to-motion benchmarks (HumanML3D and KIT-ML) without requiring complex architectural components or auxiliary kinematic losses. Second, that the absolute representation inherently simplifies and improves performance on downstream tasks like motion control and editing, removing the need for task-specific engineering and computationally expensive classifier guidance common in prior work. Third, the paper demonstrates the framework's promising generalization to the direct generation of high-dimensional SMPL-H mesh vertex coordinates, a challenging task that has been largely unexplored in the literature.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper’s primary strength is its fundamental re-evaluation of a core assumption that has dominated the text-to-motion field for years. The local-relative, kinematic-aware representation introduced with HumanML3D has become the de facto standard, adopted by nearly all recent state-of-the-art models. This paper boldly argues that this convention is not only unnecessary but detrimental for modern architectures..  \n2. The ablations are well done and most of the design choices are evaluated with rigour. \n3. The paper makes a powerful case for the practical significance of its approach, particularly for controllable motion generation. By representing motion in absolute coordinates, the model's output space is inherently aligned with the absolute spatial control signals (e.g., trajectories, key-pose constraints) that users typically provide. This stands in stark contrast to prior state-of-the-art methods like OmniControl, which are built on relative representations and must resort to complex and computationally expensive techniques like classifier guidance to enforce absolute spatial constraints. \n4. The demonstration of directly generating the motion of SMPL-H mesh vertices (N) without time consuming IK steps as required in current pipelines\n\nWEAKNESS\n1. Strange Video Format: There is not one video in the supp material but the video is submitted in pieces - with separate folders for different results. This is very unusual. With this kind of presentation, it becomes difficult for me to recommend acceptance at a conference of the caliber of NeurIPS. \n2. Video Results:For motion control, results are only shown on joints - no meshes - comparison with omnicontrol only includes joints, which is very surprising because one of the claims of the paper is that it allows for direct prediction of SMPLH vertices so why use joints\n\n3. Insufficient visualizations - there are a acouple of quantitative visualizations in the main paper. This is a 3D paper and again I don’t see how one can accept a 3D paper with such limited quantitative results.\n\n4. Missing citations: There is some merit in the idea of using absolute coordinates. I agree with the authors that the representation used in MDM and follow ups is redundant and could be refined. But the rep proposed in the paper has been used before - hang et al. MOJO - CVPR 21, Zhao et al. DartControl ICLR 25. Both these papers are not cited. There is still novelty in the current submission as its been used in the context of motion diffusion models but there is a major weakness and again because of this I cannot recommend acceptance. The authors need to properly discuss how their works differs from MOJO DartControl - and why their paper differs signifcantly\n\n5. Questionable Choice of Evaluation Metrics: This weakness is directly related to the first and is equally critical. The authors state, \"We adopt the robust evaluation framework proposed by \". This is a misleading statement. The MARDM paper explicitly argues that the standard evaluation framework is  not robust and, in fact, proposes a new, \"more robust evaluation method\" to rectify its flaws. The critique presented in MARDM is that standard metrics like FID and R-Precision are calculated over all motion features, including redundant ones. This setup, they demonstrate, unfairly penalizes continuous diffusion models (which struggle to perfectly model the noise in these non-essential dimensions) while favoring VQ-based discrete models (whose codebooks enforce consistency across all dimensions). The authors of MARDM show that the performance gap between these model classes narrows significantly when evaluation is restricted to only the essential, animatable features. By choosing to use the older, flawed metrics instead of the \"more robust\" ones proposed in the very paper they cite for their protocol, the authors of the present work undermine the credibility of their state-of-the-art claims. This choice requires strong justification, as it could be perceived as avoiding a more rigorous and fair evaluation.  \n\n6. Insufficient Analysis of Kinematic Plausibility and Failure Modes: The paper correctly notes that early attempts to use absolute coordinates failed due to the generation of \"unnatural, non-human-like motions\". The historical dominance of relative representations stems from their ability to implicitly enforce kinematic constraints like constant bone lengths and natural joint limits. This paper claims to overcome this challenge without any explicit kinematic losses, relying on the model to learn these constraints implicitly from data. While the low FID scores suggest that the  distribution of generated motions is close to the real data, this does not guarantee that every individual sample is kinematically plausible. The paper lacks a dedicated analysis of potential failure modes. For instance, does the model ever produce motions with stretched limbs, disjointed body parts, or other physically impossible artifacts, especially when faced with unusual or out-of-distribution text prompts? A qualitative analysis of failure cases, or a quantitative metric tracking bone-length variance, would be necessary to fully substantiate the claim that this fundamental weakness of absolute coordinates has been resolved. This is particularly critical for the mesh generation results, where small joint-level errors can be magnified into significant visual artifacts on the mesh surface.\n\nQUESTIONS:\nPlease see weaknesses sections. The authors could try to answer to point 1,2,4,5, 6 but I don’t think I will change my final rating. In my opinion though the idea may have merit the paper’s presentation especially the video and figures are sub-par.\n\nLIMITATIONS:\nyes to a certain extent.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ACMDM, a motion diffusion model that uses absolute joint coordinates for text-to-motion generation. The model replaces the traditional kinematic-aware local-relative representations with simpler absolute coordinates, achieving high fidelity, better text alignment, and enhanced scalability. ACMDM also excels in downstream tasks like motion control and spatial editing, without requiring extensive re-engineering.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- The idea of using absolute coordinates for motion generation is simple yet effective. The results demonstrate high fidelity and better text alignment compared to previous methods.\n- Nice Visualization: The paper provides well-made visualizations, making it easy to assess the model's performance :)\n- The writing is good - this paper is easy to follow. Most related works are cited and discussed in this paper.\n- I also like the experiment of scaling the models.\n\n## Weaknesses\n\n- Diversity concerns: While the approach improves text alignment, it's unclear whether it leads to a reduction in the diversity of generated motions. The absolute coordinate representation may limit variability.\n\n- (minor) Including some failure cases would help readers better understand the model's limitations.\n\n- (minor)There are some duplicated citations in the references (e.g., SCAMO appears twice...).\n\nQUESTIONS:\nI can imagine that the use of absolute coordinates might limit the diversity of the generated motions. Can the authors provide quantitative results on the diversity of the generated motions? It would be helpful to include both numerical data and visualizations to better illustrate this aspect. Additionally, how does the use of absolute coordinates affect the training costs? It would be valuable to understand how the computational and memory requirements change with model size and complexity.\n\nLIMITATIONS:\nSee the weakness part in the previous section.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis manuscript introduces ACMDM, a framework for human motion generation model that employs transformer diffusion models. The innovation of this work lies in that, compared with local-relative motion representation popularized by HumanML3D, the utilization of transformer improves text alignment, and strong scalability, even with a simple transformer backbone and no auxiliary kinematic-aware losses.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The article proposes using a transformer approach to enhance the model's ability to handle absolute joint coordinates in the global space.\n2. This paper clearly presents the problem and the corresponding solution. \n3. The paper is easy to follow.\n\n\nWeaknesses:\n\nIncomplete performance analysis: Although SOTA results are achieved on most metrics, the paper lacks discussion on underperforming metrics and potential strategies for improvement.\n\nQuestionable novelty of ACMDM: The motivation of ACMDM module is to propose global motion representation for the lack of local relative motion representation, but this paper lacks the discussion on local relative motion representation.\n\nIncomplete theory analysis: Although the implementation process of the model is described in Section 3, this paper lacks theoretical derivation and formula expression of the model.\n\nQUESTIONS:\n1. In terms of the accuracy of the model, as shown in table 3 provided by the author, some indicators of the current method have reached the SOTA accuracy. It is hoped that the author can analyze the reasons why other accuracy is not significant and discuss whether there is a better solution?\n\n2. It can be seen that the author's work is an improvement on HumanML3D, and local relative motion representation is not only in HumanML3D method. If possible, I hope the author can supplement more local motion representation methods and their advantages and disadvantages.\n\n3. In section 3 of the manuscript, the author proposes the method of ACMDM model. Although it gave a good description of the implementation of the model, it lacked rigorous theoretical derivation and formula expression. If possible, I hope the author can supplement and improve the relevant theoretical research to demonstrate the effectiveness of the model.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work, the authors propose using only global joint positions to represent human motions and as the input representation for training generative models. To validate their approach against larger and more established representations that include redundancy and relative information, they conduct an in-depth analysis of several design choices. Their findings demonstrate that this representation is more suitable for a wide range of applications. Finally, for representing motions as meshes, they propose a method based on direct mesh prediction instead of traditional approaches that rely on fitting to parametric models.\n\nSTRENGTHS AND WEAKNESSES:\nIn general, I really liked this work, and I believe it has several strengths that are worth highlighting:\n\n- The quality and clarity of the writing are excellent. The paper is well written, easy to follow, and clearly communicates the ideas.\n- The analysis and ablations presented are thorough and do not leave any major gaps. The experiments address key design choices commonly made in the development of motion diffusion models.\n- The supplementary material is extensive and includes all the necessary details to fully understand the work. It also provides a large number of visualizations that help qualitatively assess the quality of the proposed method.\n\nHowever, there are some weaknesses and questions I would like the authors to clarify:\n\n- As mentioned in Appendix B, the evaluation framework used in this paper is a newly introduced, more robust version that relies on a reduced representation including only fundamental motion components. This raises a concern: were all the baseline methods retrained using this new reduced representation to ensure a fair comparison? If so, is it possible to adapt the proposed representation to the original HumanML3D format and evaluate it using the standard metrics and evaluators? Since all baseline methods were originally designed for the HumanML3D representation, this comparison would be valuable and could offer additional insights.\n- One of the challenges of using global joint positions without kinematic losses is ensuring consistent bone lengths. What happens when the model generates highly out-of-distribution motions? How is bone length consistency maintained with this representation?\n- Regarding the direct mesh generation approach, while I found the results promising, I have concerns about the practical usability of this representation. In real-world applications like video games or animation, motion data is typically applied to rigged meshes, which benefit from relative joint rotations and techniques like linear blend skinning for efficient animation. What is the actual performance and generalizability of the direct mesh generation method? How do the authors envision using this representation in such application scenarios where rigged, animation-ready meshes are the norm?\n- Finally, while the paper provides a large number of qualitative examples, it would have been beneficial to include a user study to assess qualitative quality in a more systematic and reliable way.\n\nQUESTIONS:\nBased on the weaknesses identified in the previous section, I have the following questions and suggestions:\n\n1. The paper adopts a new, reduced motion representation for evaluation. Were all baseline models retrained using this representation to ensure a fair comparison? If not, could the authors provide results using the original HumanML3D format and standard evaluation metrics? This would help clarify how much of the performance gain is attributable to the new representation versus the proposed model itself.\n2. Since the method relies on global joint positions without incorporating kinematic losses, how is bone length consistency maintained? Are there any failure cases, particularly with out-of-distribution motions? Providing quantitative or visual evidence on this aspect would strengthen the validity of the approach.\n3. While the mesh-based output is promising, its practical usability in real-world applications remains unclear. How does the proposed approach integrate with standard rigging pipelines that depend on joint rotations and skinning techniques (e.g., linear blend skinning)? Additionally, what is the computational performance of the direct mesh generation method (e.g., inference time per motion sample)?\n4. Although the paper presents strong qualitative results, a user study would offer a more systematic and objective assessment of perceptual quality. Could the authors consider including such an evaluation?\n\nIf these points are adequately addressed, I would be inclined to increase my overall score for this submission.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}