Title: DGH: Dynamic Gaussian Hair
Venue: NeurIPS 2025
Accepted: yes
Keywords: Dynamic hair

Hair deformation

3D Gaussian Splatting
Abstract: The creation of photorealistic dynamic hair remains a major challenge in digital human modeling because of the complex motions, occlusions, and light scattering. Existing methods often resort to static capture and physics-based models that do not scale as they require manual parameter fine-tuning to handle the diversity of hairstyles and motions, and heavy computation to obtain high-quality appearance. In this paper, we present Dynamic Gaussian Hair (DGH), a novel framework that efficiently learns hair dynamics and appearance. We propose: (1) a coarse-to-fine model that learns temporally coherent hair motion dynamics across diverse hairstyles; (2) a strand-guided optimization module that learns a dynamic 3D Gaussian representation for hair appearance with support for differentiable rendering, enabling gradient-based learning of view-consistent appearance under motion. Unlike prior simulation-based pipelines, our approach is fully data-driven, scales with training data, and generalizes across various hairstyles and head motion sequences. Additionally, DGH can be seamlessly integrated into a 3D Gaussian avatar framework, enabling realistic, animatable hair for high-fidelity avatar representation. DGH achieves promising geometry and appearance results, providing a scalable, data-driven alternative to physics-based simulation and rendering.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a framework to jointly simulate and render 3D hair models. For the simulation part, this work proposes a coarse-to-fine strategy to separately learn the primary and secondary hair dynamics in a data-driven manner, and for the rendering part, this work extends strand-based 3DGS by introducing additional networks to model time-varying appearance under the dynamic scenarios. Experiments show promising results for both the hair simulation and rendering compared to the baselines.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths**\n\n[Improvements over the previous version]\n\nI reviewed an earlier version of this work submitted to a different venue. Compared to the previous version, the current manuscript incorporated several modifications I’m glad to see and want to highlight here:\n- The demonstration of some complex hairstyles such as the curly ones and ponytails, which helps to show the generalizability of the work to those hairstyles that are more close to our real life.\n- A more detailed analysis and discussion of the run-time performance. Though the reported performance is still nonoptimal (which is understandable), I’m glad to see the authors tone down their previous claim on building a “fast and lightweight framework” but focus on the joint learning of hair dynamics and appearance.\n- More technical details included that help address many of my previous concerns. Although the code of this work seems not to be released, I think it contains enough information to reproduce it.\n\n### **Weaknesses**\n\nSee my questions listed below.\n\nQUESTIONS:\n[Dynamic hair model vs. hair simulator]\n\nI’m glad to see the authors have included a discussion of prior work on hair simulation. However, I still have some concerns which I will list below:\n- Regardless of performance, one claim the authors made on those simulation methods is the need for iterative tuning of material parameters to achieve stable and realistic hair simulation. I agree with it, but I don’t think the proposed dynamic hair model can handle hairstyles with different material properties (e.g., stiffness) either, especially given the absence of any explicit conditioning on those material properties. From this perspective, I would argue that existing hair simulation models are still more generalizable given their incorporation of physics laws.\n- Second, the paper critiques prior real-time simulation approaches for being limited to \"simple or quasi-static deformations.\" However, I question whether the proposed framework is capable of capturing those realistic and energetic deformations. According to Figure 2 in supplemental, I thought the included head motions may come from some mocap data, but it looks like they are only some hand-crafted 3D rotations. While the supplemental video includes some “increased velocity” examples, the motions still appear quite basic and not representative of realistic, high-energy movements. As such, the current evaluation does not convincingly demonstrate the robustness of the proposed dynamic hair model under realistic motion conditions. Including experiments driven by some mocap data would provide a much stronger test of the model’s capabilities.\n- Finally, I would argue that existing hair simulators (both physics-based and neural-based) should be capable of handling simulations under these relatively subtle head movements, therefore still questioning the necessity of the proposed dynamic hair model.\n\nWhile I still hold these concerns regarding the dynamic hair model, considering the improvements made during this round of submission, I would lean toward acceptance of this work but still encourage the authors to consider and address these physics-related concerns listed above.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a method, DGH, for simulation-free hair reconstruction, rendering, and animation. Stretched 3D Gaussian splats are concatenated to approximate hair strands to model the geometry and appearance of the hair. To capture the deformation and dynamics of the hair, a 3D CNN is trained on a synthetic dataset with different grooms to predict coarse deformation of splats conditioned on the head pose and body mesh, followed by an MLP to refine the dynamics based on previous states. For better appearance fidelity, another MLP is learned on multi-view data to predict the appearance properties of 3D Gaussians. The authors conduct thorough experiments in terms of deformation reconstruction and appearance modeling, showing clear superiority of DGH.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n- Making 3DGS-based hair representation animatable is a challenging and meaningful task. DGH is a concise and effective method that fulfills this goal.\n- It is a reasonable experiment design to evaluate deformation and appearance separately. The authors animate 3DGS and GaussianHairCut with the same learned deformation model, showing the effectiveness of the appearance modeling components.\n- Figure 5 is a great plot to show the quality of learned hair dynamics.\n- The process of data generation and SDF computation is reported in detail in the supplementary document, making this method more reproducible.\n- The comparison of FPS and visual quality with different numbers of strands is very informative, showing the value of this method in real-world applications.\n# Weaknesses\n- The naming of the \"coarse stage\", \"fine stage\", and \"the second stage\" (line 70 and 141) is a bit confusing. A reader may confuse \"the fine stage\" with \"the second stage\", not realizing that they are different things. \n- Curvature-based Gaussian blending does not make much sense to me. In reality, the visibility of a segment does not change by its local curvature. Also, the visual difference isn't obvious in Figure 7 and the video, although PSNR is improved with it.\n\nQUESTIONS:\n- How robust is this method to novel head poses and movements, such as a sudden downward movement of the entire head and body?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a novel data-driven framework for photorealistic dynamic hair modeling and rendering, rather than physics-based simulation. The author creates a synthetic dynamic hair dataset and utilizes it for training. To model dynamic hair, a coarse-to-fine strategy is first proposed. The coarse stage yields (head) pose-driven deformation rather than rigid transformation based on point cloud and volume deformation, and the fine stage utilizes cross-attention on temporal feature sequence to enable temporally consistent hair animation. Then, the author leverages 3DGS for photorealistic modeling and rendering, together with a splats adjustment network to align the hair appearance with dynamic motion.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The proposed pipeline is a good contribution to dynamic hair modeling, offering a scalable, data-driven alternative to physics-based methods. \n2. The overall idea of hair dynamic modeling and photorealistic appearance optimization is straightforward and technically sound. \n3. The framework is claimed to be able to integrate with 3D gaussian avatar, which increases its application potential. \n\n**Weaknesses**\n1. Some presentations and descriptions are not clear and poorly satisfactory:\n\n(a) From line 161 to line 163, and in equation (1), no clear formula for $L_{point}$ and $L_{SDF}$\n\n(b) Line 205, what is the definition of tangent vector $t$? Is it in equation (7)? If so, the order of explanation is a bit confusing.\n\n(c) In section 3.2, Hypothesis and Curvature-based Gaussian blending parts would be better placed before Line 214.\n\n2. What is the training strategy? Is the model trained end-to-end? I cannot find the detailed elaboration.\n\n3. Simulated training data may not fully capture real-world hair complexity (e.g., lighting variations, fine-scale interactions). Results are validated on synthetic sequences; real-world performance is untested.\n\nQUESTIONS:\nSee Weaknesses\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents Dynamic Gaussian Hair(DGH), a two-stage learning-based framework for dynamic hair modeling and rendering. The first stage introduces a coarse-to-fine hair deformation network that predicts temporally coherent hair motion driven by head poses. It starts from a canonical hair model and uses SDF-based collision loss to avoid hair-body interpenetration in the coarse stage, followed by a flow-based fine stage that captures high-frequency temporal dynamics using cross-frame attention.\n\nThe second stage focuses on dynamic appearance modeling. Each hair strand is represented by cylindrical Gaussian primitives, and a lightweight MLP predicts per-frame appearance parameters(color, scale, opacity) conditioned on geometric cues such as hair tangents and view direction. A curvature-aware blending scheme is introduced to improve appearance continuity in highly curved regions.\n\nCompared to simulation-based methods like XPBD, the proposed deformation model significantly improves efficiency. In terms of appearance, the method extends prior static approaches to dynamic scenes, achieving higher rendering quality under motion and occlusion.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n-Both the deformation and appearance modules are tailored to the dynamic setting: the deformation model incorporates temporal refinement via cross-frame flow, while the appearance model dynamically adjusts Gaussian parameters based on hair tangents.\n-Extensive experiments support the paper's claims, including ablation studies on each component(e.g., SDF loss, attention, tangent features), as well as comparisons with baselines like 3DGS and Gaussian Haircut.\n-The system shows clear advantages over simulation-based methods(e.g., XPBD) in terms of runtime efficiency and over prior static appearance models in terms of visual fidelity under motion.\n\n\nWeaknesses:\n-Despite using SDF constraints in the coarse stage to avoid hair-body interpenetration, high-speed motion in the demo still causes noticeable penetration artifacts. It is unclear why similar constraints were not applied to the fine stage, where temporal dynamics are modeled.\n-The temporal refinement module only uses two previous frames(t-1 and t-2), which limits the model to learning first-order motion(velocity). Higher-order dynamics(e.g., acceleration, oscillation) may not be fully captured, and the paper does not discuss the impact of temporal window size.\n\nQUESTIONS:\nPlease see the weakness above.\n\nLIMITATIONS:\nA key limitation of the proposed method is its reliance on ground-truth dynamic hair point clouds for supervised training. Such annotations are only feasible to obtain in synthetic settings using simulation, making the method difficult to apply directly to real captured data. Additionally, while the coarse stage uses SDF-based constraints to reduce hair-body interpenetration, the fine stage lacks similar regularization. As a result, high-speed head movements can still cause noticeable penetration artifacts in dynamic sequences. Finally, the temporal refinement module only leverages two previous frames, which restricts the model to learning first-order dynamics. This may limit its ability to capture higher-order temporal effects.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-06"}