DGH: Dynamic Gaussian Hair
Authors: Junying Wang
Yuanlu Xu
Edith Tretschk
Ziyan Wang
Anastasia Ianina
Aljaz Bozic
Ulrich Neumann
Tony Tung
Venue: NeurIPS 2025 poster ()
Keywords: Dynamic hair
Hair deformation
3D Gaussian Splatting
Primary area: applications

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.

DGH: Dynamic Gaussian Hair
Junying Wangh
Yuanlu Xu²
Edith Tretschk2
Ziyan Wang'
Anastasia Ianina?
Aljaz Bozic
Urric Neumann'
Tony Tung²
University of Southern California
Meta Reality Labs Research
(4) (b) Deformed
(c) Dynamic Hair
(d) Novel View Synthesis
Figure 1. Dynamic Gaussian Hair (DGH) framework that learns dynamic deformation and photorealistic nieel-view synthesis arbitrary hairstyles driven by head motions, while respecting opper-body collision. At runtime, given a hairstyle and head motion (a), DGH infers initial hair deformations (b), refines the deformations with dynamics (c), and generates 3D Gaussian Splats to achieve photorcalistic novel-view synthesis (d).
Abstract
The creation of photorealistic dynamic hair remains major challenge in digital human modeling because of the complex motions, occlusions, and light scatter- ing. Existing methods often resort static capture and physics-based models that do not scale as they require manual parameter fine-tuning to handle the di- versity of hairstyles and motions, and heavy computation 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: (I) coarse-to-fine model that learns temporally coherent hair motion dynamics across diverse hairstyles; (2) -uided optimization module that learns a dynamic 3D Gaussian representation for hair appearance with support for differentiable rendering. enabling gradient-basee 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 3D Gaussian avatar framework, enabling realistic, animatable hair for high-fidelity avatar repre- sentation. DGH achieves promising geometry and appearance results, providing scalable, data-driven alternative to physics-based simulation and rendering. Our project page: https: //jn::///jj/n// github. lo/paper/dgh
Introduction
Realistic, high-fidelity dynamic hair modeling and rendering are crucial for creating realistic avatars in animation and AR/VR applications. Adding hair dynamics can significantly enhance the realism of animated characters, as hair deformation and high-order motion effects contribute to physical
Work performed during an internship at Meta Reality Labs Research
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Query P/ea FQO
Inn
Plesir
Fpore
fine stage: hair dynamics
Kaan
Novel view synthesis
Stage I: Coarse-to- fo-feee--eeeeeee- dynamic hair modeling
Stage II: Appearance optimization
Figure 2. Framework Overview. DGH learns hair deformation dynamics and photorcalistic appearance. Stage Coarse-to- Dynamic Hair Modeling. The input hair model and upper body are transformed into canonical hair volume hai and pose volume Vyese, respectively. Then, coarsc-to-fing strategy deforms the hair model. At the coarse stage, points p; sampled from the rigidly transformed hair, and the interpolated features from Epsc: Exsir. head pose H. positional encoding (p) are concatenated to predict displacements Ap. producing deformed hair points Phat- The fine stage refines hair deformation with dynamics by estimating flow Fio through cross- attenttotntion between volumetric features from previous frames 1/t-2 and Vi ensuring smooth temporal transitions. Stage II: Appearance Optimization. We train an MLP predict color c', scale s', and opacity a' of 3D Gaussian Splats from features the deformed hair. Differentiable rasterization leverages the appearance model synthesize high-quality renderings that adapt to hair movement and occlusion dynamics.
fed into an MLP M
plausibility. Capturing and modeling these effects, however, poses substantial challenges due to the complexity of hair motion and the intricate interactions among hair strands and head/shoulder. Physics-based hair simulation methods produce high-quality hair dynamics but lack generalization as they require manual parameter fine-tuning per hairstyle (e.g.. ponytail, curly hair) to reproduce realistic deformations (e.g., hair stiffness, density, damping). They also need to model interactions with the environment, such as collisions with shoulders, which are computationally expensive. The models are non-differentiable, and thus unsuitable for scalability with data-driven based solutions. Robust implementation of complex deformations is nontrivial, and unexpected behaviors usually break the experience. Most real-time applications model only simple quasi-static deformations.
The computational cost to create photorealistic digital hair is very high. Realistic rendering requires path tracing of accurate hair modeling, which is translucent, contains occlusions, and accounts for light scattering effects [1]. Hair occlusion patterns change while in motion, posing significant challenges for dynamic hair rendering. The creation of high-quality synthetic hair dataset at scale typically requires a rendering farm with multiple GPUs. Real-time hair rendering engines (Unreal, Unity) compromise quality (e.g., approximate lighting) while still requiring desktop GPU.
Methods relying on Neural Radiance Fields (NeRF) [2, 3] or Gaussian Splatting (3DGS) [4- 7) have enabled effective and high-quality view synthesis, baking complex hair rendering effects under constrained lighting environments. Although they enable photorealistic avatar re-animation, the hairstyle typically undergoes only rigid transformations without non-rigid or dynamic effects, especially for longer hair, due to weak control signals like head poses of a single timestep. Similarly, HaussianHair [8] and Gaussian Haireut [9] reconstruct hair geometry (from static scenes) using strand-aligned 3D Gaussian representations that can be animated with a physics- simulation engine, which in turn is usually computationally intensive and requires tedious parameter tuning.
To address these limitations, we introduce Dynamic Gaussian Hair, novel learning-basee framework that learns hair deformation with dynamics given canonical hairstyles and head motions. By attaching 3D Gaussians hair segments and leveraging the appearance optimization, DGH enables high-quality dynamic hair novel view synthesis at fraction of the computational cost of high-end rendering

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system. Unlike physics-based simulators, which rely on explicit meshes and require additional rendering and conversion steps, our DGH framework only needs head rotation and dense point cloud/pre-trained GS avatar. By converting static hair and upper-body into volume, we enable mesh- free deformation prediction, making our model more compatible with caussian-based avatars and easier to integrate into sorning-basedd re- re-animation pipelines without additional rigging or simulation overhead.
Due to the lack of real captures of dynamic hair deformations with accurate strand tracking, we create a new synthetic dynamic hair dataset from scratch. Hairstyles with strands are modeled from industry experts, and animated with a physics-based simulation engine, Mutiple-view images are generated with a render farm. The dataset includes frame-by-frame hair geometry deformation for various hair styles, along with corresponding upper body geometry and head motions. The 3D models are rendered using fine-tuned hair shaders, resulting photorealistic videos (see Appendices).
As shown in Fig. 2, our dynamic Gaussian hair framework learns hair deformation dynamics and photorealistic appearance. In the first stage, we introduce a coarse-to-fine framework for dynamic hair modeling using a feed-forward network, which, unlike traditional physics-based simulation pipelines, is fully data-driven, differentiable, and free of manual parameter tuning. Specifically, we learn a volumetric implicit deformation model in canonical space that maps static hair geometry to deformed hair, representing hair as a dense point cloud to support diverse hairstyles (e.g.. long, curly, ponytail) and accommodate point, Gaussian, or mesh-based representations. We encode the head and upper body as volumetric features. During training. a random subset of static hair points is supervised using synthetic ground-truth displacements. The coarse stage is indeieddeeedeett and serves as a physically plausible hair deformation initialization, leveraging upper-body features and an SDF constraint to prevent hair-body penetration. In the fine stage, we introduce time-dependent dynamics by predicting flow vectors and applying latent-space cross-attention over previous frames to capture temporal consistency and high-frequency hair motion effects such inertia, oscillation, and damping. In the second stage, we optimize the dynamic hair appearance for novel view synthesis, enabling photorealistic rendering under complex motions and viewpoints. Each hair strand is represented as a sequence of stretched cylindrical Gaussian primitives as shown in Fig. 3, and we introduce a lightweight non-linear model that refines appearance using strand-level tangent information. Our data-driven framework robustly handles arbitrary hairstyle deformation under varying head motions and occlusions, achieving high-fidelity, view-consistent rendering. Our results are best viewed in our supplemental video.
We summarize our contributions as: (1) Learning- volumetric hair deformation: We intro- duce a pose-driven, volumetric implicit deformation model that learns to map static hair to dynamic motion across diverse hairstyles and strand densities. Unlike physics-based methods, our approach fully data-driven, requires no manual parameter tuning, and generalizes to novel head poses. (2) Coarse-to-fine hair deformation dynamics: We propose coarse-to-fine learning framework that first predicts pose-dependent deformations and then refines temporal dynamics via flow- based residual learning. This approach ensures stability and generalizes well to unseen head motions. (3) Differentiable dynamic hair appearance optimization: We represent hair as cylindrical Gaussian primitives and optimize their dynamic appearance with lightweight, strand-guide network. This differentiable formulation enables photorealistic, view-consistent rendering under motion and occlu- sion, and integrates seamlessly with neural avatar systems. We will release our synthetic dynamic-hair dataset to accelerate research on dynamic hair modeling.
2 Related Work
Creating realistic avatars includes several challenging steps. One of them is hair capture and animation. Modeling hair appearance and geometry has been studied in the static and dynamic setting.
Static Hair Modeling Handling diverse hairstyles is challenging. Some methods focus on specific cases like braided [10]. curly [11], or generative approaches [12], while others aim to generalize across static and dynamic settings. They rely on single-view [13-15] or multi-view inputs with orientation maps [16-19], sometimes using simulated strands [18, 20] or geometric heuristics. NeuralStrands [21] constrains 3D surface orientation and uses point-based differentiable rendering [22, 23). NeuralHD- Hair [24] and earlier work [25] leverage 2D supervision with 3D spatial cues. Some methods use real wigs [26], line-based 3D reconstruction [27], OLAT images [28] for static hair modeling. Others
3

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rely on 2D observations to learn hair growth fields [24, 29]. or use optimization and differentiable rendering to extract strands [30]. Beyond image-based approaches, volumetric and neural representa- tions have been explored [2, 31, 32]. Neural Radiance Fields (NeRF) enable high-fidelity rendering for static scenes [2, 31, but suffer from slow rendering speeds. Recent representations like Mixture of Volumetric Primitives (MVP) [33] and 3D Gaussian Splatting (3DGS) [34] improve quality and real-time performance, yet dynamic hair reconstruction remains challenging. INSTA [31] models dynamic neural radiance fields with neural graphics primitives around a face model, focusing on head geometry and efficiency. Sklyarova et al. [32] reconstruct hair using implicit volumes followed by strand-level refinement guided by hairstyle priors. Volumetric hair representations [35, 36] are able to encode 3D global spatial information for static hair modeling. However, all existing work focuses on static hair modeling. leaving the challenge of reanimating static hair for animatable avatars in AR/VR applications an unresolved problem.
Dynamic Hair Modeling
The motion patterns of hair are hard to emulate. One option is hair simulation to advance between adjacent frames with temporal consistency [37]. In [38], per-frame reconstructions of hair strands are aligned with motion paths of hair strands from spatio-temporal slices of video volume. Grid search over different simulation parameters can be used to determine the set of parameters that matches the visual observations best [39]. Current physics-based and neural hair simulators [40-42] enable real-time simulation of hundreds of thousands of strands. However, achieving this on AR/VR devices remains impractical due to their limited GPU memory and processing power. Recent work Quaffure [43] has only handled quasi-static simulation and does not integrate a tractable rendering solution. Dynamic hair can also be modeled with neural volumetric approaches. HVH [44] learns a volumetric representation for dynamic capturing, while NeuWigs [45] accounts for head movements and gravity to produce realistic animations across various hairstyles.
3D Gaussian Head Avatars Modeling realistic 3D head avatars crucial for immersive VR/AR applications. While 3D Gaussian Splatting (3DGS) [34] has enabled high-fidelity head avatars, dynamic hair modeling remains a significant challenge, as realistic hair must exhibit natural motion, deformation, and interactions. 3DGS- based head avatars [5, 6, 46-48] leverage learning- based deformation fields to animate facial expressions and head movements. However, with these models the hair remains static transformation attached to the head, failing to exhibit natural flow or secondary motion. Several works have explored hair modeling within the Gaussian framework. GaussianHair [8] captures strand-level structure using cylindrical Gaussians. Gaussian HairCut [9] combines strand priors with 3DGS for photorealistic rendering, yet both do not model the dynamic hair appearance. To enable realistic hair dynamics in reanimation, propose a dynamic Gaussian hair representation that models arbitrary hairstyle deformation driven by head motion, supporting dynamic hair novel view synthesis.
3 Method
In this section, we present our method for Dynamic Gaussian Hair modeling, as illustrated in Fig. 2. Our framework consists of two main stages that model hair dynamics and hair appearance. In the first stage, we learn pose-dependent hair deformation via a volumetric implicit function conditioned on canonical static hair, head motion, and upper body mesh. This time-independent model supports arbitrary head poses and strand counts, represented as dense point clouds. We further refine temporal hair dynamics by predicting 3D flow vectors and applying latent-space cross-attentin across adjacent frames to ensure temporal consistency. In the second stage, we optimize time-varying hair appearance to handle motion-induced occlusions, enabling photorealistic novel view synthesis. During inference, we predict hair deformations in a recurrent manner based on the previous timesteps' deformations. Our appearance model then performs novel view synthesis, generating accurate and realistic hair appearance across frames.
3.1 Coarse-to-Fine Dynamic Hair Modeling
Modeling realistic hair dynamics is challenging due to complex motion and temporal variation. We address this with coarse-to-fin learning framework, and show the following hypothesis:
Hypothesis Differentiable rendering of dynamic, photorealistic hair requires accurate and temporally consistent hair tracking. Instead of relying on explicit physics-based simulation, we decompose the simulation task into two stages: a coarse stage that learns pose-driven, iine-independent deformation

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for stable initialization, and fine stage that refines high-frequency dynamics through 3D flow prediction and temporal aoss-attention. We validate this hypothesis through the design of Alg. 1.
Algorithm Coarse-to-Fime Dynamic Hair Modeling
Input: Canonical hair Pran proxy mesh (head and shoulders), head poses { 2 "1'11
11-11111
Output: Dynamic hair hair at time
1: Coarse Stage Dimee-independent, pose-driven deformation)
2: Transform canonical hair: Prann +- Trgial (Paan: 76)
3: Voxelize rigid hair and proxy mesh: Vrigid SDF ( Vpose SDF(proxy mesh)
4: Encode features: V + Concat( (proce (Vosee()pee))) Enair (Vrigid))
5: for P; E Prigid do
6: Sample feature: Vi < Interp(VV,
7: Predict displacement: Ap:
H()
8: Update position: p; <- Pe + Ap:
9: end for
10: Set Phair + {p:)
11: Fine Stage (Temporal flow refinement)
12: Voxelize past hair states: v/t=2 vit=1 <- SDF( 2), SDF(P
hair
bair
13: Encode volumes: Ve-2 &- Enair ( (Viuie V--1 f- Enair ( (V2-1)
Cross-attend features: Vnow CrossAttn Att Ve-2)
15: for p; E P do
16: Sample volume feature: f; 6 Inteerp (now,, Pi)
17: Fuse with pose and prior flow: f, < Concat(f;, 76, Flo E(p:))
Predict flow vector: Ap; t- -fr
19: Apply flow: P + P: Ap!
end for
21:
Update outputs: Phair +- {p/}, Fh < {App}
22:
return pt.
Coarse Stage. We first learn pose-dependent hair deformations from a canonical point cloud Pran The canonical hair is rigid transformed using head pose H. to produce hair which is voxelized into an SDF volume hair A proxy mesh of the posed head and shoulders similarly voxelized into Vpose- These volumes are encoded via 3D CNNs Enair and Epose. and concatenated to form the latent feature grid V. For each point P; E pagid we interpolate its feature V; from V, and input along with positional encoding ((P;)) and pose H into an MLP M to predict the displacement Ap,- The updated position p; is computed as p; + Ap;. We train using a random subset of hair points, with a total loss:
huir
Liaat Ap + ASDF
(I)
where fpain the MSE between predicted and ground-truth displacements, and LSDF penalizes collisions with the body mesh using hair-to-body Signed Distance Fields [49]. This stage yields physically plausible, pose-driven deformation that serves as initialization for dynamic refinement. Please see the Appendices for details the 3D CNN architecture.
Fine Stage. The fine stage refines dynamic hair motion by predicting temporally consistent 3D flow vectors. We first voxelize the past hair states pt-2 and pt-1 into SDF volumes V and V which are encoded via Enair into latent feature grids. A cross-attention module aggregates these into a fused volume Vnow- For each point p; E pf-1 we sample its feature f, from Vnow, then concatenate it with the current pose H. and previous flow = to form f, These features are passed to refinement network D to predict the per-point flow vector Ap,, which is applied to update the hair position p We supervise the predicted flow Ft
Hlw using MSE loss against ground-truth flow F
from synthetic data, with a total loss:
CBow MSE(FL (fooss F(T)
(2)
This stage enables temporally consistent hair animation driven by data rather than explicit simulation.
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3.2 Dynamic Hair Appearance Optimization
To achieve realistic rendering quality while ensuring speed and optimizability, we rely on 3DGS [34] to represent hair appearance. 3DGS assigns learnable position p. spatial scale S and rotation R (combined into covariance matrix Σ), opacity o, and color с to each Gaussian G:
Gxx) e (x-x)) (X-Pp) Σ RSS'R
Σ' = JWEW"
To render novel views, Gaussians are projected onto the image plane using a differentiable Surface Splatting method [50]. Given the viewing transform matrix W and the Jacobian J of the affine approximation, the covariance matrix 2' of a 3D Gaussian's corresponding 2D Gaussian is defined as above. To determine pixel's color N-1 2D Gaussians sorted by depth and then composed with a-bblending:
N
i-1
c:04 II a))),,,
(4)
where C, is the color of each Gaussian, and 04 is given by multiplying the opacity with the value of the 2D Gaussian with covariance V' at the pixel location.
Inspired by [8, 9I, we represent each hair strand as a sequence of connected cylindrical Gaussians (Fig. 3) for dynamic hair rendering, eliminating Gaussian densification [34], and fixing the number of primitives across frames. Because our dynamic hair model performs forward warping, we can transform each Gaussian from the canonical model to any frame t, which makes it trivial to directly propagate the Gaussian color e and scale s across time. The rendering is performed using Differentiable Tile Rasterizer [34] R. which yields image
However, Fig. 5 shows that naively propagating the colors of the canonical frame the dynamic se- quence can lead to missing appearance details in dynamic frames due to complex hair self-occlusion and the interaction of light with hair in motion. To address this, we propose a lightweight non-linear model 'D that adjusts Gaussian parameters according the hair dynamics. We train our model using multi- view video sequences under constant lighting. Hair scattering is complex and anisotropic due to strand interactions, as described by the hair BSDF [1, 51], which highlights the importance in appearance. To capture this, we incorporate the hair tangent vector as additional geometric input for optimizing Gaussian parameters. Specifically, we use an MLP D that takes as input the per per-point feature sampled from the encoded hair volume Enair (V ) Gaussian mean p. along with positional encodings for p. t, d and d denotes the view direction.
PI-1
4-11
longth t> radius
Pies
Cylindrical Gaussian primitives
strand tangents
Hair strand
Hair tangent
Gaussian bair
Figure Hair Representation. We show differ- ent hair representations with tangent vectors and curvature. For Gaussian hair, we attach cylindri- cal Gaussian primitive [8] to each segment with length much greater than its radius.
These enable to express anisotropic effects. outputs modified color c' in the form of spherical harmonic (SH) coefficients, scales s' and opacity a':
c' o' = D (Va P)) E(p), E(t), F(d))
(5)
As present in Fig. 2, the predicted appearance is then rendered through differentiable rasterization R. to produce the final image Fig. shows that our model enhances the hair appearance, achieving realistic renderings under consistent lighting. For training, we use common L,, SSIM and LPIPS [52] reconstruction losses:
L= Ligh Lesim Alpips
(6)
Hypothesis Modeling hair strands with a fixed number of discrete Gaussian primitives leads to visual discontinuities in regions of high curvature. We hypothesize that local curvature is an effective cue for adaptively blending neighboring Gaussians to improve visual continuity.

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Curvature-based Gaussian blending, Unlike continuous surfaces, Gaussian hair treats each segment as an independent shading unit, each with its own tangent vector t.. In high-curvature regions, large angular differences between adjacent tangents (t,, 1++1) can cause shading discontinuities. Although increasing segment density can alleviate this issue, hair tracking typically uses a fixed number of segments per strand. Here we present a simplified hair diffuse shading formulation:
x max (0, f, 1), 11 14-4441 1441 x (max(0, t, 1) max(0, 6.+1 1)
(7)
where I; is the shading intensity of the i-th hair segment, is its tangent direction, and is the light direction. The shading discontinuity between adjacent Gaussians represented by I, - I and this value increases with curvature. To address this, we introduce curvature-based blending algorithm that adaptively adjusts the interpolation of Gaussian parameters (color and opacity) based on local strand curvature. For each segment i, we compute the tangent vector L, curvature Ki, and normalized curvature Kg, which is used as the blending weight wy:
f =
P P:ll" = [t: 1
p:+1
(8)
R; + e'
W. Res
Kmax
where p: denotes the 3D position of the i-th point along the strand, Kmax is the maximum curvature across the strand, and e is small constant for numerical stability. We apply w; to blend spherical harmonics (SH) coefficients and opacity a between adjacent Gaussians:
SHaksdadd SH, (1 11---) mxx + SH 19,,
(9)
Obblanded,; = a. -11 w.) + 0.-1 1/.
As shown in Fig. 8, our curvature- blending significantly improves the visual continuity of hair is jointly optimized with other Gaussian
rendering, particularly in curved regions. This blending parameters (e-g. position, scale, SH, opacity) during training, enabling more realistic and temporally coherent appearance in dynamic hair sequences.
3.3 Inference and Implementation Details
We train our model on single A100 GPU using the Adam optimizer and learning rate of 1 x 10-4 for both stages. In Stage I, each iteration samples 200k points from the hair point cloud. Here we provide the formal definitions for Eq. 1, including the point loss Lpoin
Emulpp. - 1 and the SDF penalty loss LSDF = N Σi ma - (D:));)). where p. is the predicted 3D hair point, GT is the corresponding ground-truth hair point, and SDF(p,) denotes the signed distance to the body surface. We penalize points inside the mesh via the ReLU (i.e., max(0.-)) -)) operation. For Eq. 1, we set 1p-1.0 and =0p:= for Eq. 6, we set 1g00 Asim= and Appips
p
During inference, we are given head motion sequence and canonical hair groom. For L t= 0. we apply only the coarse stage to initialize the dynamic hair. At 1, the fine stage is used with self-attention over the result from & = O. and the input flow F0 flw is set to O. For f. > 1, the pipeline operates recurrently as described. Once the dynamic hair sequence is obtained, we infer hair appearance based on the tracked hair positions to achieve dynamic hair rendering. For further implementation details (runtime and memory analysis), please refer to the Appendices.
4 Experiments
Dataset. Due the lack of real hair tracking data, generate synthetic dataset using XPBD-based physics simulation [53]. It includes dynamic sequences across diverse hairstyles, driven by motion- captured head movements. Each simulated groom consists of 1500k hair strand with 24 vertices per strand. For each hairstyle, we simulate 100 motion sequences of 100 frames, totaling 10k frames. Each frame includes deformed hair positions, head motion parameters, and the upper-body mesh. To generate our dynamic hair appearance dataset, we simulate 500 frame sequences per groom and render multi-view videos from 24 camera angles in Blender. See the Appendices for dataset details.
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Hair
Upper body
Rigged transfomation
Our defomaaion
Ground truth
Figure 4. Hair Deformation Comparison. From left to right column, given canonical groom and upper body pose, rigid transformations result in unrealistic hair deformation with upper-body penetration, while our method achieves natural deformation across different grooms with correct collisions.
Ours deformation Figure 5. Dynamic hair appearance evaluation. The left section compares dynamic hair rendering using hair deformatiion/raccing model against baseline methods: 3DGS [34] and Gaussian Haireut (GH) [9]. The right section shows rendering results merging hair and body Gaussian primitives. Without our hair tracking model, the hair appears rigid and unrcalistic, while our appearance model enhances hair rendering quality.
3DGS
GH
Ours
3DGS
GH
Ours
deformation deformation
Ground
deformation
Evaluation. We evaluate our method on deformation and appearance, com-
Ours
Rigged hair
Subject
Error.
Chamfer,
Error.
Chamfer.]
paring it with baselines across hair subjects using our synthetic hair
Subject
0.0738
0.0233
0.1411
0,0382
Subject
0.0998
0.0294
0.1911
0.0474
dataset. Each groom's training dataset consists of 90 motion sequences, while testing performed on the re- maining 10 sequences. For dynamic appearance, we assess unseen hair mo- tions (100 frames per subject) and novel views, capturing 100 views via horizontal camera rotation. See the Appendices for more dataset and evaluation details.
Subject 3
0. 0.1187
0.0342
0.2891
0.0637
Subject 4
0.0679
0.0260
0.1054
0.0334
Subject
0.0562
0.0201
0.0928
0.0296
Average
0.0832
0.0266
0.1639
0.0424
Table 2. Hair deformation comparison. Lı and Chamfer distances across normalized subjects.

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Ours
Gaussian Haircut [9]
3D GS [34]
Subject
PSNR
SSIM
LPIPSI
PSNR1
SSIM
LPIPS.
PSNR
SSIM
L.IPS_
Subject 1
28.026
0.906
0.101
23.248
0.873
0.101
20.747
0.852
0.132
Subject 2
24.817
0.820
0.169
20.921
0.791
0.180
19.972
0.772
0.215
Subject 3
24.987
0.744
0.227
20.960
0.742
0.236
19.972
0.687
0.287
Subject 4
27.534
0.955
0.071
24.181
0.906
0.078
20.246
0.894
0.101
Subject 5
29.681
0.933
0.069
26.053
0.924
0.058
23.605
0.906
0.083
Average
27.009
0.871
0.127
23.073
0.847
0.131
20.908
0.822
0.164
Table Comparison with others baselines on hair appearance. We report PSNR1, SSIM and LPIPS for each groomed subject to compare our method with other baselines in rendering quality.
Metrics. We evaluate the quality of rendered images via reconstruction fidelity (PSNR [54]), local structural similarity (SSIM [54]), and perceptual similarity (LPIPS [52]) between the synthesized images and the ground truth. We evaluate per-frame motion via the 1,2 error, Chamfer distance [55], between our dense hair point cloud and the ground truth. We evaluate the temporal consistency of the hair motion via the L2 error between the flow vectors the predicted and ground-truth point cloud sequences, and we show the flow error of different settings in Fig. 7
Baselines. Our framework learns dynamic hair motion and time-varying appearance using Gaussians in a differentiable manner. We compare against 3DGS [34] and Gaussian Haircut [9), retraining both on our synthetic dataset for each static hairstyle. Since dynamic hair modeling is underexplored, we integrate our dynamics model into each baseline, optimize their canonical hair appearance, and re- animate hair using our estimated motion for fair comparison. We further benchmark dynamics against two references: rigid-tranffrmmed canonical hair (lower bound) and physics-based XPBD [53] results (upper bound). Full baseline-traine -rriinnnttiinngggggoi and implementation details are provided in the Appendices.
Setting
PSNR
SSIM
LPIPS
Setting
Error
Ours w/o tan. blend
20.89
0.80
0.19
Ours w/o SDF
0. 1269
Ours w/o blend
25.08
0.88
0.18
Ours w/o motion
0.0964
Ours full
28.12
0.90
0.19
Ours w/o atten.
0.0909
Table 4. Hair appearance ablation. We compare dif- ferent appearance model settings rendering quality.
Ours full
0.0832
Table 3. Hair dynamics ablation. We show the 7,2
across settings deformed hair test sets.
Sample frames: camera view
Sample frames: camera view 2
Figure 6. Dynamic Gaussian Hair qualitative evaluation. We present qualitative visual results across various hairstyles and camera views, where the driven motion is obtained from the Mixamo MoCap data [56].
4.1 Results
Tab. 2 shows that our deformation model significantly enhances realistic hair deformations and reduces errors compared to rigid transformations on static hair. As shown in Fig. 4, applying rigid transformations to static hair leads to unnatural deformations, such as the absence of gravity effects and hair penetrating the body mesh Tab. 1 presents a quantitative comparison of our method with GH (Gaussian Haircut) [9] and 3DGS [34] on our synthetic dynamic hair dataset. Our approach consistently achieves the highest PSNR and SSIM across all subjects, indicating high- fidelity rendering quality. In Fig. and we further demonstrate dynamic hair appearance across different frames and hairstyles. Qualitative comparisons demonstrate that without our appearance optimization, both 3DGS and strand-based Gaussian representations suffer from degraded rendering quality due to hair self-occlusion In contrast, our motion-dependent appearance model is robust

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Ours SDF
Ours wi SDF
Ground truth
who Abllnd
wio blend
Ours full
Ground truth
Figure 8. Ablation on appearance. Top: results appearance model variants. Bottom: zoomed details.
Figure 9. Ablation on dynamics. Top: hair w/wo SDE. Bottom: motion model effect.
to occlusions, ensuring realistic rendering, while our hair deformation model can further enhance dynamic hair realism. Our dynamic Gaussian hair representation is flexible, allowing integration with body Gaussian primitives for avatar reanimation and high-quality rendering. Additional visual results are provided in the supplemental video.
4.2 Ablation Study
Dynamic Hair Modeling, Fig. 9 shows how our proposed approaches impact hair deformation and dynamics. Ours w/ SDF uses our hair-to-body mesh SDF constraint during training, reducing hair penetration into the body mesh. Ours w/ motion shows enhanced hair dynamics effects, such as inertia and gravity, due to the hair motion model. Ours w/o SDF removes the SDF constraint, Ours w/o motion uses only the coarse hair deformation model, and Ours w/o atten removes the cross- attention component our fine stage. These results indicate that our full coarse-to-fiee approach achieves the best quality. Detailed quantitative results in Tab. 3 confirm this.
To evaluate temporal consistency, Fig. 7 plots the flow vector error across a sequence for dif- ferent settings. While our coarse stage shows similar performance to rigid hair in terms of dy- namics, the fine model significantly improves motion effects. Additionally, the attention mod- ule further enhances hair dynamics and tempo- ral consistency. Dynamic Hair Appearance Optimization. Fig. 8 compares different config- urations of our appearance model: Ours with- out tangent features or curvature-bue (w/o tan. &blend) ours without curvature-based blending (w/o blend), and our full model (Ours full). demonstrates that conditioning on structural features like hair tangent vectors enhances local detail and sharpness, while curvature-based blending further improves smoothness and scattering between hair segments, creating more realistic transi- tions. As indicated in Tab. 4, incorporating tangent features improves natural hair perception, while curvature blending significantly enhances rendering quality, resulting in more visually appealing hair.
Hair dynamics ablation. Flow error (x10-3) per timestep for different settings.
Figure
blending
Conclusion
We propose Dynamic Gaussian Hair (DGH), novel data-driven framework for dynamic hair generation with animatable 3D Gaussians. DGH models wide range of hairstyle deformation driven by arbitrary head motions (including long hair, curly hair, ponytails, etc.), handling dynamics and hair-body collision, within a coarse-to-fine strategy. Hair appearance is represented by motion- dependent connected Gaussians to handle variations under intricate motions at render time, enabling high-fich figh-fidelity dynamic hair modeling and rendering. Future work and limitations are addressed in the Appendices.
Acknowledgment
We sincerely thank Gene Lin for his help and discussions regarding the synthetic data generation pipeline, and we also appreciate Yu Ding for his assistance with dynamic hair and body merging.
10

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References
[1] Stephen Marschner, Henrik Wann Jensen, Mike Cammarano, Steve Worley, and Pat Hanrahan. Light scattering from human hair fibers. ACM Transactions Graphics (TOG), 2203)700779 2003. 2, 6
[2] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoor- thi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2, 4, 18
[3] Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo Martin-Brualle Nerfies: Deformable neural radiance fields. ICCV, 2021. 2,
4
[4] Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, and Matthias Nießner. Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians. arXiv preprint arXiv:2312.02069 2023. 2
[5] Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, and Yebin Liu. Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2024. 4
[6] Alfredo Rivero, ShahRukh Athar, Zhixin Shu, and Dimitris Samaras. Rig3dgs: Creating controllable portraits from casual monocular videos. arXiv preprint arXiv:2402.03772,, 2024.
[7] Hclisa Dhamo, Yinyu Nic, Arthur Moreau, Jifei Song, Richard Shaw, Yiren Zhou, and Eduardo Pérez-Pellitero. Headgas: Real-time animatable head avatars via 3d gaussian splatting. In European Conference on Computer Vision, pages 459-476. Springer, 2024. 2
[8] Haimin Luo, Min Ouyang, Zijun Zhao, Suyi Jiang, Longwen Zhang, Qixuan Zhang, Wei Yang, Lan Xu, and Jingyi Yu. Gaussianhair: Hair modeling and rendering with light-aware gaussians. arXiv preprint arXiv:2402. 1044883, 2024. 2, 4, 6
[9] Egor Zakharov, Vanessa Sklyarova, Michael Black, Giljoo Nam, Justus Thies, and Otmar Hilliges. Human hair reconstruction with strand-aligned 3d gaussians. In European Conference on Computer Vision, pages 409-425. Springer, 2024. 2, 4, 6, 9, 18, 21
[10] Liwen Hu, Chongyang Ma, Linjie Luo, Li-Yi Wei, and Hao Li. Capturing braided hairstyles. ACM Transactions on Graphics (TOG), 33(6): 2014.
[11] Fei Shao, Xingce Wang, Qianqian Jiang, Zhongke Wu, and Mingquan Zhou. Modeling curly hair based static super-helices. In 2015 International Conference Cyberworlds (CW),
pages 306-313. IEEE, 2015. 3
[12] Yuxiao Zhou, Menglei Chai, Alessandro Pepe. Markus Gross, and Thabo Beeler. Groomgen: A high-quality generative hair model using hierarchical latent representations. ACM Transactions on Graphics (TOG), 42(6):1-16, 2023.
[13] Menglei Chai, Linjie Luo, Kalyan Sunkavalli, Nathan Carr, Sunil Hadap, and Kun Zhou. High- quality hair modeling from single portrait photo. ACM Trans. Graph., 34(6):204 2015. 3
2015.
[14] Menglei Chai, Tianjia Shao, Hongzhi Wu, Yanlin Weng, and Kun Zhou. Autohair: Fully automatic hair modeling from single image. ACM Transactions on Graphics, 35(4), 2016.
[15] Liwen Hu, Chongyang Ma, Linjie Luo, and Hao Li. Single-view hair modeling using a hairstyle database. ACM Transactions on Graphics (ToG), 34(4):1-9, 2015. 3
[16] Sylvain Paris, Hector Briceno, and François X Sillion. Capture of hair geometry from multiple images. ACM transactions graphics (TOG), 23(3):712-719, 2004. 3
[17] Yichen Wei, Fyal Ofek, Long Quan, and Beuug-Yeung Shum. Modeling hair from multiple views. In ACM SIGGRAPH 2005 Papers, pages 816-820, 2005.

---

[18] Liwen Hu, Chongyang Ma, Linjie Luo, and Hao Li. Robust hair capture using simulated examples. ACM Transactions on Graphics (TOG), 33(4):1-10, 2014.
[19] Yuxiao Zhou, Menglei Chai, Daoye Wang, Sebastian Winberg, Erroll Wood, Kripasindhu Sarkar, Markus Gross, and Thabo Beeler. Groomcap: High-fidelity prior-free fiir-frree hair capture. arXiv preprini wxXv:22499000831 2024. 3
[20] Linjie Luo, Hao Li, Sylvain Paris, Thibaut Weise, Mark Pauly, and Szymon Rusinkiewicz. Multi-view hair capture using orientation fields. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 1490-1497. IEEE, 2012. 3
[21] Radu Alexandru Rosu, Shunsuke Saito, Ziyan Wang, Chenglei Wu, Sven Behnke, and Giljoo Nam. Neural strands: Learning hair geometry and appearance from multi-view images. In European Conference on Computer Vision, pages 73-89. Springer, 2022. 3
[22] Darius Rückert, Linus Franke, and Mare Stamminger. Adop: Approximate differentiable one-pixel point rendering. ACM Transactions Graphics (ToG), 41(4):1-14, 2022. 3
[23] Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, and Olga Sorkine- Hornung. Differen- tiable surface splatting for point-based geometry processing. ACM Transactions on Graphics (TOG), 38(6):1-14, 2019.
[24] Keyu Wu, Yifan Ye, Lingehen Yang, Hongbo Fu, Kun Zhou, and Youyi Zheng. Neuralhdhair: Automatic high-fidelity hair modeling from single image using implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1526-1535, 2022. 3,4
[25] Lingchen Yang, Zefeng Shi, Youyi Zheng, and Kun Zhou. Dynamic hair modeling from monocular videos using deep neural networks. ACM Transactions on Graphics (TOG), 38(6):
1-12, 2019.
[26] Yuefan Shen, Shunsuke Saito, Ziyan Wang, Olivier Maury, Chenglei Wu, Jessica Hodgins, Youyi Zheng, and Giljoo Nam. Ct2hair: High-fidelity 3d hair modeling using computed tomography, ACM Transactions on Graphics (TOG), 1214):1-13, 2023.
[27] Giljoo Nam. Chenglei Wu, Min H Kim, and Yaser Sheikh. Strand-accurate multi-view hair cap- ture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 165-164, 2019. 3
[28] Tiancheng Sun, Giljoo Nam, Carlos Aliaga, Christophe Hery, and Ravi Ramamoorthi. Human hair inverse rendering using multi-view photometric data. In Eurographics Symposium on Rendering (EGSR). The Eurographics Association, 2021.
[29] Zhiyi Kuang, Yiyang Chen, Hongbo Fu, Kun Zhou, and Youyi Zheng. Deepmvshair: Deep hair modeling from sparse views. In SIGGRAPH Asia 2022 Conference Papers, pages 1-8, 2022. 4
[30] Yusuke Takimoto, Hikari Takehara, Hiroyuki Sato, Zihao Zhu, and Bo Zheng. Dr. hair: Reconstructing scalp-connected hair strands without pre- pre-training via differentiable rendering of line segments. In Proceedings of the IEEE/CV Conference on Computer Vision and Pattern Recognition, pages 20601-2061, 2024. 4
[31] Wojciech Zielonka, Timo Bolkart, and Justus Thies. Instant volumetric head avatars. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pages 4574 4584, 2023. 4
[32] Vanessa Sklyarova, Jenya Chelishev, Andreea Dogaru, Igor Medvedev, Victor Lempitsky, and Egor Zakharov. Neural haircut: Prior-guided strand-based hair reconstruction. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pages 19762-19773, 2023.
[33] Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, and Jason Saragih. Mixture of volumetric primitives for efficient neural rendering. ACM Trans. Graph., 40(4), July 2021. ISSN 0730-0301. doi: 10.114533450626.459966 URL. https: //doi. 100 1 1 3450626. 3459863. 4
12

---

[34] Bernhard Kerbl, Georgios Kopanas. Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-14 2023. 4, 6, 9, 21
[35] Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hod- gins, and Michael Zollhofer. Learning compositional radiance fields of dynamic human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7704-5713, 2021. 4
[36] Ziyan Wang, Giljoo Nam, Aljaz Bozic, Chen Cao, Jason Saragih, Michael Zollhöfer, and Jessica Hodgins. A local appearance model for volumetric capture of diverse hairstyles. In 2024 International Conference on 3D Vision (3DV), pages 190-200. IEEE, 2024.
[37] Qing Zhang, Jing Tong, Huamin Wang, Zhigeng Pan, and Ruigang Yang. Simulation guided hair dynamics modeling from video. Computer Graphics Forum, 3107)2003--210, 2012. 4
[38] Zexiang Xu, Hsiang- Tao Wu, Lvdi Wang, Changxi Zheng, Xin Tong, and Yue Qi. Dynamic hair capture using spacetime optimization. To appear in ACM TOG, 33:6, 2014.
[39] Liwen Hu, Derek Bradley, Hao Li, and Thabo Beeler. Simulation-ready hair capture. Computer Graphics Forum, 36(2):281-294) 2017. 4
[40] Qing Lyu, Menglei Chai, Xiang Chen, and Kun Zhou. Real-time hair simulation with neural interpolation. IEEE Transactions on Visualization and Computer Graphics, 1894):1894-1955, 2020. 4
[41] Jerry Hsu, Tongtong Wang, Zherong Pan, Xifeng Gao, Cem Yuksel, and Kui Wu. Real-time physically guided hair interpolation. ACM Transactions on Graphics (TOG), 43(4):1-11, 2024.
[42] Gilles Daviet. Interactive hair simulation on the gpu using admm. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1-11, 2023. 4
[43] Tuur Stuyck. Gene Wei-Chin Lin, Egor Larionov, Hsiao-yu Chen, Aljaz Bozic, Nikolaos Sarafianos, and Doug Roble. Quaffure: Real-time quasi-static neural hair simulation. arXiv preprint arXiv:2412. 2024. 4
[44] Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Michael Zollhöfer, Jessica Hodgins, and Christoph Lassner. Hvh: Learning a hybrid neural volumetric representation for dynamic hair performance capture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6143-6155 2022. 4
[45] Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Chen Cao, Jason Saragih, Michael Zollhöfer, Jessica Hodgins, and Christoph Lassner. Neuwigs: A neural dynamic model for volumetric hair capture and animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8641-8651, 2023. 4
[46] Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, and Matthias Nießner. Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians. In Proceedings of the IEEE/CVF Conference Computer Vision and Patlern Recognition, pages 20299-20300 20309, 2024. 4
[47] Tobias Kirschstein, Javier Romero, Artem Sevastopolsky, Matthias Nießner, and Shunsuke Saito. Avat3r: Large animatable gaussian reconstruction model for high-fidelity 3d head avatars. arXiv preprini arXiv:2502.20220) 2025.
[48] Kartik Teotia, Hyeongwoo Kim, Pablo Garrido, Marc Habermann, Mohamed Elgharib, and Christian Theobalt. Gaussianheads: End-to-end learning of drivable gaussian head avatars from coarse-to-fine representations. ACM Transactions on Graphics (TOG), 43(6): 2024.
[49] Brian Curless and Mare Levoy. A volumetric method for building complex models from range images. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 303-312, 1996. 5
13

---

[50] Matthias Zwicker, Hanspeter Pfister, Jeroen Van Baar, and Markus Gross. Surface splatting. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques. pages 371-378 2001. 6
[51] Eugene d' d'Eo,, Guillaume Francois, Martin Hill, Joe Letteri, and Jean-Marie Aubry. An energy-conserving hair reflectance model. Computer Graphics Forum, 1014)11181-1188, 2011.
6
[52] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreason- able effectiveness of deep features as perceptual metric. In CVPR, 2018. 6, 9
[53] Miles Macklin, Matthias Müller, and Nuttapong Chentanez. Xpbd: position- based simulation of compliant constrained dynamics. In Proceedings of the 9th International Conference on Motion in Games, pages -9-54,22 2016. 7,9, 15, 19
[54] Alain Hore and Djemel Ziou. Image quality metrics: Psnr vs. ssim. In 2010 20th international conference on pattern recognition, pages
IEEE, 2010.
[55] Haoqiang Fan, Hao Su, and Leonidas Guibas. A point set generation network for 3d object reconstruction from single image. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 605-613, 2017.
[56] Adobe Systems Inc. Mixamo. https: / //www/wwwww//// http://wxxamm..mmmmm...........t wwm.ooiammmmmmmmmmmmmmmmmmm....m com/, Online character animation and motion capture dataset. Accessed 2025-10-21. 9
[57] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 18
[58] NVIDIA Corporation. NVIDIA TensorRT: High-PPeffomance Deep Learning Inference, 2025.
URL https:
nvidia. com/tensorrt Accessed: 2025-03-05. 19
[59] Xueting Li. Ye Yuan, Shalini De Mello, Gilles Daviet, Jonathan Leaf, Miles Macklin, Jan Kautz, and Umar lqbal. Simavatar: Simulation-ready avatars with layered hair and clothing. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 26320-26338 2025. 21
[60] Chengan He. Jorge Alejandro Amador Herrera, Yi Zhou, Zhixin Shu, Xin Sun, Yao Feng, Sören Pirk, Dominik L Michels, Meng Zhang, Tuanfeng Wang, and Holly Rushmeier. Digital salon: An ai and physics-driven tool for 3d hair grooming and simulation. ACM SIGGRAPH Asia 2024 Real-Time Live!, 2024. URL https: /ttt:::://iiiiaassalas...
github. io/. 22
[61] Chengan He, Xin Sun, Zhixin Shu, Fujun Luan, Sören Pirk, Jorge Alejandro Amador Herrera, Dominik I. Michels, Tuanfeng Y Wang, Meng Zhang, Holly Rushmeier, and Yi Zhou. Perm: A parametric representation for multi-style 3d hair modeling. arXiv preprint arXiv:2407.19451, 2024. 22
[62] Thomas Müller, Fabrice Rousselle, Jan Novák, and Alexander Keller. Real-time neural radiance caching for path tracing. arXiv preprint arXiv:2106. 12372, 2021. 22
[63] Stephan Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. Fastnerf: High-fidelity neural rendering 200fps. In Proceedings of the IEEF/CVF international conference on computer vision, pages 14346-14355, 2021. 22
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Appendices
In this document, we provide more details for the dataset, method, experiments, and more qualitative results, as an extension of Sec. 3 and Sec. 4 in the main paper. Please also refer to the video demo for dynamic hair results, comparison, ablation study, and more results.
A Dataset Details
As mentioned in the Sec. 1, due to the lack of real captures of dynamic hair deformations with accurate strand tracking, we create new synthetic dataset capturing both hair geometry and appearance. The hair dataset breakdown is shown below: in Fig. 10, we present variety of hairstyles in our synthetic hair dataset, including curly, wavy, blowout, ponytail and etc, and we show the distributions of hairstyle types and hair lengths.
Hair Length Distribution
Type
Hairstyle Distribution
Long Haiir Short Hair
Ponytall
20b
55.6%
2.5-
Count
44.4%
0.5
0.0
Blowuu
Ponytail
Figure 10. Hair length and hairstyle distribution
A.1 Geometry Dataset (Sec. 4)
To capture head motions, we rotate the head to record head movements. Instead of linear speed between two head positions, apply spline-based interpolation to simulate varying motion speeds. This approach allows us to more effectively capture secondary hair motions when simulating the hair with head motions with realistic damping and inertial dynamics. For each hairstyle, with XPBD-based physics simulation [53] driven by captured head motions, and for each hairstyle, generate 100 motion sequences, and each motion sequence contains 100 frames. For each frame, we record the head mesh, deformed hair strands, and then post-process to get hair volume and pose volume. We show the hair geometry dataset generation pipeline in Fig. 11
animated
Heed Motion Sequences
Pose Hair Volume Extraction
Hair Geometry Dataset
Mocapp
Statio
Deformed Hair
Simulation
Figure 11. Hair Geometry Dataset Generation Pipeline
Fig. 12 presents samples from our hair deformation dataset, including static hair with strands, upper body SDF volume, static hair SDF volume, deformed hair strands, and deformed hair SDF volumes for each hair groom. For hair SDF volumes, hair point cloud (PC) is converted to SDF grid by computing the distance from each voxel to the nearest point in the PC.
Training dataset. We train each hairstyle independently to obtain a hairstyle-specifie hair deformation model. For each hairstyle, we use 90 motion subjects, resulting geometry training dataset of 9K frames. In the coarse stage, randomly sample frame and apply rigid transformations to the
15

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Static hair
Pose volume
Rigid transformed hair volume Rigid transformed hair Deformed hair volume Deformed
Figure 12. Training samples from the hair deformation dataset, including static hair, pose volume, rigidly transformed static hair, recorded hair volume, deformed hair, and corresponding recorded hair volume.
static hair using the sampled head pose. The network learns per- per-point displacements by iteratively sampling different poses. In the fine stage, we learn flow vector field to deform the hair from frame t-1 to frame t, using randomly sampled points from the deformed hair time Each training batch consists of 200K randomly sampled hair points.
dynamic hair model across different hairstyles using 10 motion sequences, resulting in IK frames. We report the average L2 етгоr between the predicted and ground- truth deformed hair, as well as the L2 error of the estimated flow vectors (displacements between frames t- and t).
Testing dataset. We evaluate
A.2 Appearance Dataset (Sec. 4)
Once obtaining the deformed hair sequences, render multi-view videos of various hair grooms in Blender (particle system) under a consistent lighting setup to train our appearance model. Each frame has resolution of 1024 x 1024. For each hairstyle, include diverse hair colors. Fig. 13 (left) shows the color variations for each hairstyle, (middle) illustrates the hair appearance dataset generation pipeline, and (right) presents sample setup demonstrating how deformed hair is positioned for multi- view rendering. Each dynamic frame includes 24 views: 12 cameras evenly distributed horizontally around the object and 12 randomly positioned the upper semi-sphere to capture diverse angles. Fig. 14 illustrates multi-view rendering samples for different hair grooms.
Training dataset We train motion-dependent hair appearance model using multi-view video sequences. Each sequence contains 500 motion frames, and for each frame, we render images from 24 camera viewpoints distributed over a semi-sphere. This setup yields total of 12K training images per hairstyle.
Testing dataset To evaluate our appearance model, we generate a testing dataset consisting of 100 frames featuring unseen hair motion. The test sequence captured using a horizontal camera rotation arc, with each camera looking at the hair center. Viewpoints are sampled across a vertical range of 00-45% on the viewing semi-sphere, providing diverse angular coverage and challenging view-dependent appearance variations.
16

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Blosout
114,089
Deformed Sequereora
114,03
Ponybil1
30,50
Ponybail
Curly
99
Parrida Syoamm
Carty
86,574
Lightiggetuu
117,650
Nutti-dwe lalr
Hainstyle
Hairr color
Hair strand number
Hair Appesrance Datacet Generation Pipeline
Multi-Viww Hair Rendering
Figure 13. Hairstyle Color Variations and Hair Appearance Dataset Generation Pipeline
B Network and Evaluation Details
Hair, as a complex and dynamic structure, exhibits intricate spatial patterns and deformations that challenging to model with lower-dimensional representations, the hair appearance also suffer challenges as the moving pattern caused occlusion. address this, we propose two stage modeling: which Stage I and II Hair Dynamics Model and Appearance Model. As summarized in Tab. 5, Stage adopts a coarse-to to fine design: the coarse stage learns hair deformation from static hair, while the fine stage refines secondary dynamics (c.g., inertia and damping).
Module
Function
Stage I (coarse stage)
Learns hair deformation given head motions Adds secondary motion refinement Optimizes dynamic Gaussian hair appearance
Stage (fine stage)
Stage II
Table 5. Overview of model stages and their corresponding functions.
For Hair Dynamics Model, we adopt a coarse-tor torsee-to-fnee framework, representing static hair as a SDF volume to capture its complete 3D structure and spatial relationships. In this section, present the architecture of our 3D CNN networks, Epose and Enair, the implicit networks M, D, and D*, along with dataset and evaluation details.
B.1 Coarse-to-Fine Framework (Sec. 3.1)
In the Coarse Stage, we utilize a 3D U-Net architecture for Epesc and Epair with input volumes of resolution 128 128 128. We present the network structure in Fig. 15. The network employs separate encoders for static hair and pose, each comprising four convolutional blocks with filter sizes F = 4, 2F, 4F, and 8F, utilizing 3D convolutions, LeakyReLU activations, and Instance Normalization to effectively extract hierarchical features.
The decoder mirrors the encoder with four deconvolutional blocks, progressively upsampling and reconstructing volumetric features while reducing the number of filters from 8F Skip connec- tions between corresponding encoder and decoder blocks preserve high-resolution details. With base filter size of F = and output channels of 16, the 3D U-Net effectively captures global context and fine-graines details of static hair, enabling precise learning of hair deformation by leveraging the static hair structure.
In the Fine Stage, the encoders Epose and Emirr retain the same structure, with Enair sharing weights across timesteps during training. The MLPs (M, D*) consist of six fully connected layers with dimen- sions [176, 512, 512, 256, 128, 3] and [239, 512, 512, 256, 128, 3), respectively. Residual connections are employed to enhance feature representation by concatenating input features with intermediate outputs. Leaky ReI.U is used as the activation function for all layers except the final one, ensuring non-linearity and stability. The final layer outputs 3-dimensional vector, directly supervised by the ground truth displacement and 3D flow vector.
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Camera
Camera
Camera 3
Camera 4
Camera 5
Camera 6
Figure Training samples from the appearance dataset, which presents static frame various hair grooms and rendering results from different camera views.
The fine stage estimates the flow vector from Vi- to Vc. Since VLL is not available, cross- attention [57] between V--1 and V-t-2 infers dynamics. "Ours w/o atten" uses only V/C11 to estimate the flow vector. In the cross-altention. Q. K. V is hair Vt-1 hair This approach leverages the immediate context from Vt-1 and longer-term dynamics from V enabling the model to capture motion patterns and ensure temporal consistency for a smooth and reliable estimation of Vtt.
B.2 Dynamic Hair Appearance Network and Implementation(Sec. 3.2)
For dynamic hair appearance optimization, the encoder Enair encodes the deformed hair to cap- ture global motion features and 3D spatial information. A lightweight MLP D with dimensions [169, 256, 256, 256, 128, 50] decodes the Spherical Harmonics coefficients for color, the scale factor along the hair length direction. and opacity. Positional encoding for the position p. tangent vector t, and view direction d follows the encoding function proposed in NeRF [2]. For Gaussian primitive initialization, we make cylindrical Gaussian primitive attached each hair segment with length much greater than its radius. Similar to Gaussian Haircut [9], the scale of Gaussian primitives has only one degree of freedom, which proportional to the length of the line segment, while the other two are fixed to small predefined value.
18

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C-BLK
D-BLK > Deconvolutional Block
Conrotutionl Block
Deconvolutiocal Block
+ Convolutioal Block
Input feature
Input feature
Conv3D
Com:3D
[Rel.U + Inst. Vorm
LRel.U + Nomm33
Conv3D
Com33D
C
CBLK 16
C.BLK (16, 3Z
CBLX (22, 64)
Bunk 330
DRRLK (64, 16))
D.BLIC 80
BLK 132 on
UReL + Inst Nom33D
LRee.U + NostNN
118 160
Conv3D
ConTTranposSDD
LRaLU Nomm3
LRelUU + Norm33
Figure 15. Left: Our 3D CNN networks for Epose and Enair; Right: Convolutional and deconvolutional blocks.
B.3 Evaluation Details (Sec. 4)
Metrics. We evaluate hair motion over time by comparing the flow vectors between consecutive frames in the predicted and ground truth point cloud sequences. The flow vector for each point i at frame t in the predicted point cloud is defined as F = P P. where P; represents the predicted point cloud positions. Similarly, the flow vector in the ground truth point cloud is defined
as : P P where P, represents the GT point cloud positions. The total flow error each
timestep t over all points in the point cloud is given by feroor N the total number of points in the point cloud.
Σ==
E F
#1
where N is
Method
PSNR
SSIM
LPIPS L
Ours w/o LPIPS & SSIM
27.82
0.8874
0.2100
Ours w/o LPIPS
28.01
0.8923
0.1987
Ours
28.12
0.9004
0.1881
Table 6. Ablation of different losses for appearance optimization.
Inference. We train and test our model on a single A100 GPU, with training times of approximately 20 hours for the dynamic hair coarse stage, 20 hours for the fine stage, and 26 hours for the appearance model. During inference, the dynamic hair model achieves 2.0 FPS for approximately 150K hair strands, significantly outperforming the XPBD [53] physics-based simulation engine that is used to generate the synthetic hair dataset, which runs at approximately 0.33 FPS. Meanwhile, dynamic hair novel view synthesis reaches 2.22 FPS, with faster speeds for fewer strands.
Runtime and memory performance analysis. DGH design is suitable for low-compute devices. Our implementation is an upper bound. Neural net inference can be accelerated with TensorRT [58] and additional strands linearly interpolated. For hair deformation runtime analysis, we report runtime with various strand numbers on RTX4090 GPU, compared with XPBD, as shown in Fig 16. Hair inference required 5GB, while high-quality grooms (150K+ strands) required 20GB. For dynamic hair rendering runtime analysis, our appearance model renders IK images in 0.45 seconds (2.22 FPS), whereas Blender's 's hair shader rendering takes 6 seconds per image (0.17 FPS).
Ours vs. XPBD (Mackin at al.
Ours frome Rate vs. Hair Szrnn Count
Our Vetho2 SPA7)
P95
36
34
(PS)
12
30
1000
15.00
2002
2500
3000
1000
4200
40000
10k
205
30k
40C
50k
65K
70K
85k
50%
LOOK
Figure 16. DGH achieves real -ime-time performance with grooms of 10K strands (unoptimized upper bound).
Hurbbe das Stredd
Nembar
Physics-basee simulation (XPBD) runs below I FPS with hair interpolation and equal qugh-tuality photoreal rendering in Blender 3D engine.
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Appearance loss ablations. We present the appearance loss ablation study in Tab, 6. For the final loss constraint, we incorporate both perceptual loss (LPIPS) and structural loss (SSIM), demonstrating that our full method achieves the best performance.
strands All hair strands Figure 17. Hair deformation with varying strand counts, From left to right: increasing the inferred number of hair strands, totaling approximately 100K to 150K strands.
1K hair strands
K strands
20 hair strands
Ground truth
Multi-layer Gaussian avatar without hair
DGH with multi-layer Gaussian avatar
Figure 18. Our method merges the dynamic hair layer with a per- fermframe animatable Gaussian avatar. By employing multi-layer Gaussian representation (left). we obtain an avatar without the hair layer. Applying DGH within this multi-layer framework enables realistic avatar re-animation (right). Note: The human face is masked to comply with privacy regulations.
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C Qualitative Results
C.1 Hair Deformation.
In Fig. 17, we present the hair deformation test on arbitrary head poses and various hairstyles. We also present hair deformation results tested with varying numbers of hair strands, ranging from IK to the full set of approximately 150K strands. Our volumetric implicit function robustly infers hair deformation for arbitrary strand numbers without requiring post-processing hair interpolation.
C.2 Dynamic Hair Novel- Synthesis.
Baseline settings. We compare our DGH model with two baseline methods: 3DGS [34] and Gaussian Haircut [9]. We train both baselines using their publicly released code on our canonical synthetic hair dataset, which includes 48 camera views covering the full hair. For 3DGS, we initialize Gaussian primitives by attaching them to the center of each hair segment. dynamic sequences, we fix the number of Gaussians and disable density growth. We fix the opacity and positions of Gaussians, compute their orientation using hair tangents, and optimize three degrees of freedom of scale, color (represented using spherical harmonics). We use the Adam optimizer with a learning rate of 0.00025 for SH color features and 0,0005 for scale optimization. For Gaussian Haireut, similarly optimize both color and scale, and the scale is constrained to be proportional to the length of each line segment to better capture hair geometry. Once each baseline model is trained on the static canonical hairstyle, propagate the learned Gaussian parameters to dynamic sequences using our hair tracking model, which provides per-frame hair geometry.
maintain consistency with our
Comparison and additional results. We present further results on dynamic hair novel-view synthesis in Fig. and Fig. 20. To better evaluate appearance quality, we use ground-truth hair tracking and compare appearance model against other Gaussian-basse hair representation methods. Results are shown across multiple dynamic sequences and diverse hairstyles.
C.3 Multi-Layer Gaussian Avatars with Dynamic Hair.
Our dynamic hair Gaussian representation can be seamlessly integrated with pre-trained Gaussian avatar models for avatar re-animation and novel-view rendering, as shown in Fig. 21, 22, 23, 24 the render image resolution 1024 1024. Our appearance model supports supports only hair rendering. while the body is rendered using separate pre- rre-trained Gaussian model. For visualization, both hair and body Gaussians are rendered together. Note that the static hair lacks gravity effects. Using our hair tracking model, we represent hair strands as connected cylindrical Gaussians and achieve high-quality novel view synthesis through our nottonndeepedent appearance model. Unlike static hair, our dynamic hair model implicitly learns gravity effects from the dataset distribution, ensuring realistic rendering across dynamic hair motion sequences.
In Fig. 18, we further demonstrate how DGH can be seamlessly integrated with a multi-layer Gaussian avatar for realistic avatar re-animation. Since our DGH model primarily takes head motion as input, the resulting motion focuses on head-driven hair dynamics without incorporating body motion effects. Inspired by recent advancements in multi-layer Gaussian avatar modeling [59], realistic avatar editing and re-animation can be achieved by introducing additional Gaussian layers represent various components such as hair and clothing. To merge an additional hair layer with the body avatar, our framework begins with a pre-trained head GS avatar re-animated using head motion inputs. The DGH module predicts dynamic Gaussian hair deformations directly from a bolf-body GS representation or dense point cloud, which are then merged with the head GS to produce realistic, data-driven hair motion.
In contrast, physics-basee methods re-animate GS avatar by converting it into an explicit mesh, simulating hair motion with a physics engine, rendering it through 3D pipeline, and re-converting the result into a 3DGS format before merging. Unlike such mesh-based pipelines that require multiple conversion and rendering stages, our DGH framework operates entirely in the Gaussian domain. By representing both hair and the upper body as volumetric Gaussians, it enables mesh-free deformation prediction, ensuring compatibility with Gaussian- based avatars and seamless integration into learning- based re-animation pipelines without rigging or simulation overhead.
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D Limitation and Future Work
DGH currently handles hair-upper-body collisions. Environmental constraints and relighting DGH (via albedo inference) could be handled in future work. Due to the challenges of accurately tracking real hair dynamics, precise deformation and per-timestep positions of hair strands are difficult to record. Our model is currently trained and evaluated on synthetic data, focusing on generalizing to head motions. In future work, we aim to create large dynamic hair dataset based on static 3D hair datasets [60, 61J. leveraging learned deformed hair prior. This identity robustly infer diverse hairstyles and perform well on real-world data.
model could
In the second stage, lightweight MLP combined with a differentiable rasterizer achieves -ighlfideiity novel view rendering, balancing efficiency and quality. While fast for dense Gaussian primatives, the current version is not real-time for dense hair (100K-150K hair strands). Future work could focus on optimizing the MLP or leveraging caching methods [62, 63], such viewpoint-awe caching. to achieve real-time performance.
temporal
For multi-layer Gaussian avatars with dynamic hair applications, model currently supports integration with head avatars driven solely by head motion. Full-body effects such as jumping and squirming under gravity are not yet modeled. In future work, we plan incorporate explicit material conditioning and full-body motion inputs to improve generalization across diverse hairstyles, particularly with access to more comprehensive 3D hairstyle dataset.
E Broader Impacts
As positive impact, our work enhances the realism of Gaussian-based digital humans, enabling more expressive virtual interactions in telepresence, AR/VR, and virtual production. As a negative impact, increased photorealism may also raise ethical concerns, including potential misuse for identity manipulation or deceptive content generation.
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3DGS
GH
Ours
3DGS
GH
Ours
3DGS
GH
Ours
Frame
Frame 2
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Figure 19. Comparison results for dynamic hair rendering
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3DGS
GH
Ours
3DGS
GH
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3DGS
GH
Ours
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Figure 20. Comparison results for dynamic hair rendering
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Static hair
Frame
Frame 2
Frame 3
Frame 4
Frame 5
Static hair
Frame 1
Frame 2
Frame 3
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Figure 21. Our dynamic Gaussian hair appearance model enables novel view synthesis from static hair using our hair tracking model. Note: Static hair lacks gravity, while the dynamic model implicitly learns gravity from the dataset distribution.
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Static hair
Frame 1
Frame 2
Frame 3
Frame 4
Frame 5
Static hair
Frame 1
Frame 2
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Figure 22, Novel view synthesis results from static hair using our hair tracking model across various hair grooms.
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Static hair
Frame 1
Frame 2
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Static hair
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Figure 23, Given our hair tracking model and body pose, our framework enables reanimation and novel-view rendcring with hair dynamics, including curly hair.
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Static hair
Frame 1
Frame 2
Frame 3
Frame 4
Frame 5
Static hair
Frame
Frame 2
Frame 3
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Figure 24. Given our hair tracking model and body pose, our framework enables reanimation novel-view rendering with hair dynamics, including ponytail hairstyles.
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Reviews:
[{"review_id": "XPOs100Jys", "reviewer": "Reviewer_VHAd", "summary": "This 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.", "questions": "Please see the weakness above.", "limitations": "A 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "This paper presents Dynamic Gaussian Hair(DGH), a two-stage learning-based framework for dynamic hair modeling and rendering. Though I still have concerns about the difficulty of applying the memthod to real captured data, considering the clear advantages of the method over simulation-based methods in terms of runtime efficiency and over prior static appearance models in terms of visual fidelity under motion, I lean toward acceptance for this submission and keep the initial rating.", "rebuttal": "Thank you for your valuable feedback! We carefully considered your comments and provide detailed responses below:\n\n1. **Generalization to real data**:\n\nOur method relies on synthetic data for ground-truth (dynamic hair) and supervised training, as discussed in the Introduction (L51–52) and Limitations (Supple. L155–157), as capturing strand-level dynamic hair in the wild remains challenging due to hardware limitations and the absence of robust hair tracking systems. Hence, we opted to create photorealistic dynamic hair data at scale, using state-of-the-art offline toolkits from the graphics community (i.e., physics-based simulation, head motion, and 3D engine with hair shaders for rendering), as detailed in the paper (Main L243–249, Supple. A).\n\nThis dataset includes a wide range of head poses and dynamics, enabling our model to generalize to realistic motions, as demonstrated in the video (03:11). While we initially explored unsupervised training using in-the-wild videos and coarse hair meshes, the results lacked strand-level precision and visual sharpness required for dynamic Gaussian rendering.\n\nBeyond hair prediction, our other goal is to align with the 3DGS representation. Our learning-based Stage I model supports dynamic Gaussian hair as a separate layer, designed to integrate seamlessly with multi-layer Gaussian head avatars (Main L16–18). This (point-based) mesh-free design enables re-animation of arbitrary hairstyles (long hair, curls, ponytails) with plausible dynamics while avoiding the complexities of physics-based simulation pipelines.\n\nOur DGH framework is compatible with 3DGS avatars and can be extended to improve head avatar realism. The pipeline is as follows:\n\nReal multi-view images → Strand-based static hair reconstruction [1] → DGH → Merge with animation-ready 3DGS avatar\n\nDue to NeurIPS policy, we cannot include visual results at this stage, but we will present additional results with dynamic hair merged into human avatars in the final version. As mentioned in Main L87, we also plan to release our synthetic dataset to support and accelerate data-driven dynamic hair modeling.\n\n[1] Sklyarova, Vanessa, et al. \"Neural haircut: Prior-guided strand-based hair reconstruction.\" ICCV 2023.\n\n2. **SDF regularization in fine stage**:\n\nThank you for the thoughtful observation. While the coarse stage includes SDF constraints to discourage hair-body interpenetration, we do not apply the same constraints in the fine stage. This is because the fine stage operates in a residual manner, predicting subtle flow-based refinements on top of the coarse deformation. These updates are typically small and designed to model secondary dynamics (e.g., acceleration, damping), rather than introducing large displacements that could lead to new penetrations.\n\nBased on our observations, we omitted SDF supervision in the fine stage, and also especially since real-time SDF evaluations are computationally expensive in training. That said, we agree that incorporating lightweight geometric constraints in the fine stage could further improve robustness under fast motion, and we are planning to explore this direction in future work.\n\n3. **The impact of temporal window size**:\n\nLet us clarify what we refer to as \"temporal modeling\". As described in L65–69 and Fig. 2 (Framework Overview), our Stage I consists of two modules:\n\n- Coarse stage: Uses only the current frame’s pose to predict the hair deformation relative to the rigid hair. Since no temporal history is involved, this stage models first-order, pose-conditioned deformation.\n\n- Fine stage: Refines the deformation by predicting per-frame flow vectors $\\Delta x_{t}$ from previously deformed hair. By leveraging information from $t{-}2$ and $t{-}1$, this stage captures second-order dynamics (acceleration, damping), while also ensuring temporal coherence. (L166-173)\n\nSpecifically, the fine (temporal) stage learns the flow vector from the deformed hair at $t{-}1$ to $t$, conditioned on deformation and flow from earlier steps ($t{-}2$ to $t{-}1$). This enables the model to implicitly capture second-order dynamics, such as acceleration, by observing how motion evolves over time.\n\nAcceleration is computed via finite differences of velocity, requiring 3 positions:\n$$\na_t = \\frac{(x_{t} - x_{t-1}) - (x_{t-1} - x_{t-2})}{\\Delta t^2}\n$$\n\nOur fine model predicts $x_t$ based on:\n- $x_{t-2}$ and $x_{t-1}$: previous deformed hair  \n- $\\Delta x_{t-1} = x_{t-1} - x_{t-2}$: the previous flow vector\n\nThe current model already observes the change in flow via $x_{t-2} \\rightarrow x_{t-1}$ and it implicitly captures second-order effects (acceleration) using two positions and one velocity vector. With enough training data, it can learn acceleration-like patterns from local motion trends. We agree that expanding the temporal window ($t{-}3$, $t{-}4$) could help capture longer-term trends, we find the two-frame design effective for modeling higher-order dynamics.\n\nThis capability is demonstrated in our video (02:35–02:48), where we can see the damping effects. We will clarify this temporal modeling strategy and include the window size discussion in the revised version."}, {"review_id": "wQSE1pcp1T", "reviewer": "Reviewer_NCwA", "summary": "The 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.", "questions": "See Weaknesses", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "I'd like to maintain my initial rating for this paper. Most of my concerns have been adequately addressed and the presented experimental results are convincing. Both the dataset and the proposed method can serve as solid platforms for followers to improve upon.", "rebuttal": "Thank you for the thoughtful feedback! We address your concerns in below and will do our best to incorporate the suggestions (equation clarity, section orders, etc ):\n\n1. **Equation clarity**:\n\n(a) From L161-163, and in Eq. (1), no clear formula for $\\mathcal{L_\\text{point}}$ and $\\mathcal{L_\\text{SDF}}$.\n\nThank you for noting the missing explicit definitions of $\\mathcal{L_\\text{point}}$ and $\\mathcal{L_\\text{SDF}}$ in Eq. (1). While briefly described in L162–163, we agree that providing formal formulas will improve clarity.\n\n-  Point MSE loss\n$$\n\\mathcal{L_\\text{point}} = \\frac{1}{N} \\sum_{i=1}^{N} \\left\\| \\hat{\\mathbf{p}}_i - \\mathbf{p}_i^{\\mathrm{GT}} \\right\\|_2^2\n$$\n\nwhere $\\hat{\\mathbf{p}}_i$ is the predicted 3D hair point and $\\mathbf{p}_i^{\\text{GT}}$ is the corresponding ground-truth hair point.\n\n- SDF penalty loss\n$$\n\\mathcal{L_\\text{SDF}} = \\frac{1}{N} \\sum_{i=1}^{N} \\max\\left( 0,\\ -\\mathrm{SDF}(\\hat{\\mathbf{p}}_i) \\right)\n$$\n\nwhere $\\mathrm{SDF}(\\hat{p}_i)$ is the signed distance to the body surface, and we penalize points inside the mesh by applying ReLU.\n\nWe will include these definitions in the final version.\n\n(2) Tangent vector definition.\n\nThe hair tangent vector represents the local hair direction and the visual definition is in Fig. 3. And Yes, the formal definition is given in Eq (7). Thank you for the suggestion, we will clarify the explanation order in the revision.\n\n(3) Section structure.\n\nThank you for the suggestion. We will reorder the sections as suggested to improve the logical flow.\n\n2. **Training strategy**:\n\nOur learning-based framework follows a two-stage training strategy, as described in Introduction (L57–75). The model is not trained end-to-end (to be explored in future work). We first train a hair deformation model that includes a secondary dynamics module, and then train a separate appearance model. Training times for each model are provided in the Supple. (L101–103). During inference, we first predict hair dynamics, then generate appearance from the deformed hair.\n\n3. **Real-world generalization and hair complexity**:\n\nWe understand the concerns regarding real data testing. Our model is robust to real-world hair complexity, supported by a well-prepared synthetic dataset and a learning-based architecture. As the first data-driven framework for dynamic hair modeling with 3DGS representations, our method also offers a novel solution to the rigid hair problem in Gaussian head avatar re-animation.\n\nDue to NeurIPS policy, we cannot include PDFs for visual results this year. However, we have conducted new tests combining additional motion with pre-trained real human Gaussian avatars, demonstrating that our model serves as a novel and effective supplement for multi-layer Gaussian avatar re-animation.\n\nTo capture lighting effects, our appearance model estimates spherical harmonics (SH) coefficients as a compact color representation. The training dataset includes artist-designed hair shaders under natural lighting conditions to simulate real-world scenarios.\n\nWhile our current setup is based on synthetic data, we acknowledge its limitations and consider future directions such as dataset expansion and hair relighting to further improve realism and generalization.\n\nAs noted in the Introduction (L51–52) and Limitations (Supple. L155–157), capturing strand-level dynamic hair in-the-wild is challenging due to hardware limitations and the lack of robust hair tracking. To address this, we created a large-scale, lab-controlled synthetic dataset driven by various head movements (Main L243–249, Supple. A). This dataset includes diverse head poses and motions, and photorealistic rendering, which we believe generalize well to real scenarios, as demonstrated in video (03:11).\n\nAs mentioned in Main L87, we also plan to release our synthetic dataset to support and accelerate data-driven dynamic hair modeling."}, {"review_id": "SdsNoCbg5x", "reviewer": "Reviewer_dW3z", "summary": "This 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.", "questions": "- How robust is this method to novel head poses and movements, such as a sudden downward movement of the entire head and body?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# 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.", "was_revised": true, "final_justification": "My major confusion on naming and the curvature-aware blending components is clarified by the authors. Therefore, I am glad to still vote for acceptance.", "rebuttal": "Thank you for the thoughtful feedback! We address your concerns in detail below and will make our best effort to incorporate the suggestions:\n\n1. **Stage naming ambiguity**:\n\nThank you for pointing out the naming ambiguity. This will be clarified.  As shown in Fig. 2, our framework consists of two main stages:\n\n- Stage I: Coarse-to-fine dynamic hair modeling\n- Stage II: Appearance optimization\n\nWithin Stage I, we adopt a coarse-to-fine design:\n- The coarse stage learns hair deformation from static hair.\n- The fine stage refines secondary dynamics (e.g., inertia, damping).\n\nTherefore, the terms \"fine stage\" and \"second stage\" refer to different modules.\n\nTo improve clarity, we provide the following table:\n\n| **Module**              | **Function**                                  |\n|-------------------------|--------------------------------------------------|\n| Stage I (coarse stage)  | Learns hair deformation given head motions  |\n| Stage I (fine stage)    | Adds secondary motion refinement                 |\n| Stage II                | Optimizes dynamic Gaussian hair appearance       |\n\nAs this caused confusion, we will rename Stage I and II as *Hair Dynamics Model* and *Appearance Model*, in the revision.\n\n2. **Curvature-based Gaussian blending**:\n\nThank you for the insightful question. As stated in our hypothesis (L218–220), each hair strand is composed of discrete segments, which can lead to appearance discontinuities, particularly in high-curvature regions where the tangent direction changes abruptly.\n\nWhile curvature does not directly impact visibility (which is related to camera viewpoint and depth ordering), it significantly affects the continuity of shading and appearance across segments. \n\nUnlike continuous surfaces, Gaussian hair treats each segment as an independent shading unit, each with its own tangent vector $\\mathbf{t_i}$. In high-curvature regions, if the angle between $\\mathbf{t_i}$ and $\\mathbf{t_{i+1}}$ becomes large, it will lead to discontinuities in shading intensity. Ideally, this can be mitigated by increasing segment density, but usually for hair tracking purpose, we use a fixed number of segments per hair strand, so an alternative approach is to apply curvature-aware blending to smooth visual transitions.\n\nHere we give a simple hair diffuse shading formulation:\n$$I_i \\propto \\max(0, \\mathbf{t_i} \\cdot \\mathbf{l})$$\nwhere $I_i$ is the shading intensity of the $i$-th hair segment, $\\mathbf{t_i}$ is its tangent direction, and $\\mathbf{l}$ is the light direction. And the shading discontinuity between adjacent Gaussians is:\n$$|I_i - I_{i+1}| \\propto \\left| \\max(0, \\mathbf{t_i} \\cdot \\mathbf{l}) - \\max(0, \\mathbf{t_{i+1}} \\cdot \\mathbf{l}) \\right|.$$\n And this value increases with curvature. Our curvature-based blending design reduces this effect by weighting contributions from adjacent segments according to their angular deviation, ensuring smoother appearance without increasing segment density. Without blending, high-curvature areas may exhibit visual artifacts such as shading discontinuities, as shown in Fig. 7 (with zoom-in) and the video demo (02:57–03:09; quality may be reduced due to video compression).\n\nWe will include additional visual comparisons in the revision to further illustrate this contribution.\n\n3. **Robustness to unseen motion patterns (e.g., sudden downward movements)**:\n\nThanks to our coarse-to-fine dynamic hair modeling framework (Fig. 2 Framework Overview) and training on large-scale motion sequences (Supple. A), our model is robust to unseen head motions, including sudden downward movements. As shown in the demo video (02:34–02:48), under a sudden head drop and turn, the coarse stage captures the primary deformation, while the fine stage models secondary motion (e.g., damping effects) and preserves realistic hair dynamics. Due to NeurIPS policy, we cannot include additional PDFs for visual results at this stage. As suggested, we will add more qualitative results, including re-animation results with dynamic hair merged into real human avatars, in the final version to further illustrate this."}, {"review_id": "qzZxUkjmYg", "reviewer": "Reviewer_hDbX", "summary": "This 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.", "questions": "[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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### **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.", "was_revised": true, "final_justification": "This paper proposes a neural simulation and rendering framework for hair dynamics on the synthetic data. Though there are still room left for improvement,  considering the contribution and progress made since the last round I reviewed this paper, I acknowledge the authors' efforts and lean toward acceptance for this submission.", "rebuttal": "Thank you for the valuable feedback! We also appreciate your positive view of the improvements in our learning-based framework. We will do our best to address your concerns as detailed below.\n\n1. **Material property sensitivity**:\n\nWe agree that traditional physics-based simulation models benefit from explicitly defined physical parameters (e.g., stiffness), which allow direct control across material types. In contrast, our approach formulates dynamic hair modeling as a data-driven task within the 3DGS representation. For example, here we would like to explain with some simple formulations:\n\n- Physics-based formulation:\n\n$$\nm \\frac{d^2 \\mathbf{x}}{dt^2} = -k (\\mathbf{x} - \\mathbf{x}_{0})- c \\frac{d\\mathbf{x}}{dt} + \\mathbf{F}\n$$\n\nThis is a simplified spring-damper system formulation, left side indicates “how much the hair wants to move,” and the right side states “what is making it move.”, here $m$ is the mass of the hair segment, $\\mathbf{x}$ its current position, $\\mathbf{x}_{0}$ the rest position, $k$ the stiffness, $c$ the damping coefficient, and $\\mathbf{F}$ represents external forces such as gravity. And the movement of each hair segment is controlled by these parameters.\n\n- Data-driven formulation:\n\nHere we show a simplified high-level formulation of data-driven model. We define a neural network $f_\\theta$ that models hair dynamics, where $\\theta$ denotes the learnable parameters. Given a hair current position $\\mathbf{x}^i$, motion flow vector/velocity $\\mathbf{v}^i$, and a style feature $\\mathbf{s}$ (e.g., hair volume latent code), the network predicts a motion update:\n\n$$\n\\Delta \\mathbf{x}^{i+1} = f_\\theta(\\mathbf{x}^i, \\mathbf{v}^i, \\mathbf{s})\n$$\n\nThe next position is obtained via residual addition: $\\mathbf{x}^{i+1} = \\mathbf{x}^i + \\Delta \\mathbf{x}^{i+1}$.\n\nTherefore, rather than manually tuning physical parameters, our model learns motion priors from a large, diverse dataset (see Supple. A). While material properties (e.g., stiffness, damping) are not explicitly modeled, style features such as the hair latent code $\\mathbf{s}$ serve as implicit priors that guide the learned dynamics in our data-driven framework. This enables robust generalization across unseen motion patterns without hair parameters tuning.\n\n2. **Realistic motion coverage and evaluation**:\n\nWe agree that realistic and high-energy motion is essential for evaluating dynamic hair behavior. While the video highlights select cases (e.g., increased velocity), we are adding more diverse high-energy examples. \n\nHence, to further evaluate our model, we did evaluations on the public motion capture dataset Mixamo. We selected the “Nervously Look Around” sequence, which contains relatively high-energy head motions. For testing, we extracted the head rotation and upper body mesh, and used them as inputs.\n\nWe applied this motion to 3 hairstyles: Blowout, Ponytail, and Curly, to assess generalization to unseen, high-energy inputs.\n\nVisually, our model generates smooth, and coherent dynamics by sharing volumetric priors across neighboring strands, resulting in unified, directional hair motion even under energetic inputs. Due to NeurIPS policy, we cannot include visual results via link at this stage. Instead, we report the L2 error metrics below, computed against physics-based XPBD ground truth over a 100-frame subsequence, and will include qualitative comparisons in the final revision.\n\n| Hairstyle | Ours (L2 Error ↓) | Rigid (L2 Error ↓) |\n|-----------|--------------|-------------|\n| Blowout   |    0.1307   |   0.2976    |\n| Ponytail  |    0.0965    |   0.2547   |\n| Curly     |    0.1158   |   0.2011   |\n\n3. **The necessity of the proposed dynamic hair model**:\n\nThe motivation for designing a learning-based dynamic hair model (Stage I) is the integration with the 3DGS representation, to enable dynamic Gaussian hair as a separate layer that integrates seamlessly with multi-layer Gaussian head avatars (Main L16–18). Unlike prior works [1,2] that focus on static hair or lack realistic dynamics [3], our volumetric formulation allows plausible hair deformation prediction from a dense point cloud or pre-trained 3DGS, without requiring explicit body meshes.\n\n- DGH pipeline (Ours):\n\nPre-trained head GS avatar  \n→ Re-animation  \n→ DGH predicts dynamic Gaussian hair deformation (from half-body GS / point cloud)  \n→ Merge with head GS  \n→ Final avatar with dynamic hair\n\n- Physics-cased pipeline:\n\nPre-trained head GS avatar  \n→ Re-animation  \n→ Convert to explicit mesh  \n→ Run physics-based hair simulation  \n→ Render with 3D engine and shaders to obtain hair appearance  \n→ Convert rendered hair to 3DGS representation  \n→ Merge  \n→ Final avatar\n\nUnlike physics-based simulators, which rely on explicit meshes and require additional rendering and conversion steps, our DGH framework only needs head rotation and a dense point cloud/pre-trained GS avatar. By converting static hair and upper-body into volume, we enable mesh-free deformation prediction, making our model more compatible with Gaussian-based avatars and easier to integrate into learning-based re-animation pipelines without additional rigging or simulation overhead.\n\nTo date, neural-based simulators are mostly quasi-static [4] and do not model dynamics (e.g., damping effect, acceleration) and upper-body (shoulder) intersections with realism [5]. We also view our learning-based model as a complementary alternative to traditional simulators. Rather than relying on explicit material parameters, it learns hair motion priors from data. We believe with more hairstyle data, this framework holds potential for a more generalizable and comprehensive solution to dynamic hair modeling. We also want to highlight that DGH also enables photorealistic rendering of dynamic hair (using 3DGS) at lower latency than traditional rendering with 3D engines running sophisticated hair shaders with physics-based simulation needs rendering on top. We have new results showing our dynamic hair integrated with real human Gaussian avatars, which we will include in the revision.\n\nAnd we thank you again for the thoughtful suggestions, especially Q1, which provided valuable insights and inspired us to explore future extensions. As suggested, we are interested in exploring lightweight physics-inspired constraints to further enhance realism in future work.\n\n[1] Luo, Haimin, et al. \"Gaussianhair: Hair modeling and rendering with light-aware gaussians.\" arXiv preprint arXiv:2402.10483 (2024).\n\n[2] Zakharov, Egor, et al. \"Human hair reconstruction with strand-aligned 3d gaussians.\" ECCV 2024.\n\n[3] Qian, Shenhan, et al. \"Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians.\" CVPR 2024.\n\n[4] Stuyck, Tuur, et al. “Quaffure: Real-Time Quasi-Static Neural Hair Simulation”, CVPR 2025\n\n[5] Wang, Ziyan, et al. “NeuWigs: ANeural Dynamic Model for Volumetric Hair Capture and Animation”, CVPR 2023"}]

Author rebuttal:

Meta-review: How to generate photorealistic dynamic hairs is a challenging and on-going problem in digital human synthesis. This paper proposes an effective framework to learn hair dynamics and appearance. Extensive experiments have been conducted to verify the effectiveness of each component and demonstrate the superiority of the proposed method to other SOTA approaches. There are totally four reviewers handling this paper. Among them, three reviewers voted “Borderline accept” and the other one suggested “Accept”. Furthermore, most of the reviewers stated clearly in the Final Justification that they leaned towards accepting this paper after discussing with the authors. The AC agrees with the reviewers that this paper deserves to be published at NeurIPS due to the above-mentioned merits. The authors are recommended to carefully revise their paper according to the reviewers’ suggestions.
Decision: Accept (poster)