Secondary Motion-Aware 3D Clothed Gaussian Avatars from Monocular Videos
Authors: Seungeun Lee
SeungJun Moon
Hah Min Lew
Ji-Su Kang
Gyeong-Moon Park
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Computer Vision
Neural Rendering
3D Avatar Modeling
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose a 3D Gaussian splatting avatar method that realistically models loose clothing and dynamic appearances from monocular videos, overcoming the limitations of template-based approaches.

Abstract: Recent advances in neural rendering, particularly 3D Gaussian Splatting (3DGS), have enabled animatable 3D human avatars from single videos with efficient rendering and high fidelity. However, current methods struggle with dynamic appearances, especially in loose garments (e.g., skirts), causing unrealistic cloth motion and needle artifacts. This paper introduces a novel approach to dynamic appearance modeling for 3DGS-based avatars, focusing on loose clothing. We identify two key challenges: (1) limited Gaussian deformation under pre-defined template articulation, and (2) a mismatch between body-template assumptions and the geometry of loose apparel. To address these issues, we propose a motion-aware autoregressive structural deformation framework for Gaussians. We structure Gaussians into an approximate graph and recursively predict structure-preserving updates, yielding realistic, template-free cloth dynamics. Our framework enables robust dynamic appearance modeling under the single-view constraint, producing accurate foreground silhouettes and precise alignment of Gaussian points with clothed shapes. To demonstrate the effectiveness of our method, we introduce an evaluation dataset featuring subjects performing dynamic movements in loose clothing, and extensive experiments validate that our approach significantly outperforms existing 3DGS-based methods in modeling dynamic appearances from monocular videos.

SECONDARY AETTON-AWARE 3D CLOTHED GAUSSIAN AVATARS FROM MONOCULAR VIDEOS
Seungeun Lee', Seung.Ju Moon Hah Min Lew", Kang', Gyeong-Moon Park Kllcon Al Research," "LLWRDD, "Korea University
ABSTRACT
Recent advances in neural rendering, particularly 3D Gaussian Splatting (3DGS), have enabled animatable 3D human avatars from single videos with efficient render- ing and high fidelity. However, current methods struggle with dynamic appearances, especially in loose garments (e.g.. skirts), causing unrealistic cloth motion and needle artifacts. This paper introduces a novel approach to dynamic appearance modeling for 3DGS-based avatars, focusing on loose clothing. We identify two key challenges: (1) limited Gaussian deformation under pre- pre-defined template articula- tion, and (2) a mismatch between body-template assumptions and the geometry of loose apparel. To address these issues, we propose motion-aware autoregres- sive structural deformation framework for Gaussians. We structure Gaussians into an approximate graph and recursively predict structure- preserving updates, yielding realistic, template-free frree cloth dynamics. Our framework enables robust dynamic appearance modeling under the single-view constraint, producing accu- rate foreground silhouettes and precise alignment of Gaussian points with clothed shapes. To demonstrate the effectiveness of our method, we introduce an evaluation dataset featuring subjects performing dynamic movements in loose clothing, and extensive experiments validate that our approach significantly outperforms existing 3DGS-bases methods in modeling dynamic appearances from monocular videos.
INTRODUCTION
Creating animatable 3D avatars from monocular videos involves reconstructing lifelike, controllable representation of a person capable of replicating both primary motions (i.e., movements of major body parts), and secondary motions (i.e., time-varying cloth dynamics). Achieving this capability is critical for immersive experiences in fields such virtual reality, telepresence, and interactive entertainment, where realistic human representations significantly enhance user engagement (Sutherland et al. 1965; Lee al., 2024). With the advent of 3D Gaussian Splatting (3DGS) (Kerbl & al. (023)) high-quality neural rendering becomes feasible, substantially improving avatar realism and efficiency in synthesis from monocular videos.
However, existing 3DGS-based avatar methods (Lei et al., 2024 Hu et al. 2024a Moon et al. 2024
Qian et al. 2024b) predominantly excel at modeling primary motion but exhibit limitations in robustly capturing secondary motion, illustrated in Fig. D.. where it shows the animation result of the 3D Gaussian avatar in a novel pose exhibiting a dynamic posture unseen during training. This is due to the fact that they rely skeletal skinning of coarse meshes for animation (Loper et al.) (2015), which inherently lacks subtle deformation effects such as inertia-driven soft-tissue dynamics. Consequently, it remains challenging to consistently reproduce these nuanced motions using neural networks alone.
There are two major challenges that hinder the secondary motion-aware dynamic appearance modeling in creating animatable 3DGS avatar from single video: (1) temporal context-unawant Gaussian deformation, and (2) cloth shape-agnostic Gaussian point initialization. First, existing methods deform the Gaussians of clothed avatar function of the current body pose, which is aligned with the sampled images, to learn the appearance of a 3D Gaussian avatar. While this approach effectively captures the primary motion of main body parts, it is limited in representing secondary motion in garments such as dresses and skirts, which is strongly entangled with temporal continuity (Fig. [T).
"Corresoonding author

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(a) Challenge: Appearance Modeling Including Sccondary-Motion Dynamies.
Driving Pose
Existing Monocuar 3DGS- based Avatars
Ours
(c) Motivation 2: Difliculty in Representing Loose-Fiting Clothes.
(b) Motivation
iitivaatinn 1: Temporally Inconsistent Deformation.
100
Ours
80
morton error
60
100
200
time
300 .000
500
Reference Image
Points (Base)
GS Points (Ours)
Figure 1: Conventional 3DGS-based avatars (Base) fail to model the dynamic appearance of subjects wearing
loose garments, particularly in scenarios involving secondary motion (Lei et al. 2024 Qian al. 20246 Hu
et al. 2024a Moon et al. 2024). (a) Our method overcomes these limitations, enabling high-fidelity rendering of Gaussian avatars exhibiting dynamic motion from a single video. (b) In contrast, conventional methods define deformation through pre-specified articulation models such as linear blend skinning, which operate independently each frame without accounting for temporal context- often leading motion error spikes that indicate poor alignment with driving signals. (c) Furthermore, for initializing 3D Gaussians, they rely on parametric template model resembling a naked body, which struggles to generate Gaussians for clothing regions deviating from the hody surface, especially with loose-fitting garments.
Secondly, explicit representations based 3D Gaussians are highly sensitive the accuracy of their initial point placement, as widely discussed in primary 3DGS studies (Yu et al.] 2024 Luiten et al. 2024). However, existing methods for creating 3DGS-based avatars from monocular videos (Lei et al. 2024, Hu et al.] [0244a] |Moon et al.) 2024] Qian et al.] 2024b). rely on parametric template models initialize the shape of articulated subjects. These template models represent a naked body shape, leading to significant discrepancies between the initialized points and the actual shape when dealing with subjects wearing loose-fitting garments (Fig. [T)). Therefore, few Gaussians should represent not only the body parts but also the appearance of the clothing, causing artifacts in novel pose animations where the model has not observed similar poses during training.
In this paper, we present novel framework for modeling dynamic appearances of loose-fitine fitting garments in 3D avatars, explicitly addressing the challenges posed by secondary motion. Our approach introduces a two-stage process, starting with personalized Gaussian initialization to accurately align Gaussian primitives with clothed silhouettes without relying on restrictive naked-body templates. Central to our system is the Secondary Motion-Awai Gaussian Deformation (SMAD) module, which constructs a velocity-encoder Gaussian graph and autoregressively predicts second-orde Gaussian dynamics. By moving beyond the limitations of skeletal linear blend skinning, SMAD enables realistic modeling of fine-grained cloth motion such as inertia-driver flutter while strictly preserving structural coherence during deformation to ensure high-fidelity rendering across diverse and unseen poses.
Our contributions are summarized follows:
We propose novel method for animatable 3D avatar reconstruction based on 3DGS, which enables dynamic appearance modeling of dressed avatars.
We propose a secondary motion-aware Gaussian deformation, introducing a velocity- encoded Gaussian graph representation that autoregressively estimates Gaussian dynamics.
Extensive experiments demonstrate that our method outperforms existing methods on the subjects wearing loose-fitting clothes with dynamic movement.
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2 RELATED WORK
Animatable 3D Avatars from Multi-view Videos. has long been a major focus in vision and graphics. Early systems (Stoll et al. 2010, Alldieck et al., 2018 Joo et al. 2015 Pons-Moll et al.) 2017] Habermann et al.J 2019) reconstructed actors in multi-view studios and animated meshes via multi-view geometry and hand-crafted articulation designs. While these approaches empower the controllability, it required substantial expert intervention. The shift to implicit neural
representations, especially neural radiance fields (Mildenhall al. [021). introduced photorealistic
neural avatars (Peng et al.
202 a Habermann & et [01]] Zheng et al., 2023 Shen et al. 2023b
Li al. 2023 Zhu et al., 2024] Shen et al.] 2023a Yin et al. 2023b, Chen et 2024] Saito et al.
2024) and free-view synthesis (Kwon ct al. 2021 Liu al. 2021 Isik et al., 2023 Kwon et al.
2024b), though often with slow training and additional structural constraints for stable driving. The
3DGS (Kerbl & et al. 2023) further achieved efficient rendering with high fidelity (Li et al. 2024
Zielonka et al. 2025; Zheng et al. 2024, Kwon et al., 2024a Lin et al. 2024. Zhan et al. 2025
Liao et al. 20.4). Yet. their high- fidelity performance fundamentally relies dense, calibrated
multi-view supervision, geometry constraints, and explicit subject-specific ground-truth template meshes. By contrast, our method is deliberately designed for the single-video setting, aiming to create user-friendly animatable clothed avatars directly from casual monocular footage.
Animatable 3D Gaussian Avatars from Monocular Videos. Advances in neural rendering and markerless motion- caponne techniques have enabled the construction of user-friendly 3D avatars from monocular videos. With these advancements, it has been to learn a neural implicit representation defined in continuous canonical space near template mesh, and to deform this representation into the observation space using predefined articulations driven by motion inputs (Su et al. 2021 Weng ctt al. 22200; Chen et al.) 2021; WWen al. 2022; Wang et al.) 2022; Yu et al. [2023 Jiang et al. 2023a b). The advent of 3DGS has further accelerated photo- modeling of 3d avatars; several works attach Gaussian primitives to skeletal model and learn pose-conditioned deformations from
monocular videos (Qian et al. 2024b Hu et al. 2024a Moon et al. 20224, Shao et al., 2002 Lei
al. 2024 Hu et al.) 2024b Zhai et al. 2025 Guo et al. 2025). However, existing methods assume Template (Loper et al. 2015 2 basee initialization and its pre-defined articulation, which struggle to capture subtle, temporally coherent non-rigid effects. We build upon other line, introducing two key aspects: a template-free initialization that directly aligns the Gaussian primitives, eliminating the need for naked-body templates; and a pessics-inspired autoregressive deformation module that predicts velocities and accelerations with finite difference method (Xie et al., (024), and captures second-order dynamics through a velocity-encoder Gaussian graph.
Dynamic Clothed Human Modeling. Beyond primary motion driven by the main body movement, several works Ma et al. (202T) 2022); Prokudiin et al. (223); [Seth et al. (2025) have also considered
secondary motion, such as cloth dynamics. One line of works (Habermann al., 2020 Habermann
& et al., 2021]] Habermann et al.) [021]: Liao et al.) 2024 Feng et al. 2022 2023 Guo et al.) 2023 2024) reconstructs clothed surfaces using neural implicit representations, but canonicalization with predefined articulation struggles with loose garments. Another approaches combines non-rigid deformation with LBS and neural networks, yet requires subject-specific ground-truth clothed meshes. A separate direction incorporates physics simulation by numerically solving differential equations of the dynamic systems (Terzopoulos et al. 1987; Müller et al.) 2007 Maakkin et al. 2016). However, they are computationally expensive and difficult to parametrize: To alleviate this, several works (Pan) et al, 2022, Santesteban et al. 2022 Grigorev et al. 2023 2024) approximate dynamic systems with neural networks--e c.g., augmenting models with virtual bones employing recurrent architectures to predict garment -dffommattoooootiionnnnn sequences, but depend on high-level supervision such as 4D scans, booy segmentation, and explicit colliders, and operate only on polygonal meshes. Our work is different lines of this work; we aim to create avatars represented as 3D Gaussian primitives and model its dynamics and appearances, given only from monocular videos, without access to any 3D ground-truth or prior geometric knowledge.
3 METHOD
Given a monocular RGB video V = (4)T= capturing a human subject in motion, our goal is to reconstruct fully animatable 3D Gaussian avatar that faithfully models dynamic appearances of
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Personalized Gaussian Initialization
Secondary Motion-Aware Gaussian Deformation
Groph Approiintion
Canenical Gaussian
Fy
Deformer
Deformer
Deformer
caformation
G8 Renderw
Mip
Velocities
fr
57 SF
Figure 2: To model sccondary motions in 3DGS-based avatars, we introduce a two-stage framework: (1) Personalized Gaussian Initialization using deformable NeRF to estimate canonical Gaussians G" and (2) Secondary Motion-Aware Deformation. 9 are structured as Gaussian graph r, processed by a GNN-based autoregressive deformer, decoded via Ue into deformed Gaussians G Motion descriptors derived from poses θ guide temporally coherent deformation. Then GS Renderer then synthesizes the final images.
loose-fitting clothed subjects. We adopt a dynamic set of 3D Gaussian primitives whose spatiotempo- ral properties evolve over time to capture complex non-rigid deformations, such as clothing dynamics. Formally, we represent the avatar at time as a set of N deformed Gaussians:
g.¹ (Ju,i Ei,i, Ct,i, Ct,i, (r,))
where fe, E 333 is the 3D mean position, Σe, E p3x3 is the covariance matrix modeling spatial extent and orientation, Cr R3 denotes RGB color, and 04,1 E R represents opacity. The number of primitives N remains fixed across time, but their parameters are dynamically updated to reflect conditioning motion, video 1 (T, = R(GD) by projecting the deformed Gaussians through differentiable splatting renderer R. Fig. iiluutratt the overall process of the proposed method.
of SMPL poses
{0.)-=1- We then obtain the animated rendering
Baselines. We adopt simple baseline that obtains dense canonical Gaussian primitives using a 4D NeRF (Pumarola al., 202T). Concretely, train deformable neural radiance field the input monocular video and map each observation-space point at time t to canonical space (reference time). By querying color and density in the canonical space, we recover a dense canonical density field that captures both body and loose clothing without relying
parametric template.
After training, we extract canonical Gaussians by thresholding the time- averaged canonical density (x) + Σ, o (x, t) and clustering the surviving voxels to obtain Gaussian centers {4f}, }. with isotropic variances {E{) and colors {. This yields a dense set of canonical 3D Gaussian primitives g that serves the person-specifie Gaussian initialization (PGI) for subsequent stages.
3.1 VELOCITY-ENCOME GAUSSIAN GRAPH
To overcome the limited capability of representing secondary motions caused by reliance on linear blend skinning of parametric template human models (Loper al. 2015 Pavlakos et al. 2019). we propose autoregressive Gaussian deformation method that moves beyond the template model. Furthermore, to ensure robust performance even when the number of Gaussians grows exponentially and to alleviate computational complexity, propose graph-based deformation approach that approximates Gaussian interactions.
Graph Construction. Given set of N initial Gaussian points {Mi, U2. (NN. J. we downsample the Gaussian points to E R x3 (M & N) with voxel-grid downsampling (Rusu & Cousins 2011) these M nodes serve the final Gaussian primitives used for rendering. We then construct graph = (X, where A E M M. is adjacency matrix. It is constructed via k-Nearest Neighbors (K-NN) by computing pairwise distances d(x,, Xj) ||x; x;112, Vx; X, E X. Each element of
4

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d(xx,,)) ) Pd
Auy is formulated as exp
where Pa controls sensitivity to distances.
Velocity Encoding (VE). We build the node features H = [h), h2 ..... hw) at each node position x, Let us consider h, as concatenation of the node position x; and its velocity vz(() x.(-)_x Ar the time state Furthermore, to capture long-range dependencies, we buffer the past Tr memory vectors as set of v. (t), vi(t - .),..., vi(t Tu To condition set of body pose priors Ir- = {θ,, 0z-1,..,, 02-5 with time window т, we additionally embed it to E = {el, ...... eM with MLP. At this end, the node feature h, is defined h, (X,, V., C2).
3.2 SECONDARY MOTION-AWARE DEFORMATION (SMAD)
Our goal to move beyond linear blend skinning (LBS) with parametric body models (Loper et a 2013) and learn an animatable 3DGS avatar that can faithfully reproduce secondary motions. Motivated by deformation methods that generalize to unseen motions without relying on pre-defined kinematic hierarchies (Zheng et al. [202]] Grigorey et al. 2023), employ graph neural network (GNN) deformer that autoregressively predicts the non-rigid dynamics of human bodies hence, of Gaussian primitives.
Definition. We model each Gaussian node as point mass qi whose motion follows second-order mass prass-spring damper system (Terzopoulos et al. 1987; Provot et al. 1995). Let xt E R3 and (t) (t) E R denote the position and velocity time f. The dynamics are
(() = 9t x1) + ni (1) Σ, Kig (1( x,(1)
rest 13
where (1) = (t) is acceleration, "/ is a damping coefficient, kij is the spring stiffness between nodes and j. Lest is their rest offset in canonical space, and Foxt (L) is external driving force.
With a discrete step Al, we apply explicit Fuler integration:
1 Fext (1)
(1(
7/ vi(l)
Σ Kis (x:(1) - xx L
(3)
9i
v.(L + At) v() + (!);(),
x:(1 + At) (1) + At At + At).
(4)
This second-order formulation naturally induces secondary motion (e.g., cloth flutter). In practice, we let message-passing GNN (Gilmer et al. 2020) learn these updates rather than prescribing forces explicitly.
Architecture.
The Gaussian graph deformer parameterizes the above updates with a message-
passing GNN. Each node i carries feature h; (t) e Rd obtained from Sec. 3.1 At time i, node i aggregates information from its neighbors using an adjacency (ty With an MLP Ma, we define messages
my (1- Mo (h,(t), hy(()) E dm
m ACE (1)
(1)= Ab(() A,)00 m)-.()))
(5)
Two update functions then produce the next-step node feature and physical state:
((()) = Fo (h m (F)). a.()) = Go (hf((), m WEE (D),
(6)
(7)
where G3 serves as a neural surrogate for the mass spring damper updates in Eq. After passing through final pressage-passing layers, we obtain updated positions and velocities for all nodes. Each node corresponds to a deformed Gaussian Gj,. and we finally set:
+ x, (1+ At),
C₁ Σ, + (z h())),
(8)
where 2; E Rd is a learned latent code and Up predicts color, opacity, and covariance for each Gaussian.
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Training Objectives. After computing 9 = {9 G ...,.... G we render it via Gaussian Splatting- based rasterizer R to I, = R( We define a total SMAD loss term LSMAD as:
LSMAD = LRGB + Xiso + Admmp
(9)
We mainly use the common 1.1 rgb photometric loss between rendered images and ground-truth images, which is formulated as:
LRGB
[ - Iall1
(10)
minimizes the pixel intensities of rendered Gaussians R to the ground truth images 4. In addition, we utilize two regularization terms:
N
Lis
Σ (Ix: Xy | 11,² (R,))E
deamp
lv,(()))2.
(11)
The isometry Loss Liso penalizes deviations in geodesic distance preserve local surface area. prevents stretching or shrinking of garment regions; useful for preserving cloth realism during motion. We set Aiso 0.1, where emphasize length preservation. The damping Loss Liang regularizes velocity magnitudes to reduce high-frequency vibration and dynamic instability. It reduces visual fluttering or noise in motion, especially noticeable in fine cloth edges. We Adamp = 0.01, where avoids over-constraining dynamic details.
4 EXPERIMENTS
Dataset. ZJU-MoCap (Peng et al. 2021a) is a primary benchmark for animatable 3D avatars. Using HumanNeRI (Weng et al. 2022) split sequences, we report novel view synthesis results due to limited pose diversity. To supplement the lack of motion variation and loose-fitting garments, we udditionally evaluate on two benchmarks. 4D-Dress (Wang et al. 2024a) firstly introduces world 4D human clothing dataset featuring dynamic cloth motions, designed to advance research in realistic garment modeling and animation. We carefully selected five subjects, each wearing loose-fittin fitting clothing such skirts puffer jackets. We also introduce LoCo-Human, new in-the-wild dataset featuring five Loose-Clow Clothed Humans performing 5 dynamic and 1 static motions per subject. The static sequence is used for training, and the others for evaluating generalization in-the-wild scenarios.
We compare our method with existing approaches on 3DGS-base avatars from monocular videos. Given the extensive body of prior work in this domain, it is practically infeasible to compare against all existing methods. Therefore, we specifically focus on publicly
Baselines & Evaluation Metrics.
available baseline methods (Lei et al. 2024, Hu et al. 2024a Qian al., 2024b) Moon al.) 2024) that explicitly address dynamic appearance modeling. We evaluate the visual fidelity of the rendered animatable avatars with widely used metrics: PSNR, SSIM, and LPIPS. PSNR and SSIM measure pixel-level similarity and structural consistency with the ground-truth images, while LPIPS captures perceptual quality based on deep feature distances. These metrics collectively assess both low-level accuracy and high-level perceptual realism. To quantitatively assess both temporal consistency and how faithfully the animated avatars follow the driving motion, we compute the motion error (Kanazawa et al. 2019) between driving signal and motion estimated from the rendered animations.
4.1 RESULTS
We comprehensively compare our proposed method to state-of-the-art animatable 3D Gaussian avatar methods on three datasets: the 4D-Dress, ZJU-Mocap, and our introduced LoCo-Human in in-the-wild evaluation dataset. We adopt standard metrics including PSNR, SSIM, and LPIPS to quantitatively measure visual fidelity and perceptual quality of animated avatars in rendered images. We first evaluate the ability to synthesize novel poses of dressed avatars on 4D-Dress. As presented in Tab. (a), our method outperforms baseline methods across all metrics, demonstrating superior reconstruction quality. Qualitative comparisons (Fig. 3) further confirm our method's capability to produce realistic cloth dynamics, mitigating common artifacts such as unrealistic garment splitting observed in baselines. Next, formally compare Following the conventional evaluation protocol, we quantitatively and qualitatively assess novel-view synthesis quality (Tab. (b) & Fig. 5). Results indicate that our method consistently achieves superior
method the widely-used ZJU-Mocap benchmark.

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Reference Image
Driving
Avator
3DGGS-vvatar
ExAvatar
Figure Qualitative comparison novel pose synthesis on 4D- Dress dataset. We compare our method to
serveral 3D Gaussian Splatiin-bass basedd Avatars (Lei et al. 2024 Hu et al. 2024a Qian et al. 2024b] Moon et al. 2024). For each subject, we present reference image, driving pose, rendered image and error map to ground-truth image. Our method models robust dynamic appcarances wearing loose-fitting clothes compared to the baselines.
Driving
Excoonar
Figure 4: Qualitative comparison on the in-the-wild LoCo-Human dataset. Given the target driving poses, we animate avatars wearing loose-fitting garments. Compared to ExAvatar (Moon et al. 2024), our method better preserves cloth details and faithfully produces coherent motion under diverse poses. Insets highlight finer garment structures, showing sharper appearances and more realistic deformation.
performance compared to previous single-vin viddo avatar approaches, reflecting improvements in visual sharpness and perceptual realism. In addition, we conduct extended evaluations on LoCo-Human, in-the-wet whid dataset assess the generalization ability of our method in real-world scenarios. As shown in Tab. (c), our approach consistently outperforms existing baselines across diverse subjects. The qualitative results Fig. 4 further support these findings demonstrating the robustness of method even in scenarios involving challenging clothing, complex motions, and various confounding factors. These empirical results suggest that our deformation network, which mimics second-order dynamic system, better captures cloth dynamics compared to conventional deformation schemes based on parametric template models. These extensive experiments validate that our approach effectively addresses critical challenges associated with dynamic appearance modeling from single monocular videos.
Ours
3DGS-Avarar
GART
HumunNeRF
ARAH
Instant-NVR
Figure 5: Qualitative comparison novel view synthesis on 2JU-Mocap (Peng et al. 2021a). Our method yields view-consistent and artifact-free appearance modeling, even for repetitive motions in novel view synthesis scenarios.
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Synthhssis on 4D-Drress Dataset
Method
00148
00120
00185
00187
00190
PSNR1
SSIMT
LPIPS.
PSNR?
SSIMI
LPIPS.
PSNNR
SSIM?
LPIPS]
[SNKI
SSIM!
LPIPS)
PSNR1
SSIMI
LPPPS
2014
20.86
0,9509
0.6661
23.52
0.9622
0,0413
26.84
0,0599
0.0488
25.81
0.901
0.0902
0,9627
0.0175
Gaussian AvaRE Ui R.al
2024a
20.91
0.9512
0.0657
24.12
0,9630
0.0356
26.62
0.9586
0.0500
21.96
0.9317
0.0884
26,44
0.959]
0.0512
3DCSS-Avatrr Oun
22.70
0.9560
0.0471
25.49
0.9636
0J0243
27.54
0.9595
0.0794
25.00
0.939
0.457
29,49
0.9616
0.027K
ExAvator
21.93
0.9536
0.9657
0.0367
0.9618
0.9403
0.9585
Ours
24.74
0.9601
(U0397
27.62
0,9700
0.0301
29.98
0.9673
0.0370
27.71
0.9548
0.0443
20.44
0.9635
0.0342
(b) Novel View Synthesis ZJU-MoCapp
Method
394
393
392
387
386
PSNR1
SSIMI LPIPS.|PSNK
SSIM1
LPIPS.||PSND
PSNNR
SSIM1
PSSRR SSIM
LPIPS)
PSNR1
SSIMI
LPUPS)
NeuraRRdy Pasp al
2021a
29.10
0.9593
0.1545
28.61
0,9500
0.0590
50.10
0.9642
0.0552
27.00
0,9118
0.0594
30.54
0.9678
0.0464
HumanNekF [Wang
2012
30.31
0.9642
0,0228
28.31
0.9633
0.0367
31.04
0.7055
0.9632
0.0855
33.20
0,9752
0.0289
ManoHuman of gr 2053
29.15
0.9595
0.0380
27.64
0.9566
0.0431
29.50
0.9635
0.0394
27.93
0.9601
0.0417
37.94
0.9695
0.0360
ARAII Wang al. 2014
29:46
0.9632
0.0407
28.77
0.9615
0.0423
32.02
0.9711
0,0352
28.19
0,9656
0.0401
33,50
0,9781
0.0314
0.9651
0.0325
0.9620
0.0355
0.736
0.9644
0,9650
3DGS Avalar
Jun et
4024b
30.54
0.9661
0.0312
28.88
0.9635
0.0352
31.66
0.9730
0.0301
28.33
0.9612
0.0322
33.63
0.9773
0.0257
Ours
30,89
0.9677
0.0311
29.48
0,9643 0J0341
32.33
0.9754
0,0194
28,86
0.9650
0.0329
33,86
0.9784
0,0252
LuCConamman (ln-theWWidd)
Method
594
S05
PSNRT
SSIM
LPIPS.|PSNR? SSIMI
LPIPS. SSIM1
LPISS]PPSR) SSIM LPWS)) PSNK
SSIM;
LPIPS.
ADCiS-Avatar QQin al b024b
23.15
0.9374
0.0567
24.21
0.9301
0,0579
23.74
0,9399
0.0594
22.87
0.9337
0.0618
22,59
0,9312
00632
ExAvatur Moon
24.82
0.9478 0.0499
25.07
0.9483
0.0468
24.43
019665
0.0527
23.93
0.9422
0.0533
23.68
0.9426 0.0711
Ours
26.17 0.9576 (.0423] 26.44 0,9589 0.1409 28.76 0.9554 0.01411 25.38 0.9531 0.0467
24.83 0.9517 0.048
Table 1: Quantitative comparisons across (a) novel pose synthesis 4D-Dress, (b) novel view synthesis on ZJU-MoCap, and (c) LoCo-Humal in-the-wild. We highlight the best (bold) and second-best (underline) performance in each case.
4.2 ABLATION STUDY
Loss & Architecture Design. We start from the base configuration of vanilla GNN with pose- dependent deformation, without any physically plausible information. Adding physics-inspired finite-difference method with its regularization (Al) yields clear gain of +0.84 PSNR, and 10.3% LPIPS. Introducing an adaptive spring stifness kij coefficients (A2), which adaptively distinguishes the rigid and non-rigid parts of subjects in unsupervised setting, further improves the rendering quality under dynamic motions. The advanced message- message-passing strategy for GNN with embedding edge features (A3) bring another boost of +0.68 PSNR, and - -10.2% LPIPS. The full configuration with latent codes for time-varying dynamic appearance finally achieves the best results, which is +2.68 PSNR and 31.0% LPIPS reduction over AO, underscoring the complementary roles of physics constraints and graph design.
Velocity Encoding (VE). Encoding an autoregressive window of past velocities markedly improves temporal fidelity. Performance rises monotonically from no VE (BO) to larger horizons, peaking at Ty =1 (B4), with a net +5.83 PSNR and 40.3% LPIPS drop. Very short context (Fe 1) yields limited gains. while overly long horizons saturate; = 11 strikes the best balance between temporal context and feature efficiency.
SMAD Capacity (M). Increasing the number of Gaussian graph nodes improves accuracy up to moderate resolution. Compared to the no-SMAD baseline (CO), capacity scaling to M - 40k (C4) delivers +3.60 PSNR, +0.017 SSIM, and 32.2% LPIPS reduction. Extremely small graphs (< 10k) under-represent non-rigid dynamics, while very large ones (100k) underperform C4, suggesting optimization and overfitting issues at excessive capacity.
Fig. 6 (left) shows that VE reduces motion spikes by 35.5%, with green frames showing stable rendering and red frames showing flickering. Fig. (right) shows PGI improves detail beyond the body, while SMAD removes skirt artifacts present in template-only results.
4.3 ANALYSES
Model Selection, To validate the effectiveness of our proposed design for SMAD module, we additionally conducted a controlled comparison against a carefully designed MLP-based autore- gressive deformer, following (Zheng et al., 2021), and vanilla GNN. This baselines use the same inputs (positions, encoded velocities) as our method, ensuring a fair comparison. Table shows that the MLP deformer fits the training motion but degrades significantly on unseen motion, while the
8

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Loss & Arch, Design
Velocity Encuding (VE)
SMAD Capucity (M)
Method
PSNRT
SSIM
LPIPS)
Method
PSNRT
SSIMT
LPIPS)
Method
PSNKT
SSIM?
LPIPS)
AD:
Rass
25.21
0.952
0.053
BD: wio
22.06
0.930
0.067
CD: SMAD
0.946
0.089
Al:
phys.
26.05
0.956
0.052
B1: Tu
23.41
0.932
0,060
Cl: M = 5k
25.36
0.950
0.053
A2:
-alepiive Reg
26.44
0.958
0.049
B2:
7
24.95
0.944
0.053
Ch: M 10k
26.47
0.958
0.048
A3:
message passing
27.12
0.961
0,944
Tu
0.958
C3: 100k
0.962
0.155
A4:
Full (Ours)
27.89
0.963
0,040
B4: To 11 (Ours) 27.89
0,963
0,040
C4: 40k (Ours)
27.89
0.963
0.040
Table 2: Ablation study on the effectiveness of our mainly proposed components. Three column blocks report (Left) loss/arch. design, (Middle) velocity encoding, and (Right) SMAD capacity M.
WOVE
Oure
Mediur 1010
130
150
Time 5top
Image
Figure 6: Ablation study on the visual effectiveness of (left) VE, (right) PGI, and SMAD. VE significantly reduces the motion error by encouraging temporal consistent deformation. PGI contributes to capturing fine- detailed clothing patterns, and SMAD sufficiently guarantees the robustness of clothing dynamics.
GNN-based deformers remains substantially more stable and accurate. This confirms that the graph- based formulation provides stronger structural priors and better generalization for Nothed-humaan deformation. We also observe that embedding features on edges through message passing yields additional performance gains.
Generalization. Our auto-regressive deformation lever- ages second-order state (x,, Vi), where velocities are obtained via finite differences. This provides physically meaningful motion representation that aligns with how real deformable systems evolve, enabling more stable extrap- olation than pose-only models. Prior work in human and cloth dynamics similarly shows that explicit velocity states improve rollout stability. By integrating over (x,; Vt) and regularizing with damping and local incal-isometry constraints, our model suppresses high- frequeereeecy drift and captures inertia-driven behavior, leading to robust generalization to unseen motions. Table further supports this observation. It reports quantitative results the 4D-Dress subjects across train, test, and out-of-distributie (OOD) motion sequences. To assess whether performance differences across these distributions statistically significant, we conducted paired t-tests for each setting. No comparison yielded a significant difference, indicating that our model maintains consistent performance regardless of motion distribution. This empirical evidence reinforces that our approach generalizes reliably to dynamic motions unseen during training.
Data
PSNR*
SSIM
L.IPSS..
00D
26.51
0.956
0.049
0.963
Train
28.64
0.984
0.037
Table 3: Quantitative results on train/test, and out-of-distribution (OOD) motion se- quences to evaluate generalization capability of our method (blue: p-val > 0.05).
Error Accumulation. It is well-known that auto-regressive models are prone numerical error accumulation over long sequences. To analyze and reflect on this point, we captured two types of motion sequences, each lasting over 30 seconds: (a) a dynamic pose sequence, and (b) repetitive pose sequence. We evaluated our proposed method, and also conducted a comparative analysis with and without our proposed velocity encoding scheme to evaluate effectiveness. Our velocity encoding scheme appears to mitigate this issuc by incorporating history of multiple past states, rather than relying solely on the most recent estimate. This allows the model to remain robust even when the immediate past prediction is noisy, reducing the risk of cumulative drift.
Training Cost. Our model requires an average of 12.5 hours for personalized Gaussian initialization and 4.5 hours for training the secondary motion-aware deformation module, totaling approximately 17 hours on a single NVIDIA RTX 3090 GPU. Considering that existing state-of-the--a methods (Moon) al. 2024) typically require around 4 hours of training, our approach indeed incurs higher computa-

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Model
PSNRT SSIMI LPIPS,
Test
PSNRT SSIMI LPIPS.
Train
Model
PSNNT SSIMT LPIPS) PSNRT SSIMT LPIPS,
dynamic pose
repetitive pose
MLP
25.46
0.954
0.056
27.97
0,973
0.044
We VE
24.47
0.949
0.050
24.69
0,950
0.049
vanilla GNN
28.68
0.958
28.44
0.040
W/ VE (Ours)
25.65
0.955
0.044
26.84
0.960
0.033
Ours
27.89
0.963
0.040
28.64
0.984
0.037
Table 5: Analyses of error accumulation ablating the velocity encoding (VE) strategy on two long sequences.
Table 4: Quantitative results of difference design choices of SMAD train/test distributions.
tional cost. However, we emphasize that, unlike prior methods whose limited model capacity yields only marginal gains even with extended training, our formulation continues deliver significant performance improvements when trained longer (see Fig. (7). This suggests that our method possesses higher effective capacity and is well suited for high-fidelity dynamic appearance modeling in personalized avatar reconstruction.
5 DISCUSSION
On the Importance of Gaussian Initialization. Accurate initialization is fundamental for animatable 3D Gaussian avatars, especially when modeling loose-fitting clothing. Prior monocular methods rely on parametric template bodies (Loper et al., 2015 pavlakos et al. 2019), assuming minimally clothed geometry. As seen in Fig. [e), this creates large mismatches between tem- plate surfaces and real garment volumes, causing undersampling, silhouette distortion, and instability under unseen poses. Because Gaussians are explicit point samples, such errors propagate into deformation and cannot be repaired by skinning alone. Our personalized Gaussian initialization avoids these issues by estimating a clothed canonical field via neural deformable field, producing subject-speciffi and cromeery-faithhlee Gaussian distribution. This reduces the deformation network's 's burden, enabling it to focus on true non-rigid mo- tion rather than fixing incorrect geometry. Fig. G show that PGI improves clothing detail, reduces skirt-splitting, and stabilizes secondary motion. Overall, initialization is not preprocessing step but a critical determinant of garment fidelity and temporal stability.
Training Coel
200
200
taaning time (oor)
Figure 7: Analysis of the training-cov trade- off compared to an ex- isting method.
Template-free Deformation. Even with a faithful canonical geometry, deformation remains con- strained when tied to template-based articulation such as LBS. These methods define motion as direct function of skeletal pose, which fails for loose garments that do not follow body kinematics. As visualized in Fig. [b), template-driven deformation creates motion-error spikes, flickering, and cloth splitting because it lacks temporal awareness and restricts non-rigid behavior. Our SMAD module departs from this paradigm by learning a template-free autoregressive deformation field on velocity-encoded Gaussian graph. Instead of following fixed hierarchy, Gaussians interact through learned graph messages, enabling the model to infer how cloth regions co-move or lag independently of the body. This grants expressive, pose-agnostic deformation capability and yields coherent dynamics across diverse motions. Results in Table and Fig. show that removing template constraints dramatically improves robustness, generalization, and overall clothing realism.
CONCLUSION
In this paper, introduced novel approach for dynamic appearance modeling of 3D Gaussian Splatting-basee avatars from monocular videos, focusing on foose-fitting clothing dynamics. We addressed two main challenges: limited Gaussian deformation from template articulation, and misalignment issues from Gaussian initialization relying on naked body templates. To resolve these, we proposed autoregressive Gaussian deformation strategy that predicts velocities for realistic cloth dynamics, and personalized Gaussian initialization using a deformable neural radiance field to capture clothed silhouettes. Additionally, we evaluate on an in in-the-wild dataset featuring subjects performing dynamic movements in challenging clothing. Extensive evaluations confirmed our method improves realism and outperforms existing approaches in both controlled and unconstrained settings.
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ACKNOWLEDGEMENTS
This research was supported by the Korea Creative Content Agency (KOCCA) grant funded by the Ministry of Culture, Sports and Tourism of the Republic of Korea (RS-2024-00441177)) and supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2019 11100199111179999 Artificial Intelligence Graduate School Program (Korea University), and DSS-022-000557885 AI Research Hub Project).
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Appendix
Table of Contents
Notation
18
B
Implementation Details
19
B.1
Shocitt-Encooed Gaussian Graph
19
B.2
Secondary Motion-Awer Gaussian Deformation
19
Training
Experimental Setting Details
20
C.1 Dataset Description
................................
20
Baseline Methods
................................
20
Evaluation Metries
21
D
Additional Analyses and Discussions
22
D.1
Syper-Parammter Search
22
D.2 Statistical Significance
23
D,3 Generalization
23
E More Results
24
F
Limitations
24
G Broader Impacts
24
17

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A
NOTATION
We summarize the key notations used in main paper in Table F
Table F: Summary of mathematical key notations.
Notation
Dimension / Set
Definition
V
T
IR #x3
Monocular RGB video with T frames
2
T
Number of video frames
3
Gf
Deforned Gaussians at time
N
Number of Gaussian primitives
5
R
Position of the i-th Gaussian
Eyp
R2x3
Covariance matrix (size & orientation)
7
R²
RGB color
Opacity
(0.)721
8O(3)
SMPL joint pose sequence
R(-)
[R3, o))
J Rxx
Differentiable Gaussian renderer
Canonical (undeformed) Gaussian Gaussian graph (nodes & adjacency) Number of downsampled graph nodes (M N) Node positions after voxel downsampling
(X
X Rx3 A MMX
M
14
R
15
AG
[0,1]
Edge weight (k-NN Gaussian kemel)
16
Pa
R
Sensitivity factor for Gaussian kernel distance
17
hi
(as 6, 0z)
R
Node feature: position, velocity butter, pose embed
18
Body pose prior embedding
(1)
R
Instantaneous velocity
20
N
Past-velocity buffer length
21
R R
Point mass for physies model Acceleration of node
22
ax(() 2:(1)
23
Damping coefficient Spring stiffness between nodes i, Rest offset the spring
24
R R
25
L
26
(X)
R R
External force applicd to node Simulation time-step
27 At
28 m)-:((t)
Message passed from node j to node i Learnable latent code for Gaussian i Time-averaged density for Gaussian extraction Regularization loss terms
29
30
((ir) = + ((n,
31
Ciaos Ldamp
32
Dinu Adamp
R
Hyperparameters for loss weighting
18

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B IMPLEMENTATION DETAILS
B.I. VELOCITY-ENCODED GAUSSIAN GRAPII
Voxel-grid downsampling (N + M). Given a set of N initial Gaussian points, we introduce autoregressively graph-based Gaussian deformation to transform the Gaussians without pre-defined articulation template parametric model for enhancing secondary motion dynamics. To avoid an O(N)) neighbourhood search and limit graph size for the GNN, we down-sample the Gaussian cloud on an isotropic voxel grid:
1. Grid resolution. Let dmin denote the minimum distance below which two Gaussians would overlap in the 3DGS renderer (e.g. the renderer's splat radius at canonical scale). We choose the voxel edge length
2dmin. which empirically yields =10 Gaussians per occupied voxel.
2. Hash insertion. Every Gaussian is hashed into voxel key. It retains the index with the smallest per-voxel rendering error, measured on 4x subsampled depth map; all other Gaussians that voxel are discarded.
3. Representative pooling. For the surviving indices we conduct average pooling, giving a single graph node. The total number nodes M = 40k, an order of magnitude smaller than N without noticeable quality loss.
--Nerestt-eiihhoon edge set. With the down-sampled node coordinates X is different from the one defined in PGT), we build an undirected, symmetric k-NN graph: A = (i. x Topk ||x; Xy {12))) ) We set 16, which is sufficiently dense to preserve local manifold connectivity yet keeps the message-passingg cost low.
(xxx JM (which
B.2 SECONDARY MOTION-AWARE GAUSSIAN DEFORMATION
Architecture. Given the velocity-encoded Gaussian graph r (X, SMAD converts the current node state at animation step into frame-specific Gaussian attribute deltas {Ap; AE, Ac, 1 through three conceptually simple stages. We present the architectural details of our SMAD.
Node projection. Each node consists of concatenated features h, {x,, Vi, e, where embedding vector through MI.P is obtained by driving pose sequences θ,-r:t = (0,, (0,...,,, 0.-r). The h, seeds the saassagge-pssiing stage.
Message-passing iterations. At each iteration é, we construct an edge feature vector for every directed pair (1, EA
h h X, x, v
X x; 1].
The shared edge-MLP Ms compresses e to a high-dimensional message mt)i(t). For every receiver node we perform mean aggregation over its k nearest neighbours: m R EjEN() myj- The aggregated vector is fed, together with the previous hidden state h into a GRU cell hf = Gm(m, agg hf-1). Because Fg share weights across iterations, the network learns a recurrent, physics- inspired propagation of inertia without increasing parameter count.
Decoder Up. After three iterations, we obtain the refined latent representation hy for each node. A two-stage MLP acts as shared decoder whose final activations feed four independent linear heads:
Dui, Av; AE, Ac; (h)).
Here, Aμi is 3-D position offset, Av; a 3-D velocity refinement that re-queued into the velocity ring buffer, AE; a log-diagonal covariance update, and Ac, E [0, 1] a colour residual (sigmoid- bounded). These deltas are added to the canonical attributes before the Gaussian splatting renderer is invoked for the current frame
SMAD therefore (i) embeds pose and recent motion into a compact latent space, (ii) injects neigh- bourhood cues through three message-passing steps that emulate mass spring-dammn interactions, and (ii) decodes temporally coherent, view-awan adjustments to every Gaussian's 's geometry and appearance.
19

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B.3 TRAINING
We adopted 2-stage training- Fristly, we train personalized Gaussian initialization as pre-stage, where it locates initial Gaussians densely aligned onto the person-specific silhouette. Thereafter, we train secondary motion-aware Gaussian deformation to auto-regressively transform the canonical 3D Gaussians that are aware of clothing dynamics. We used the Adam optimizer (Kingma & Ba 2013) with an initial learning rate of 0.001, decaying by a factor of 0.5 no improvement is made in four consecutive epochs. We used PyTorch (Paszke al., 2019) for the backend processing. All experiments were conducted on AMD Ryzen Threadripper PRO 5965WX CPU and an NVIDIA GeForce RTX 3090 GPU.
C EXPERIMENTAL SETTING DETAILS
C.I. DATASET DESCRIPTION
Motivation. Existing datasets for evaluating animatable 3D avatars predominanty focus on subjects wearing tight-fitting clothing and performing repetitive, often monotonous motions. Although the recently proposed 4D-Dress dataset (Wang et al., 2024b) addresses some of these limitations by including diverse garment types, it is still collected in controlled laboratory setting and primarily designed for multi-view capture evaluations. However, our ultimate goal is to democratize avatar generation-making it robust and accessible to everyday users using only monocular inputs. To this end, it is essential to evaluate performance under in in-the-wild scenarios, where diverse factors such occlusion, motion blur, and uncontrolled lighting can affect avatar quality. We introduce an evaluation dataset Juma- featuring (1) subjects wearing loose-fitting garments, (2) realistic clothing dynamics exhibiting secondary motion, and (3) videos captured in the wild. This setting enables evaluation of avatar reconstruction robustness under real-world conditions.
Dataset Statistics. Our dataset comprises five unique subjects, each recorded in total of six sequences: one static-motion sequence and five dynamic-motion sequences. For each subject, one sequence captures 360-degree rotation, while the remaining four sequences feature free-form dynamic motion, performed without scripted guidelines. All subjects wear challenging garments, such as long skirts and padded coats, designed to emphasize loose-fitting clothing dynamics. Fig. H shows qualitative results on several samples from our dataset.
Capture Setup. All sequences were captured using standard smartphone devices. We provide to 10-minute-long RGB video sequences for each subject, along with corresponding segmentation masks, depth maps, and SMPL pose parameters. Segmentation masks were obtained using SAM v2 (Ravi et al.] 2024)), with optional manual refinement to ensure silhouette accuracy. For depth information, we used smartphone depth cameras to obtain coarse estimates, which are further refined using Metric3D (Yin et al. 2023a). To extract driving pose parameters, we fit the SMPL model (Loper ct al. 2015) to each input monocular video, following the protocol described in (Moon et al. 2024).
C.2 BASELINE METHODS
Our objective is to construct animatable 3D human avatars from a single video, faithfully reflecting secondary motion, based on the 3D Gaussian Splatting representation. To validate the effectiveness of our method, we conduct a comparative analysis with existing 3DGS-bart based avatar approaches. Given the rapid expansion of research in this domain, an exhaustive comparison with all prior works is impractical. Therefore, we select a subset of publicly available methods, with particular emphasis on those that explicitly address dynamic appearance modeling.
GART (Lei et al. 2024). GART introduces the Gaussian Articulated Template (GART) model, designed to reconstruct non-rigid articulated human subjects from monocular videos. To facilitate challenging deformation, it employs a learnable forward skinning strategy via latent bones. However, due to the lack of supervision on where to place novel bones and how to assign skinning weights for each Gaussian, the method struggles to maintain structural consistency during deformation. This often leads to a breakdown in kinematic coherence and introduces excessive degrees of freedom, making stable animation difficult.
20

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Reference
Animation results of rendered 3D Gaussian avatars across time axis.
Figure Qualitative results on LoCo-Human consisting subjects wearing loose-fitting clothes with dynamic motions.
GuussianAvatar (Hu et al. 2024a). GaussianAvatar proposes an efficient method for creating realistic human avatars with dynamic 3D appearances from single video. It utilizes UV positional maps to encode pose-dependent features and integrates them with canonical surface geometry. However, its pose representation is heavily reliant parametric template priors. While effective for minimally clothed humans, this reliance limits its generalization to clothed avatars whose geometry deviates significantly from the template, especially in the case of skirts, which often exhibit unnatural splitting between the legs under dynamic motion.
3DGS-Avatar QQian et al. 2024ap. 3DGS-Avatal also presents framework for creating animatable human avatars from monocular video using 3D Gaussian primitives. introduces a non-rigid deformation network that learns per-Gaussian offsets to represent dynamic clothed avatars. However, by assigning independent degrees of freedom to each Gaussian, the method neglects the underlying structural coherence of the avatar. This leads undesired needle-like artifacts, particularly under dynamic motions.
ExAvatar (Moon et al. 2024). ExAvatar proposes a hybrid representation that combines a whole-body parametric mesh with 3D Gaussian Splatting. By binding each Gaussian to corresponding mesh vertices, the model ensures stable deformation under novel motions. Nevertheless, it exhibits weak appearance modeling for clothed subjects wearing loose-fitting garments, such as coats skirts, which significantly deviate from the shape of the minimally clothed parametric template.
In summary, existing methods largely depend shape and articulation priors from parametric template models to synthesize and animate avatars. This reliance limits their ability to model (1) loose-fitting clothed humans with geometry far from minimally clothed templates, and (2) realistic deformation that preserves geometric structure while capturing clothing dynamics. Our approach aims overcome these limitations by introducing template-fie formulation tailored for secondary motion-aware avatar modeling.
C.3 EVALUATION METRICS
We used PSNR, SSIM, LPIPS, and motion error the primary evaluation metrics.
Peak Signal-to-Noise Ratio (PSNR). PSNR is a widely used metric for evaluating the reconstruction quality of compressed or reconstructed images by comparing them the original. It quantifies the
ratio between the maximum possible pixel value and the power of the distortion (error) introduced. Given an original image I and rendered image i of animatable 3D Gaussian avatars, we first compute the Mean Squared Error (MSE). Then the PSNR defined as:
2552
PSNR = 10 logio
MSE
21

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Structural Similarity Index Measure (SSIM). SSIM is perceptual metric that quantifies image quality degradation based on changes in structural information, taking into account human visual perception. Unlike PSNR, it considers luminance, contrast, and structural similarity. Given two local image patches x and y, SSIM is defined as:
SSIM v) (2palyy + C1) + C2) (ng + m Ci)(o + o +2)
where (Jzz Myy mean intensities, (o o are variances, dzy covariance between and y- The final SSIM value is obtained by averaging local SSIM scores across the entire image.
Learned Perceptual Image Patch Similarity (LPIPS). LPIPS perceptual metric that compares images using deep features extracted from pretrained neural networks. It is designed to align closely with human perceptual judgments by evaluating similarity in learned feature space. Given two images and f, let f'()) and f' (y) denote the normalized feature maps from layer of pretrained network o, with spatial dimensions H1 x W₁ and channel dimension C₁. Then LPIPS is defined as:
H: Wi Σ -
LPIPS(I, = Σ wt H,W,
where wi are learned weights that reweight the contribution of each layer better match human perceptual similarity. We use deep features from (Simonyan & Zisserman (0144). For ZJU-Mocap, following the convention of previous studies (Qian al., 2024a)) we reported the LPIPS values scaled by 10a in the main draft to make the performance differences with the baselines more clearly distinguishable.
Motion Error: We additionally measured motion error to evaluate the temporally consistent animation and fidelity to the driving motion of the generated avatars. Specifically, this computed as the acceleration error between the driving pose (used the condition) and the corresponding pose of the rendered Gaussian avatar, which is estimated reverse using pre-trained pose estimator (Li et al. (022). To assess this, measured acceleration enror, presented in (Kanazawa et al.) 2019) the acceleration eтror metric is used. It measures the average deviation between the estimated and ground-truth joint accelerations across temporal sequence. Given sequence of 3D joint positions
[x]
joints E [R3J), [T the acceleration at time is approximated using the second-order finite difference:
x .+11 2x j
1-1 The acceleration error is then computed as:
joints
joints
joints
T-1
Motion Error
Σ T 2=
|| |
Ajoints
2
where a/.ins and afoinss denote the predicted and ground -tuth joint accelerations, respectively.
D ADDITIONAL ANALYSES AND DISCUSSIONS
D.1 HYPER- PAREME-RR SEARCH
We ablated key hyperparameters: scaling factors of loss
Adamp
Āgp
30.0
functions Aampp E (0.001-1.0) and Asoo E {0.01-1.0) in
28.0
Fig. The results show that Adamp 0.01 and A 0.1 performed best; Adamp 1.0 sharply degraded results. im-
26.0
24.0
plying excessive temporal smoothing, while isotropy gains largely saturated past 0.1. Based on the results shown in the figure. both Ciso and Ldamp help suppress exces- sive deformation and promote stable optimization, though they operate differently. Ciso preserves local isotropy, pre- venting geometric distortions, and its increase leads to gradual improvement in PSNR. In contrast, Cammp miti- gates excessive dynamic oscillations, yielding more pronounced PSNR gain within appropriate
22.0
20.0
Figure I: Hyper-parameter search on weight- ing factor of loss functions Admmp and A
22

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range. Combined, the two terms jointly enforce structural fidelity and dynamic stability, achieving balanced improvement in both visual consistency and numerical robustness.
D.2 STATISTICAL SIGNIFICANCE
We conducted two-sided paired t-test, conservatively setting p = 0.05 to relieve multi-comparioon issue. The test was based on SSIM metric scores evaluated across the test sequences of all subjects used for evaluation on the 4D-Dress. We performed (1) statistical significance analysis against comparison methods (Lei et al., 2024 Hu et al. 2024a Qian et al.) 2024b Moon et al. (024), and (2) significance testing against ablated versions of proposed main components. When compared with EDGG-basee baseline methods, all resulting p-values were lower than 0.005, demonstrating that our method achieves significantly improved performance despite the conservative threshold (see Table G). Furthermore, to assess the effectiveness of each major component proposed in this paper, we performed two-sided paired f-tests between the full model and its ablated variants. It indicated that all components were found to be statistically significant, highlighting in particular the effectiveness of our template- free Gaussian deformation module.
Method GART Let et 2004
Cousiaasivauzz Hu al. 20244 3DGS
2024a
Moon 20.4
VE
SMAD
p-valoe
66 10-1
42 10
6.0 10
STX 30
333 30
177 L0
Table G: Statistical significance (p < 0.05). We performed a two-sided paired f-test against each baseline method conservatively p 0.05 to relieve coltaicom issue. Our method exhibit statistical significance compared to the baselines and ablated ones, suggesting that our full method have significant performance improvement.
D.3 GENERALIZATION
We further evaluate the generalization performance. Fig. Fig.Dshow shows the distribution of training poses and test poses on a t-SNE plot, as well as the performance on the in-the-wild dataset. The blue box indicates the distribution of the training motion, and the orange box indicates that of the test motion. Even though the test motion was unseen during training, our method demonstrates improved generalization performance. In Fig. we plot how the perceptual error changes relative to the motion similarity between the training and testing data, measured via normalized cross-correlation (NCC) between the time-varying 3D conditional poses. We observe more pronounced increase in the error for the baseline as the testing motions deviate further from the training data. Furthermore, while it shows a large variation standard error that increases as motion similarity decreases, our method consistently maintains a low level of standard error. Since the standard error is computed between the train and test motions, lower value indicates less overfitting and suggests better generalization performance. This suggests that our method exhibits robust generalization performance on par with the linear skinning model of the conventional template parametric model.
LIT' Notiaa Sentett
Figure J: Visual check of in-distribution (blue) and out-of-distrihation (orange) driving poses with t-sne plot. (b) Average perceptual metric (LPIPS: lower is better) with standard error plot 4D-Dress over motion similarity between train and test set. Our method (red) maintains consistent rendering performance even for test motions with low similarity to the training pottionsshowing relatively less performance degradation compared to high-sin high-similarity whereas baseline (blue) exhibits significant drop in perceptual quality when handling motions with low motion similarity.
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E
MORE RESULTS
We present additional qualitative comparison results in the remainder. Please refer to the Fig. K L
M
O
F
LIMITATIONS
First, our method struggles under dynamics that involve sudden and large motion changes. Although we designed the system to account for temporal context using velocity encoding and auto-regressive modeling, it still has difficulty predicting the emergence of node accelerations that lie outside the training distribution. Second, our method does not model multi-garment interactions. The current Gaussian graph is single-layer structure that captures the overall clothed shape and ensures deformations that preserve this global structure. However, does not model interactions between garments predict their independent motions. In future work, aim to address these limitations. To tackle the first challenge, we could introduce a deformation model that considers bidirectional temporal context. Alternatively, incorporating generative flow matching technique that predicts bundle of vectors (a vector field) may offer a promising way to learn and represent the distribution of complex motions. To address the second issue, we could consider constructing a hierarchical Gaussian graph and introducing a graph neural network to model interactions between different garments. However, achieving this would require highly accurate semantic segmentation between garments. Currently, such segmentation remains difficult in the presence of diverse self ceff-occlusions and depth ambiguities from single-view video. Therefore, enabling high-quality multi multi-garment garment segmentation from single video alone would itself be highly challenging yet exciting direction for future research in hierarchical Gaussian deformation modeling.
G BROADER IMPACTS
Potential Negative Societal Impacts. Our technology could be misused, leading negative societal consequences. One major risk is Deepfake-style impersonation: a realistic avatar of a person could be created without consent and used impersonate them, enabling misinformation or fraud. The ability to replicate someone's 's likeness from a single video also raises privacy concerns, as individuals could have their image replicated and misused in unwanted ways, which can erode trust in digital media. It could also impact creative industries: unauthorized digital replicas of actors might violate intellectual property rights and undermine the entertainment industry's 's economy, and a proliferation of lifelike fake characters could confuse audiences and devalue genuine performances. These risks underscore the need for ethical guidelines and safeguards to prevent malicious use of Al-driven avatar technology.
Broader Impact. Our work offers positive implications for research, industry, and consumers. Research Community: Our method introduces new approach to animatable avatars using 3D Gaussian Splatting, advancing neural rendering, and provides an in ne-theewild dynamic clothing dataset to spur further research neural avatars and secondary motion modeling.
Industry: The improved realism and efficiency of our approach can benefit digital human applications in entertainment, gaming, and virtual reality by enabling creators to produce lifelike characters with realistic cloth dynamics from minimal input, allowing immersive real-time experiences.
Consumers: More realistic and animatable avatars mean more immersive virtual experiences for end- users. Users in VR and gaming will be able to interact through avatars that mirror their appearance and clothing motion, enhancing their sense of presence. By bridging real and virtual representations, our work enriches virtual experiences.
Ethics Statement. This work makes use of both publicly available datasets (e.g., ZJU-MoCap, 4D-Dress) and a newly collected dataset, LoCo-Human, which contains in-the-wild monocular video sequences of clothed human subjects. For all publicly available datasets, we adhere to their respective license terms and usage conditions. For LoCo-Human, all participants were recruited with explicit informed consent, covering video recording, research use, and potential public release of the anonymized dataset. No minors or vulnerable populations were included. Personally identifying metadata beyond facial and body appearance was not collected, and access to raw recordings will be restricted. The dataset will be released after peer review with research-only license
24

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prohibiting redistribution and commercial use, and with clear take-down procedures if requested by participants. We acknowledge that technologies enabling high-fidelity 3D avatar reconstruction from monocular videos may be misused for malicious purposes (e.g., impersonation, non-consensual content generation). To mitigate such risks, we emphasize responsible use of the dataset and models, encourage watermarking or detection mechanisms for synthetic outputs, and restrict the release of model weights to verified research purposes only. We also recognize the possibility of bias due to the limited diversity of clothing types, subjects, and motions in LoCo-Human, We report dataset composition and limitations transparently and encourage future work to expand demographic and cultural coverage for fairness and inclusivity. No sensitive medical or financial information used in this work. Institutional review board (IRB) approval was not required, but ethical considerations regarding informed consent, privacy, and responsible release were carefully followed.
25

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Ground Truth
3DGS-Avatar
Ours
Figure K: Qualitative Results of 00148 subjects on 4D-Dress dataset, compared to Qian al. 2024b with multiple motions time axis.
26

---

Ground Truth
ExAvatar
Ours
Figure L: Qualitative Results of 00169 subjects on 4D-Dress dataset, compared to Moon al. 2024 with multiple motions across axis.
27

---

Ground Truth
GART
Ours
Figure M: Qualitative Results of 00170 subjects 4D-Dress dataset, compared Lei al. 2024 with multiple motions time axis.
28

---

Ground Truth
ExAvatar
Ours
Figure N: Qualitative Results of 00185 subjects on 4D-Dress dataset, compared to Moon et al. 2024 with multiple motions across time axis.
29

---

Truth
GaussianAvatar
Ours
Figure O: Qualitative Results of 00187 subjects on 4D-Dress datasct, compared Hu et al. (002aa) with multiple motions across time axis.
30

---

Ground Truth
ExAvatar
Ours
Figure P: Qualitative Results of 00190 subjects on 4D-Dress dataset, compared to Moon et al. 2024 with multiple motions across time
31


Reviews:
[{"review_id": "yTalrFMhWg", "reviewer": "Reviewer_Wxo4", "summary": "This paper tackles the challenging problem of creating animatable 3D human avatars from a single monocular video, with a specific focus on realistically modeling the secondary motion of loose-fitting clothing (e.g., skirts, coats). The authors identify two primary failings of current 3D Gaussian Splatting based methods: cloth shape-agnostic initialization and temporal context-unaware deformation. To solve this, the paper proposes a two-stage framework. The first is Personalized Gaussian Initialization (PGI) that avoids naked-body templates by first training a deformable 4D NeRF on the input video. A set of canonical 3D Gaussians is then extracted from this person-specific clothed shape. The second is Secondary Motion-Aware Deformation (SMAD) where the canonical Gaussians are structured into a graph. A GNN-based deformer then learns to animate these Gaussians.\n\nThe authors also introduce a new in-the-wild dataset, LoCo-Human, featuring subjects in loose clothing captured with smartphones. Experiments on LoCo-Human and other benchmarks (4D-Dress, ZJU-MoCap) show that the proposed method outperforms recent 3DGS-avatar baselines, particularly in the quality and realism of cloth animation.", "questions": "1. The authors describe down-sampling N Gaussians to M graph nodes for the GNN. How are the deformations computed on these M nodes transferred back to the full set of N Gaussians for rendering?\n2. Could the authors clarify the \"Dynamic Texture\" aspect of the title?\n3. Section 4.3 and Table 2 state the best velocity window is T_v = 15, but Appendix D.1 says T_v = 11 yielded the highest performance. Could the authors clarify the difference in interpretation of these in selecting the final model?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The authors identify and illustrating (with Fig. 1) a key failure mode of modern animatable avatar methods, i.e., modeling varying clothing dynamics with time. This is in geenral a notoriously difficult and important problem, and the authors' analysis of why current methods fail (template mismatch and lack of temporal context ) is convincing. I would suggest adding some other relevant works [1,2,3,4] for trying to tackle the goal of modeling loose clothing with/without a template using point clouds and [5,6] that similarly use implicit representations for creating an initial representation to model clothing. [7] PhysGaussian is one of the works using physics to model dynamic Gaussians.\n2. The proposed two-stage solution directly addresses the identified problems. The PGI stage creates a high-fidelity canonical representation of the clothed individual, which is a much better starting point for deformation. Note that this is explored in various other works as well [5,6]. The SMAD module's design is the paper's main strength. Moving from a per-frame, pose-conditioned model to an autoregressive, physics-inspired one is an important shift. Using velocity encoding to give the GNN a temporal state allows it to learn complex dynamics (like inertia and damping), which is something static models cannot do.\n3. The experiments are thorough and are done on standard benchmark (ZJU-MoCap) as well as on more challenging and relevant datasets (4D-Dress, LoCo-Human) that contain subjects wearing loose clothing. The quantitative results (Table 1)  show improvement over all baselines across all three datasets. The qualitative results (Fig. 3, 4) visibly demonstrate the method's ability to avoid common artifacts like skirt-splitting and needle artifacts seen in competing work.\n4. The ablation studies (Table 2, Fig. 6) effectively isolate the contributions of the key components, showing that Velocity Encoding and the graph-based SMAD deformer are essential to the performance gains.\n5. The proposed LoCo-Human dataset is a valuable contribution. As it is captured in-the-wild with standard smartphones, it lowers the barrier to entry and will likely spur further research in this area.", "weaknesses": "1. The paper explains that N canonical Gaussians are down-sampled to M graph nodes (M << N) and that the GNN deforms these M nodes. However, it never explains how the deformation of these M nodes is propagated back to the full N Gaussians for the final rendering. Is each Gaussian rigidly attached to the nearest graph node? Is there an interpolation scheme (e.g., barycentric, LBS-like)? This is a critical, missing link in the pipeline.\n2. The title is \"Dynamic Texture Modeling...\". However, the paper's novelty and focus are on dynamic geometry and motion. The authors model the deformation of 3D Gaussians (position, covariance) autoregressively. While color is predicted by a decoder (Eq. 8), there is no discussion of modeling dynamic texture (e.g., time-varying BRDFs, wrinkle maps, or view-dependent shading effects). The dynamic appearance is a result of the dynamic geometry, but the title suggests the texture itself is being modeled dynamically, which does not appear to be the case.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sY5RB9HUse", "reviewer": "Reviewer_1tPA", "summary": "* This paper introduces a method for creating a person-specific animatable avatar from a single video.\n* Method combines a commonly used approach of attaching gaussian splats to a human LBS model, and intorduces a secondary motion modeling element on top. Namely, the gaussian location and its moments are modeled as a dynamic system, parameterized by an autoregressive graph neural network conditioned on per-frame latent codes and representations of history/neighbors states and velocity. \n* Evaluation is conducted on a set of standard benchmarks (ZJU-Mocap, 4D-Dress), as well as a newly introduced dataset, comprised of videos more suitable for evaluation on subjects with loose garments (LoCo-Human).", "questions": "1. Autoregressive formulation is known to be prone to error accumulation issues. Have authors considered evaluating their method on longer sequences\nto confirm no \"explosions\" happens due to this?\n\n2. Why different methods across different benchmarks?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "*Clarity/quality:*\n- Paper is relatively well-written and is easy to follow.\n- Method seems easy to implement.\n\n*Originality / significance:*\n- Existing approaches for avatar modeling indeed lack realistic 2ndary\nmotion modeling, and modeling it with a hybrid physically inspired approach\n(predicting physical properties with a NN) sounds like a technically sound\napproach.\n\n*Evaluation:*\n- Quantitative and qualitative comparison indicates that the method\nperforms favorably compared to the chosen baselines.\n- On the examples shown in the supp video clothing deformations indeed\nlook convincing.", "weaknesses": "*Method Limitations:*\n- Method is person-specific, which means that a new model is trained per input video, and if the information (e.g. about the back of the body) is missing, there is no way to recover it from a prior.\n- Similarly, one can be sceptical that complex physics of clothing\neformations can be learned from a single video without relying on any data-driven prior or a large dataset.\n- (Arguable) This means that the method is unlikely to generate truly realistic motions for poses outside of training distribution, and is likely\noverfitting to training sequences.\n\n\n*Novelty / Significance:*\n- The overall GS+LBS pipeline is not novel, not fully clear if the proposed dynamics formulation combined with GNN has\nbeen done before.\n\n*Experimental Evaluation:*\n- It is unclear why the methods are different across different bencharks.\n- Not fully related to the papers itself, but the quality of ZJU-Mocap\ndataset is extremely poor to the point that results on that dataset\nare not informative.\n- (Minor) For ablation study, it would be useful to understand how the A0 performs compared to baselines (is it already better?). Also, was it conducted on a single subject? If so, it is unclear if the results\nare very reliable.\n- (Minor) It is actually unclear if the method is in any way specific to a single video setup (arguably, it is not - see method limitations), and there exists a variety of datasets of much higher quality (ActorsHQ, Goliath) which could inform whether the formulation provides\nextra benefits in less noisy scenarios (and enable using more\nbaselines).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nNYEp0UqPu", "reviewer": "Reviewer_Gn6q", "summary": "The paper proposes a framework for modeling dynamic appearances of avatars from single monocular videos using 3DGS, with an emphasis on loose-fitting garments and secondary motion (e.g., skirt flutter). Instead of relying on pre-defined template-based initialization and skeletal skinning-based animation, this paper obtains dense Gaussian initialization through personalized 4D NeRFs, constructs a velocity-encoded Gaussian graph and finally learns a secondary motion-aware deformation  module that autoregressively predicts second-order dynamics. The authors also collected a new LoCo-Human dataset, containing in-the-wild videos with dynamic cloth motion. Experiments show strong improvements over state-of-the-art 3DGS-based avatar methods (e.g., 3DGS-Avatar, ExAvatar) on multiple datasets.", "questions": "Missing citation: \n\nLi et al. Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling. CVPR 2024", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The velocity-encoded Gaussian graph is a plausible design: it introduces physical intuition (mass–spring–damper) while maintaining differentiability for network learning. Such a design addresses a key gap in existing 3DGS-based avatars that only model the per-frame pose-to-deformation mapping and neglect the second-order dynamics.\n\n* The LoCo-Human dataset addresses the lack of dynamic loose clothing under monocular capture in existing benchmarks. The dataset ethics statement is detailed, with informed consent and consideration for potential misuse.\n\n* The paper is generally well-written and easy to follow. Figures are clear, with helpful comparisons and ablations.", "weaknesses": "* Constructing a node graph for modeling loose garments is not a new thing. Similar ideas have been well explored in previous methods using mesh-based representations like \"Real-time Deep Dynamic Characters\" (Habermann et al 2021). More discussions on the relationship between this paper and existing methods are necessary. \n\n* Although the paper draws analogies to second-order mass–spring systems, the GNN-based updates are learned implicitly, and no physical consistency (e.g., mass, stiffness calibration) is enforced. The “physics-inspired” term might overstate the grounding; clarification on whether parameters (k_ij, γ_i) are learned or derived would help.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aORzFypZvC", "reviewer": "Reviewer_ZfFP", "summary": "This paper presents a novel, two-stage framework for creating animatable 3D Gaussian Splatting (3DGS) avatars from a single monocular video, with a specific focus on realistically modeling the \"secondary motion\" of loose-fitting garments. The authors identify two key failings of prior work: (1) poor initialization of Gaussians due to a mismatch between \"naked body\" templates and clothed geometry, and (2) an inability to model complex cloth dynamics due to deformation models that lack temporal context.\n\nTo solve this, this paper proposes:\n1)Personalized Gaussian Initialization (PGI): A pre-processing stage that first trains a 4D NeRF to create a canonical, clothed representation of the subject, from which the initial 3D Gaussians are extracted. This ensures the Gaussians' starting positions match the actual clothed shape.\n\n2)Secondary Motion-Aware Deformation (SMAD): A novel deformation model that represents the canonical Gaussians as a graph. A GNN then autoregressively predicts the position and velocity of the graph nodes, inspired by a second-order mass-spring-damper system. By explicitly encoding a buffer of past velocities, this model captures the temporal context crucial for realistic cloth motion.\n\nThis paper validates the method against several recent 3DGS-avatar baselines on three datasets, including the new \"LoCo-Human\" in-the-wild dataset they introduce. The experiments show state-of-the-art results, with quantitative Table 1 and qualitative Fig. 3, 4  data demonstrating a clear superiority in modeling loose clothing and avoiding common artifacts like skirt-splitting.", "questions": "Why does this paper choose GNN-based? Is there any model selection ablation experiment?\n\nWhat do the physics in the ablation study represent? Are they the two physics-based losses in Equations 11 and 12?\n\nAnd what do the hierarchical body–cloth graph and contact-aware cross-edges represent?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Clear Problem Definition: The paper does an excellent job of identifying, diagnosing, and illustrating a significant weakness in current monocular avatar creation: the failure to model secondary motion for loose clothing. The analysis of initialization mismatch and temporal-unaware deformation is insightful and directly motivates the proposed solution.\n\nNovel and Sound Methodology: The PGI stage is a clever solution to the initialization problem. Using a deformable NeRF to build a subject-specific, clothed canonical space is a much more robust approach than trying to fit a generic, naked template (e.g., SMPL) to loosely-clothed subjects.\n\nThe SMAD module is the paper's core strength. Moving from a simple pose-conditioned deformation to a physics-inspired, autoregressive GNN is a significant and logical step. The \"Velocity Encoding\" (VE), which incorporates a buffer of past motions, is a direct and effective way to model the history-dependent nature of cloth dynamics.\n\nExtensive and Convincing Experiments: The experimental validation is a major strength. The method is compared against multiple strong, recent 3DGS-based methods. Evaluation spans three distinct challenges: novel view synthesis (ZJU-MoCap), novel pose synthesis (4D-Dress), and in-the-wild generalization (LoCo-Human). The method achieves state-of-the-art quantitative results across the board (Table 1). The qualitative results (Fig. 1, 3, 4) are particularly compelling, clearly showing the elimination of artifacts (like skirts splitting or \"needle\" artifacts) that plague other methods.\n\nDataset Contribution: The introduction of the LoCo-Human dataset, featuring in-the-wild videos of subjects in loose clothing, is a valuable contribution to the community, which lacks such data.", "weaknesses": "Insufficient Ablation Experiments: There is no ablation experiments and no discussion about the selection of GNN-based autoregressive deformer with other models.\n\nMissing Experiment Details: In the ablation study of Physics & Graph Design, there is no detailed explanation about the ablation content. For example, physics in A0 and Contact-aware cross-edges in A3 are ambiguous.\n\nDescriptive Ambiguity:  In the experiment part, this paper describes that the LoCo-Human features five Loose-Clothed Humans performing 5 dynamic and 1 static motions per subject. However, in the data statistics of the Appendix, this paper states that the dataset comprises 5 unique subjects, each recorded across 5 sequences.\n\nCost of PGI Stage: The PGI stage relies on training a full 4D NeRF for each subject before training the main SMAD model. Table F shows that this stage takes 12.5 hours, nearly 3x longer than the 4.5-hour SMAD training. While the fast inference (26 fps)  is excellent, the total training time (17 hours) is substantial. This high \"personalization\" cost should be more clearly discussed as a trade-off.\n\nFigure Quality Issue: Some texts in the figure are not correctly ordered, such as the text “Motion” in Fig. 1 (b). There is a redundant line on the right of Figure L.\n\nOthers: In the More Results part of the Appendix, the citation of the figure is not correct.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)