PhysDiff-VTON: Cross-Domain Physics Modeling and Trajectory Optimization for Virtual Try-On
Authors: Shibin Mei
Bingbing Ni
Venue: NeurIPS 2025 poster ()
Keywords: virtual try-on
garment deformation modeling
physics modeling
trajectory optimization
Primary area: applications
TL;DR: We present PhysDiff-VTON via physics modeling and trajectory optimization for better virtual try-on performance.

Abstract: We present PhysDiff-VTON, a diffusion-based framework for image-based virtual try-on that systematically addresses the dual challenges of garment deformation modeling and high-frequency detail preservation.  The core innovation lies in integrating physics-inspired mechanisms into the diffusion process: a pose-guided deformable warping module simulates fabric dynamics by predicting spatial offsets conditioned on human pose semantics, while wavelet-enhanced feature decomposition explicitly preserves texture fidelity through frequency-aware attention.  Further enhancing generation quality, a novel sampling strategy optimizes the denoising trajectory via least action principles, enforcing temporal coherence, spatial smoothness, and multi-scale structural consistency.  Comprehensive evaluations across multiple datasets demonstrate significant improvements in both geometric plausibility and perceptual quality compared to existing approaches.  The framework establishes a new paradigm for synthesizing photorealistic try-on images that adhere to physical constraints while maintaining intricate garment details, advancing the practical applicability of diffusion models in fashion technology.

MhysDiff-VTONT Cross-Domain Physics Modeling and Trajectory Optimization for Virtual Try-On
Shibin Mei
Bingbing Ni Shanghai Jiao Tong University nibingbingdsjtu. edu. cn
Huawei shibin waw.mmmmmm22270m....... com
Abstract
We present PhysDiff-TTON, diffusion-baln -aaoed framework for image-based virtual try-on that systematically addresses the dual challenges of garment deformation modeling and high-frequenc detail preservation. The core innovation lies in in- tegrating physics-inspire mechanisms into the diffusion process: a yose-guided deformable warping module simulates fabric dynamics by predicting spatial offsets conditioned on human pose semantics, while wavelet-enhanced feature decom- position explicitly preserves texture fidelity through frequency-aware attention. Further enhancing generation quality, a novel sampling strategy optimizes the de- noising trajectory via least action principles. enforeing temporal coherence, spatial smoothness, and multi-scale structural consistency. Comprehensive evaluations across multiple datasets demonstrate significant improvements in both geometric plausibility and perceptual quality compared to existing approaches. The frame- work establishes new paradigm for synthesizing photorealistic try-on images that adhere to physical constraints while maintaining intricate garment details, advancing the practical applicability of diffusion models fashion technology.
Introduction
31 have revolutionized image-based virtual try-on 40 23 g by enabling high-fidelity synthesis of garment-person interactions [18] 4 (VTON) [9
Recent advances in diffusion models 12
However, existing approaches still struggle reconcile two critical aspects, that is, preserving high-frequency texture details while accommodating complex fabric deformations, and enforcing physical plausibility in garment dynamics under diverse human poses. Current pipelines often rely on global affine transformations or heuristic warping 44, which inadequately model localized nonlin- ear deformations caused by pose variations
40). Meanwhile, the stochastic nature of diffusion sampling introduces unintended artifacts in fine textures, particularly under occlusion or extreme articulation
Recent arts
20 have witnessed concerted efforts to address these challenges. IDM-VTON 4
pioneers dual encoders to decouple garment semantics and structural features, achieving notable improvements in texture fidelity through cross-attention fusion of high-level semantic embeddings and low-level UNet 32 features. However, its linear blending strategy rigidly combines these features without modeling temporal fabric dynamics, causing discontinuous wrinkle transitions in articulated poses. GP-VTON 40) introduces geometric parsing resolve coarse misalignments via localized bast- warping, yet its component-leve deformation neglects microscale texture continuity across seam boundaries, exacerbating pattern discontinuities in plaid striped fabrics. Parallel wavelet-based approaches leverage frequency-domain decomposition to preserve high- high-frequency details, but their isolated spectral processing fails to synchronize with pose-dependent deformation fields, resulting in physically inconsistent texture densities under stretching [8). These innovations,
"ooresponning author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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while advancing specific aspects, collectively expose systemic limitation, that is, the absence of unified frameworks that bridge data-driven feature learning with continuum mechanics principles governing fabric deformation. Consequently, existing methods enforce artificial separations between geometric warping and texture synthesis, propagating errors that manifest as either over-smoothed details or biomechanically implausible drapery.
The dual demands of geometrically plausible deformation and texture-accurate synthesis in virtual try-on necessitate a co-design framework that bridges physics-inspired mechanics with spectral fidelity preservation [48]. Traditional warping methods suffer from irreversible
distortions when handling complex poses, as their rigid geometric transformations conflict with the Nyquist-Shaninn Shannnon sampling theorem governing textile patterss [13]. Our pose- pose-guided deformable warping addresses this by simulating urrin-dependent fabric dynamics through pose embeddings. Here, pose embeddings are employed to predict spatially variant offsets that preserve local curvature continuity, while cross-attention between pose maps and garment segments injects material adaptabil- ity, where stiffer fabrics yield smaller offsets for identical movements, emulating real-world drape physics [22]. Crucially, this physics-aware deformation creates a geometrically stable foundation for subsequent wavelet-enhanced texture preservation. Conventional UNet architectures inherently attenuate high-frequency signals through successive downsampling, exacerbating texture erosion during iterative denoising. To counteract this, we integrate Haar wavelet transforms into skip connec- tions, explicitly decoupling high-frequenn components. such as edge gradients and tecttoteettures, from low- now-freuuency shape approximations. A frequency- gated attention mechanism dynamically recalibrates subband contributions, that is, amplifying directional harmonics along deformation axes while suppressing orthogonally misaligned noise [38]. This dual-domain synergy ensures that geometric transformations adhere continuum mechanics while spectral constraints enforce texture Nyquist compliance- fundamental advance over isolated spatial or frequency- approaches.
Building upon the geometrically consistent deformations and spectrally preserved textures achieved by our cross-domain framework, the final pillar, potential-regularize path optimization (PRPO), addresses the temporal and structural coherence challenges inherent in iterative diffusion sampling. While pose-guided warping ensures fabric dynamics obey Newtonian principles and wavelet de- composition maintains Nyguist-compliant textures, the stochastic denoising trajectory may still accumulate errors across timesteps, manifesting as anatomically implausible wrinkles or discon- tinuous fabric flows during arm articulation. PRPO reinterprets this sampling process through the lens of continuum mechanics (26), formulating it as a variational problem that minimizes an action functional encompassing three physics- -issiiss- potentials, that is, temporal incoherence penalty via inter-timestep smoothness, spatial irregularities constraint via total variation, and structural inconsis- tencies 45 suppression via multiscale self-similarity. By deriving gradient corrections from these potentials, PRPO steers the diffusion path toward energetically favorable states. This completes our poysses-integraide control loop, where the first two modules establish scometric-spectre foundations, while PRPO orchestrates their synergistic evolution across the generative trajectory, achieving texture fidelity even under extreme articulation.
diffusion for virtual try-on, establishing new standards in geometric fidelity and spectral authenticity. Our contributions can be summarized as,
Our PhysDiff- VITON framework pioneers
We develop PhysDOff-VITON, framework that unifies continuum mechanics principles with diffusion dynamics through a pose-conditioned deformation module and wavelet-constrain texture synthesis, enabling simulation of fabric dynamics under complex articulations.
We propose adaptive deformation preserving curvature continuity, wavelet-constrained syn- thesis maintaining texture fidelity, and trajectory regularization through energy-minimized sampling.
Comprehensive experiments confirm the superiority of our method.
2 Cross-Domain Physics Modeling
In this section, we present the PhysDiff-VTON framework, which systematically integrates physics- inspired deformation modeling in Sec. 2.2 spectral fidelity preservation in See. (.. We start with a preliminary in See. 2. that formalizes the diffusion process for virtual try-on.

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2.1 Preliminary: Diffusion Models for Virtual Try-On
Diffusion models formulate image synthesis as an iterative denoising process governed by stochastic differential equations [12]. For virtual try-on, given source person image Ip E IR Nxx x and garment image I R x3 the goal is to generate I where I is realistically worn by the person in Ip while preserving pose and texture details. The forward diffusion process progressively corrupts the target image Xy with Gaussian noise across T timesteps.
T
((x|(x(-1) = (xx: -Ix-------,11 ((F1r//))) mo) = II [x
where 3 controls the noise schedule. The reverse process learns to iteratively denoise X7 (0,
by estimating the score function 6o (xx, I, C.) conditioned on inputs C (Tp. Ig. Co), where Co represents other extra conditions, such as text, mask, and pose keypoints. The conditional generation objective can be denoted as,
C= E= xoo. (x 1, C) -[1]]
The reverse sampling process is to gradually restore the data distribution through the trained denoising network ep-
Unlike generic image synthesis, virtual try-on requires solving a compositional inpainting problem, where replaces the original garment region in I, with I, while maintaining photometric consistency in non-target regions, biomechanical plausibility in garment deformation, and high- fighffrequency texture fidelity under perspective distortion. This necessitates specialized conditioning mechanisms. Typical implementations [18, 4 encode Ig through
garment encoder &g to produce multi-scale features (f M which are fused with person features fP via cross-attention layers and self-attention layers in the diffusion model.
The framework of our virtual try-on model consistent with IDM-VITON 4 which is based on diffusion models, and applies two separate modules to extract semantic information from garment images and encode into the base UNet. We utilizes a visual encoder to extract high-level semantic information from garment images and a parallel UNet, i.e., GarmentNet, to extract low-level features, thus preserving details. In addition, detailed text prompts are provided for both garment and person images enhance the authenticity of the generated images.
2.2 Pose-Guided Deformable Fabric Dynamics Modeling
The geometric discrepancy between canonical garment representations and dynamically posed human bodies introduces two fundamental challenges in virtual try-on that is, irreversible texture distortion caused by rigid spatial transformations, and physically implausible deformation due to neglecting material-dependes affine transformations or thin-plate splines (TPS) [IO 40 impose global smoothness constraints incompatible with localized fabric dynamics, while convolutional warping lacks explicit mechanisms to encode human kinematics. Our physies-aware deformation field addresses these limitations through pose-semantic conditioned offset prediction that emulates strain dependen displacement.
strain-stress relationships. Traditional approaches relying on
We first construct a pose embedding E, E Hx through cascaded residual blocks from the pose image. We will integrate the pose information into the garment features of different layers. Given garment features Fg E Hx H extracted via pre-trained UNet certain layer, cross-modal attention computes pixel-wise affinity between human pose and fabric regions,
W.E, (W&F (i)
aij = Softmax
VD
where W q. Wa project features to query-key space. The attention map A E HR aggregates pose-specific deformation cues, guiding the prediction strain-aware offsets,
Ap (F, (A Ep)),
Here @ denotes element-wise multiplication that emphasizes pose-relevant features, and @ is simple concatenation. The offset predictor G learns material-depeneel deformation patterns, where stiffer fabrics exhibit smaller || for equivalent joint movements, observed in real draping physics.
3

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The warped garment features F, are computed via deformable convolution:
F Fx Σ wk Fg Pk + Apk)
(5)
where pt enumerates K sampling locations in the regular grid, and wi denotes adaptive weights.
Pose-conditioned offset prediction fundamentally differs from prior geometric warping (37 in that it integrates human pose into deformation mechanics. Multi-layer attention enables hierarchical modeling, where global pose changes (e.g. arm elevation) are captured in low-resolution layers, while high-resolution branches handle local wrinkles. Compared to occlusion-agnosté warping in [49], our approach implicitly handles self-occlusions through strain-dependent displacement, where occluded regions automatically receive smaller ||ll| due to attenuated attention responses. Compared to TPPS-ased methods [10]. our data-d data-driven approach better handles non-line non-linear wrinkles when trained on diverse poses. The network automatically learns amplify offsets near bending joints while suppressing unrealistic stretching in rigid areas.
2.3 Eanhane-Enhanned Spectral Fidelity Preservation
High-frequency texture erosion poses a fundamental challenge in diffusion-based virtual try-on, as iterative downsampling operations in UNet architectures progressively attenuate directional gradients and micro-pattent (e.g., plaid alignments or embroidery stitches) [1 B3 To address this, we propose a Haar wavelet transform module embedded in skip connections, which explicitly decouples and reinforces fighffreueeccy components throughout the denoising trajectory. The key insight stems from the observation that conventional spatial attention mechanisms exhibit limited discriminative ca- pacity in preserving Nyquist-critical frequencies [15] those carrying essential perceptual information about textile microstructures.
Our implementation begins with fixed Haar wavelet decomposition applied to intermediate feature maps X E BxCx after each downsampling layer. The transform applies separable ID convolution along row and column dimensions using low-pass (W₂ = [1, 1J) and high-pass (W [1, filters. This yields four subbands,
(LL, LH, HI, HH) DWT(X),
where LL captures low-frequency approximations, while (LH, HL, HH) encode horizontal, verti- cal, and diagonal nigh-frequend details, respectively. These subbands are concatenated and processed by a frequency-gated attention mechanism,
A (Coov, (GN(Convv (Cannra(ConnaLLL LH, HH))),
where a denotes the sigmoid function, GN represents group normalization, and Conv; convo- lutional layers. The attention mask A E [0, 1| XW dynamically amplifies critical frequency components based on local texture complexity. The final enhanced features computed through residual modulation,
(8)
where O denotes element wise multiplication. This formulation ensures gradient stability while enabling explicit high- feeh-freuunccy preservation. The Haar basis proves particularly effective due to its compact support and directional sensitivity. These properties align with the anisotropic nature of garment textures under deformation.
The essence of this approach lies in transforming spatial-domain image information into the frequency domain through wavelet basis functions such Haar or Daubechies [46], decomposing garment images into low-frequency components (representing global contours and color distributions) and high-frequency components (capturing edge details and micro-textures). For instance, in a plaid shirt scenario, low-frequency components preserve the general grid arrangement pattern, while high- fegg-frequency components precisely record the sharpness of each grid edge and microscopic structures at intersections. Introducing wavelet transforms into the model equips the network with a 'computational microscope", compelling continuous attention to these vulnerable details during generation. By designing frequency dynamically amplifies influential frequency bands, enhancing weights on high-frequency subbands when processing glossy silk textures, while suppressing them in smooth regions of plain T-shirts to
attention mechanisms in the wavelet domain, the model

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prevent noise introduction. This adaptive mechanism ensures optimal detail fidelity across diverse garment categories.
Synergy between Pose-Guided Deformation and
The synergistic interaction between components further amplifies their respective advantages. The explicit preservation of high-frequency details through wavelet transforms enriches local texture infor- mation for deformable convolution, maintaining microstructural continuity during deformation example, preventing line fractures in plaid patterns when simulating skirt sway. Conversely, pose- guided warping guarantees proper spatial distribution of tighffreueecc textures. When garments stretch due to pose variations (c.g.. T-shirt print widening during arm extension), high-frequenge texture densities adapt accordingly [14] This synchronized coordination between detail preservation and deformation constitutes the cornerstone of photorealistic generation.
PRPO for Trajectory Optimization
In this section, we elaborate the trajectory optimization into unified diffusion paradigm in Sec. 3.1 and potential function design for our newly proposed sampling method in Sec. 3.2
3.1 Potential-Regularizz Path Optimization
The stochastic denoising trajectory of diffusion models, while effective in exploring the data manifold, may introduce path oscillations that violate physical priors inherent virtual try-on tasks. For instance, abrupt changes in latent states across timesteps can lead to discontinuous fabric flows or
implausible wrinkles during arm articulation. Inspired by the principle of least action (36 physics,
where dynamic systems evolve along paths minimizing action functional 25 41 We reinterpret
the diffusion sampling process as a variational optimization problem. This perspective allows for injecting physics-inspired constraints into the generative trajectory, steering it toward states that
balance data fidelity with physical plausibility.
Action Functional. Define the action fuunctional S[r(()) over the diffusion path xtt) from noise x((") clean data (0)):
Slx((1) [ [(s0()((),t) Vz log (x(())
S. Flx(())dt + a L |E(()))
Dynamic Matching
Poteotial Regularization
Stochastic Control
where sy is the learned score function, E(x) denotes the potential energy encoding physical con- straints, and {t) controls stochasticity. The first term enforces consistency with the learned data manifold, the second imposes task-specific physical priors, and the third regulates exploration- exploitation trade-offs.
Variational Optimization. Applying variational calculus to minimize S yields the modified reverse- time SDE:
dx [f t) 9(t)² so (1 (x. ()] dt vz +
ag(t)dw
(10)
Standard Reverse SDE
Potcotial Gradient
Controlled Noisc
where fxx and g(t) are drift and diffusion coefficients from the forward process. The potential gradient term >g(t)² E(xr explicitly corrects the trajectory toward low-energy states, while (g(t)) injects annealed noise to avoid local minima.
Discretized Sampling, Integrating (10) via the Malle-Maruyama scheme gives the PRPO update rule:
Br ae
Ft-1
60 (2t,
+ XB.V + a Bv Bi
(11)
V
Potential Correction
Annealed
Deterministic Update
where a,, Br are DDPM scheduling parameters, and z ~ NN I). The noise scale a decays as (1) Tmax exp( exp 1)) prioritize exploration early and refinement late.
The potential gradient V introduces second-order guidance beyond the score function's 's first-order manifold approximation, suppressing non-physical oscillations (e.g.. jagged edges in plaid patterns).
5

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Algorithm Potential-Reguulriza Diffusion Sampling (PRPO)
Require: Pretrained score model 60, potential function E(x), initial noise øt ~ (0,
1: for = do
2: Compute <x IT Ba), Be = 04/00-1
3: Predict noise E, = EB (ze,
4: Deterministic update: be vor It vI-aa E2
5: Compute potential gradient: VE
V (3(++)
6: Apply correction: 14 <- 14 + ABe F
7: Sample noise scale: d+ O max exp( -k(T 1))
8: Inject noise: It-1 μl σov Bez where 2 ~ (0,1)
9: end for
10: return Denoised sample To =0
The annealing noise schedule preserves diversity while ensuring final sample coherence, which is crucial for resolving ambiguous cases like occluded garment regions. Alg. details the PRPO sampling process. Lines 2-4 implement the standard DDPM prediction, while Lines 5-6 compute the potential gradient correction. The adaptive noise injection in Lines 7-8 ensures progressive transition from stochastic exploration to deterministic refinement. Notably, PRPO maintains compatibility with existing diffusion frameworks by simply augmenting the sampling step with physics-aware corrections.
3.2 Potential Function Design
The efficacy of PRPO critically depends on the design of potential energy functions E(x) that encode domain-specific physical priors. We derive three complementary potentials addressing temporal coherence, spatial regularity, and structural consistency based on the fundamental requirements for photorealistic virtual try-on. Each potential component derived from first principles of stochastic dynamics and image statistics, ensuring their complementary roles in virtual try-on generation.
Inter- inter-Timestep Smoothness Potential Let 2; Rd denote the latent state at timestep t in the diffusion process, with the forward Markov chain defined by:
((x]2²=1) A((zi v0r2-11 (1 a.)1)
The reverse process approximates the true posterior (==+-122)) through variational inference. To
suppress abrupt transitions between timesteps which manifest as discontinuous fabric flows we impose the smoothness potential:
Esmoot = Xe|1=z 1
where [E[=411 |=,] 2a follows the forward process expectation, and X > implements dime-decaying regularization strength. This term minimizes deviations from the theoretical diffusion trajectory, effectively damping high-frequency oscillations in the denoising path [20]. For articu- lated garments, such temporal coherence ensures wrinkle formation adheres to progressive drapery dynamics rather than erratic noise artifacts.
Total Variation Spatial Potential Natural images exhibit heavy-tailed gradient distributions predominantly smooth regions punctuated by sparse edges. To replicate these statistics in generated garments, we define:
(TT(2
Σ (ivaraii + [i,),(),)))
where V and v. denote horizontal/vertical finite differences. This total variation (TV) term imposes a piecewise smoothness prior p(2, x e -FFr(())) steering solutions toward regions separated by sharp edges 335 In virtual try-on, Erv proves critical for preserving high- frequency details like embroidery patterns and fabric seams while suppressing salt-and ann-ee noise in homogeneous areas.
Multiscale Self-Similarity Potential Leveraging the inherent hierarchy in diffusion processes, we enforce structural consistency across scales:
EMs (x)
[D.)), EID

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Table Quantitative comparisons on VITON-HD and DressCode test sets. PhysDiff-VTON VTON demon- strates superior performance in both low-level similarity and high-level semantic similarity (LPIPS, SSIM, CLIP-I) and image fidelity (FID). Several GAN-based virtual try-on methods and Diffusion- based virtual try-on methods are introduced to compare with our proposed PrysDiff-VITOO Bold denotes the best score for each metric.
Dataset
VITON-HI
DressCode
Method
LPIPS & SSIM 1 FID J CLIP-I LPIPS à SSIM 1 FID & CLIP-I1
GAN- based methods
HR-VITON
0.115
0.883
9.70
0.832
0.112 0.484
0.910
21.42
0.771
GP-VTON
0.105
0.898
6.43 0.874
0.780
55.21
0.628
Diffusion-basse methods
LaDI- VTON
0.156
0.872
8.85
0.834
0.149
0.905
16.54
0.803
DCI-VTON
0.166
0.856
8.73
0.840
0.162
0.893
17.63
0.777
VtaTOeVITON VITON
0.133
0.885
6.52
0.871
0.107
0.910
14.37
0.866
IDM-VITON
0.102
0.870
6.29
0.883
0.062
0.920
8.64
0.904
PhysDifi(Ours)
0.093
0.881
6.21
0.894
0.055
0.932
8.27
0.918
propagates coarse- scale expectations. This potential ensures localized details (e.g., sleeve pleats) remain geometrically consistent with global garment structure (27). For complex poses, it prevents anatomically implausible distortions by maintaining cross-scale correspondences in deformation fields.
Here, Dj denotes downsampling by factor s, and (2.-))zz) -a(--1D((2)
Unified Variational Perspective The composite potential E(x) Esmooth + ETV + EMs rectifies
the reverse process distribution:
T po (=0:T) x II po po(z)-122)) II -XB(s)
This Bayesian formulation injects physics-aware priors into the generative trajectory without altering the base diffusion model. The temporal term Esmooth governs fabric dynamics continuity, Erv enforces jyquist-complian textures, and EMS maintains anthropometric plausibility across scales, which collectively addressing the trilemma of garment realism.
3.3 Implementation Details
We employ the Adam optimizer with a fixed learning rate of x 10-5 for 130 training epochs, requiring approximately 95 hours 4xH800 GPUs. Our data augmentation strategy aligns with Stable-VITON [18), featuring 0.5 probability of horizontal flipping and 0.5 probability random affine transformations. During inference, we utilize the PRPO sampler with 30 denoising steps and maximum strength (n) = 1.0), initiating from random noise while disregarding masked regions in the input person image. For classifier-free guidance, inspired by IDM-VITON 44] and SpaTTex (2). we jointly condition the model using low-level garment features and high-level semantic features from IP-Adapter 43). Distinctively, we implement pose-guided feature warping through a learnable deformation module (--) that modulates garment features F, based on pose map P: F, D(F, IP)) The guidance scale w is set to 2.0. The garment features are extracted from the first 10 channels of pretrained diffusion bottleneck layer of UNet, which are subsequently injected into the target diffusion wwwelet-rransfomm-
model after pose-aware deformation. To enhance detail preservation, we integrate based frequency selection modules after each cross-attention layer in the UNet downsampling blocks, operating in the Haar wavelet domain to perform rrequency-adaptiin feature modulation. Following 4) the SDXL. inpainting model introduced as our base diffusion model, and the UNet of SDXL 28 as the garment net.
7

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MOLOAAL
Input
StableVTON
IDM-VTON
PhysDift-VTON
Imput
StableVTON
IDM-VTON
PhysDIff-VTON
Qualitative comparison. Our method can generate more natural and physically accurate distortions and wrinkles.
Figure I:
Table 2: Hyper-parameter analysis. We evaluate our method on the VITON-HD dataset under different numbers of pose-award deformation features, wavelet transformation injection position, potential regularization strength A, and maximum noise level maaz. For simplicity, we only present LPIPS and FID metrics.
(a) Feature Numbers.
(b) Wavelet inject.
(c) Reg. Strength.
(d) Noise level.
nums
LPIPS
FID
pos
LPIPS
FID
LPIPS
FID
mmas
LPIPS
FID
5
0.101
6.30
preves 0.103
6.32
5
0.099
6.29
0.8
0.100
6.31
10
0.093
6.21
preat 0.096
6.24
10
0,093
6.21
1.0
0.096
6.25
15
0.092
6.24
preout 0.093
6.21
0.104
6.32
0.093
6.21
20
0.092
6.22
20
0.109
6.38
1.4
0.104
6.36
Experiments
4.1
Experimental Setup
Datasets We conduct comprehensive evaluations on VITON-HD 3 and DressCode [24]. We train our model on the VITON-HD dataset, which contains 11,647 person-garment image pairs.
Metrics Our quantitative analysis employs four complementary measures, i.e., LPIPS [47 for perceptual similarity, SSIM [39] assessing structural preservation, FID [1]] evaluating distributional
alignment, and CLIP-I 29 quantifying semantic consistency.
Baselines We compare against two architectural paradigms following HR-VITON 19 and GP- VTON [40] representing GAN-based approaches. Diffusion-basee LaDI- 23 DCI-VTON (8]. StableVITON [18] and DOM-N [4] utilizing latent garment conditioning. All baselines are evaluated native 1024x768 resolution using official implementations.
4.2 Results and Analysis
Qualitative Evaluation Fig. Dillustrates the qualitative comparison of our method with Vta TON (18) and IDM-VITON 4. Our physically aware warping technology maintains physically correct distortion where fabrics are stacked, even when the baseline wrinkles unnaturally. The wavelet enhancement process successfully preserves details such as textures, patterns, and text that are difficult to capture with other methods. PRPO trajectory optimization prevents unnatural stretching silk materials that diffusion baselines struggle with.
Quantitative Comparison As Tab. shows, PhysDiff-VTON achieves state-of-the-an performance across all metrics. Particularly noteworthy is the L.IPS improvement over IDM-VITON on VITON- HD, demonstrating the effectiveness of our frequency-aware architecture for full-body outfits. The FID reduction VITON-HD confirms enhanced physical plausibility through continuum mechanics

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Table Ablation study. Contribution of each component evaluated by removing terms of LPIPS, SSIM, FID, and CLIP-I.
Dataset
VITON-HD
DressCode
Method
LPIPS + SSIM FID J CLIP-I LPIPS + SSIM f FID CLIP-I
w/o Deform
0.102
0.873
6.33
0.882
0.069
0.922
8.51
0.898
w/o Wavelet
0.098
0.868
6.24
0.888
0.062
0.916
8.40
0.904
w/o PRPO
0.096
0.875
6.25
0.890
0.059
0.925
8.33
0.912
PhysDiff VITON
0.093
0.881
6.21
0.894
0.055
0.932
8.27
0.918
Table 4: Efficiency of our method. PCMA represents peak CUDA memory allocated, and TOI represents the time of a batch inference (resolution: x024x768, steps=30, batch size=2).
StableVITON IDM-VITON PhysDiff- VITON
PCMA(G)
13.105
25.183
25.245
TOI(s)
29s
11s
11s
modeling. CLIP-I gains highlight the superior semantic alignment between the generated garments and the target garments.
4.3 Hyperparameter Analysis
We conduct systematic hyperparameter studies on the VITON-HD validation set to evaluate four critical design choices. All experiments use the same training protocol with batch size 32 and 200K iterations. We evaluate our method on the VITON-HD dataset under different number of pose-aware deformation features, wavelet transformation injection position, potential regularization strength A, and maximum noise level 0 man. We empirically select the values of these hyperparameters based on experimental results in Tab.
4.4 Ablation Studies and Efficiency
To validate our core innovations, we systematically disable individual components in Tab. For VITON-HD, removing pose-guided deformation (w/o Deform) causes severe FID degradation, as rigid warping fails to simulate fabric dynamics. Disabling wavelet decomposition (w/o Waveler) increases SSIM, demonstrating its critical role in preserving high-frequency textures. The absence of potential-aware sampling (w/o PRPO) increases LPIPS due temporal inconsistency in denoising trajectories. Full implementation achieves optimal balance across all metrics. We also investigate the algorithm efficiency regarding batch inference time and CUDA memory consumption, shown in Tab.
Conclusion
We introduce MhysDiffVTONN physics-integrated diffusion framework that harmonizes fabric dynamics and spectral fidelity in image-based virtual try-on. The proposed method bridges continuum mechanics with generative modeling through pose-guided deformable warping, which simulates strain- dependent garment deformations while preserving local curvature continuity. Complementing this, wavelet-constrained decomposition explicitly safeguards high-frequency textile patterns via frequency- adaptive attention, overcoming spectral erosion inherent in iterative denoising. A novel trajectory optimization strategy further enhances spatiotemporal coherence by reformulating diffusion sampling as an energy minimized variational process, ensuring anatomically consistent drapery evolution. Comprehensive experiments validate the framework's superiority in synthesizing geometrically plausible and texture-faithie faithful results under challenging articulations, establishing new theoretical connections between physical simulation and diffusion-based synthesis. This work advances virtual try-on toward practical deployment while offering blueprint for physics-awan generative models in dynamic image synthesis tasks.

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Acknowledgement. This work is supported by the Science and Technology Commission of Shanghai Municipality under research grant No. 2518|401187.
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A Theoretical Rationale for PRPO
The theoretical justification for the Potential-Regulaane Path Optimization (PRPO) can be compre- hensively analyzed through its foundational connections to stochastic optimal control, compatibility with probabilistic evolution equations, and consistency in discrete implementations. At its core, PRPO reinterprets the reverse process of the diffusion model through the lens of path integral control theory, where the action functional
S(x(()) E [1ss0(((()))) Vz logg(>(())1 XE(x) a(E((()
encodes a trade-off between score-matching matching fidelity, Nomain--seciifi regularization, and controlled stochastic exploration. Minimizing this action corresponds selecting the most probable paths under the Onsager-Machlup formalism (9) where the kinetic matching term ensures adherence to the data manifold, the potential term F(x)) imposes soft constraints like smoothness physical consistency, and the noise energy term modulates exploration sensitivity. The derived modified stochastic differential equation (SDE)
dx | g² Vx dt + agdu
maintains theoretical consistency through its Fokker-Planck equation
dp. - - (Is [ Ag P.) + 0²9²
ViP Pc:
aL
where bounded regularization strength preserves the contraction properties of the primary drift term, and controlled noise intensity satisfies Novikov's condition [7] to maintain measure equivalence between forward and reverse processes. Discretization analysis reveals that the PRPO update step
Be VI-
to( + AB &. + B B
Tt-
Et
V
ae
achieves ( approximation error through Itō- Itō-Trylor expansion [6). matching conventional diffusion model discretization accuracy while introducing non-invasive regularization. The poten- tial function Exx) operates under weak interference principles ([AVV < |[se|]) and manifold preservation constraints, ensuring adjustments remain proximal to the support of the data distribution. Dynamic noise annealing via (t) O implements simulated annealing annealing-inspired explo- ration, probabilistically converging to global minima while enabling early-stage diversity exploration. Crucially, PRPO's inference-tie timeeneee-ieeeeeeee adaptation paradigm preserves pretrained score networks, unlike energy-based fine-tuning methods, achieving task-specific regularization through deterministic path optimization rather than retraining. This synthesis of variational action minimization with con- trolled stochastic dynamics provides rigorous mathematical grounding while maintaining practical flexibility across domains.
B From Action Functional to Reverse-time SDE
The derivation of the modified reverse-time dynamics equation from the action functional can be systematically explained through variational optimization within the stochastic calculus framework. Starting with the action functional
s[x(t)|= - (x))( - V ( + +A f E(x)dt+ + +o
we parameterize the reverse process dynamics by a stochastic differential equation (SDE) dr a(x, d) +bx, 1)dww, where the drift term a(x, I) and diffusion term b(x, 1) are optimized to minimize S[=(()) : The first term in S, enforcing score matching, directly recovers the conventional reverse drift a(x, 1) = f(x, s(1) so (x, 1) through the equivalence between score matching loss minimization and drift correction.
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Variational analysis of the potential regularization term À E introduces an additional gradient correction: perturbing the path xtt) x(t) dx(() yields variation
5 ( ((w)de)) = = [zF(()) 8x dxd
which corresponds to augmenting the drift with V scaled by the noise coefficient g(t)² from the original diffusion process. Simultancously, the stochastic control term [[E(t) regulates noise energy via optimal control theory, leading diffusion term adjustment b(x, t) ag(t) that preserves the Wiener process structure while modulating exploration intensity.
Combining these contributions, the optimized SDE becomes,
dx: (s(,,)) -(0) (0(, + >g(()²v, di og(()dw.
The compatibility this modified dynamics with the target distribution go is verified through its Fokker-Planck equation 30
Op. - (ff- g2 V pr) 2
o²g² v Pt.
01
where the original reverse process recovered when = 0 and o = 1. Theoretical consistency requires bounded regularization strength À to avoid destabilizing the primary drift term and adherence to Girsanov's theorem 77] for noise intensity (t)... This derivation rigorously unifies score matching, potential-guided regularization, and controlled stochasticity within single variational framework, establishing the mathematical foundation for the path optimization mechanism of PRPO.
C From Reverse-time SDE to PRPO Sampling
In variance-preserving forward diffusion governed by the SDE
dz - ((t) x dt + B(t du,
we discretize with At = 1 according to the DDPM [12] parametrization 3r = B()), a; = 1 B,, and ā = II ag. The corresponding reverse-time time SDE takes the form
dr = | B(txz {()) (z, t)] dt + B(t) dw,
where so t) co, /) I āي. Introducing an energy-gradee correction and controllable noise term yields the modified dynamics
dx (()) - (z,)) dt + VzE()d) + a B(t) dū
standard drift
energy correction
controllable noise
Applying the Euler Maruyama scheme 21 with At to each term gives for the step - 1 the updates
-t - -1 - B,so(zr,t)
2 x2
+
V1-a, c0(2 =
(Ir VI-a, co (2((t))))
A3 V. E(x,,
and
1B 2~ N(,11..
Thus, one arrives the PRPO sampling rule
1 2
co( - -
Be
+ X3, V.E(Tt) + / B.
Tt-1
Matching coefficients confirms that the discrete energy-gradient term X3, V E and noise coefficient /B, exactly reflect their continuous-time origins, while the discretization error remains O ensuring numerical stability provided X V FEl remains small relative to the deterministic update and o / 3, decays appropriately.
21

---

D Numerical Stability
In order to ensure numerical stability in the PRPO discrete update
1
Be at + A Bu)( + o(I) V
(
Vaz
we require two conditions. First, the energy-correction term must remain bounded, which entails
Bt (1 āx)
A [v <
co(
Dividing both sides by Be (with Be 0) gives
||0(
A[V))(=)))
Vau(1 - σz)
Under the common score-matching assumption ||co(22, t)ll vI ar this further simplifies to
Va:
so that when Ot, - 0 one must choose À sufficiently small or design E(x) so that [[v] decays naturally.
Second, the noise amplitude must be controllable by designing (t) to decay over we set
(t)
mmax kt
then the variance the noise term o BB,
Var (ar
o max
2kt
8g
and requiring lim o m 2kt Br guarantees that as + 0 (late in generation) the stochastic perturbation vanishes. For example, if Br (8max/ (oaax//T) (a linear schedule), then one enforces
2kt Bmax t
SE
T
by tuning l and Tmax- Together, these two requirements,
A B(Vx(( <
and o(1) Tmaxe
ensure that the PRPO algorithm remains numerically stable by balancing the deterministic score- matching update against energy-based correction and diminishing noise.
22


Reviews:
[{"review_id": "GCwoFVc67K", "reviewer": "Reviewer_ot98", "summary": "The paper introduces PhysDiff-VTON, a novel framework for virtual try-on that integrates physics-inspired mechanisms into diffusion models to address the challenges of realistic garment deformation and high-frequency texture preservation. The key contributions of this work includes:\n1. a pose-guided deformable warping module that simulates fabric dynamics by predicting spatial offsets conditioned on human pose semantics.\n2. a wavelet-based feature decomposition method which used to address the issue of texture erosion during the diffusion process\n3. Potential-Regularized Path Optimization (PRPO), a novel sampling strategy that optimizes the denoising trajectory by enforcing temporal coherence, spatial smoothness, and multi-scale structural consistency.", "questions": "1. It would be even more helpful for readers if there were structural diagrams.\n2. Adding more visual comparisons, especially for challenging cases (e.g., complex poses, intricate textures), would provide a clearer picture of the method's strengths and limitations.\n3. Adding a section that explores potential applications in other domains (e.g., animation, video editing) would highlight the broader impact of the work.", "limitations": "1. The authors should include a dedicated section on limitations, where they discuss the current scope and boundaries of their work.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths\n1. The paper presents a well-thought-out integration of physics-inspired mechanisms into diffusion models. The proposed methods, such as the pose-guided deformable warping module and wavelet-enhanced texture preservation, are technically sound and well-supported by mathematical formulations.\n2. The integration of physics-inspired mechanisms into diffusion models could have broader implications beyond virtual try-on, such as in other areas of computer vision and graphics where realistic simulations are required.\n\nWeaknesses\n1. Some sections, particularly those involving the mathematical formulations of the diffusion process and the potential functions, may be challenging for readers without a strong background in the field. More intuitive explanations or visual aids could help make these sections more accessible.\n2. The paper presents a limited number of visual examples of the virtual try-on results, which makes it difficult to fully assess the comparative advantages of the proposed method over other approaches across a broader range of cases.", "was_revised": true, "final_justification": "positive to this paper", "rebuttal": "Dear Reviewer ot98,\n\nThank you for your thoughtful feedback and constructive critique of our paper PhysDiff-VTON. We appreciate your recognition of the technical soundness and broader implications of our work. Below, we address your suggestions point by point.\n\n**1. Enhanced Accessibility of Mathematical Formulations​.**\nWe acknowledge that Sections 2.2–3.2 involve intricate formulations (e.g., Eq. 9–11 for PRPO). To improve clarity, we will add intuitive descriptions of the physics-inspired potentials (temporal coherence, spatial smoothness, multiscale consistency) in the revised version.\nWe will also include a schematic diagram illustrating the interplay between pose-guided warping, wavelet decomposition, and PRPO. \n\n**2. ​Expanded Visual Comparisons​.**\nWe agree that additional qualitative results are crucial. In the revised version, we will add new visual comparisons showcasing ​challenging cases​ (e.g., extreme poses, intricate textures like plaids or embroidery).\nWe will explicitly highlight improvements in texture preservation and deformation realism versus baselines. The existing Figure 1 demonstrates natural distortions, but we will augment it with more diverse examples.\nWe apologize again for the confusion caused by the lack of visualizations and limited space.\n\n**3. ​Broader Applications Section​.**\nWe will add a dedicated subsection discussing extensions to the above applications. (1) Animation/VFX: Simulating cloth dynamics via PRPO trajectory optimization. (2) Video Editing: Frame-consistent virtual try-on via temporal coherence potentials. (3) Digital Fashion: High-fidelity texture synthesis for AR/VR applications.\n\n**4. Limitations.​**\nWe will add a new section that states limitations, including scenes that our method still has difficulty handling, scenes with exacerbated deformation errors, and challenging text fidelity. \n\nThank you again for your valuable feedback and constructive suggestions."}, {"review_id": "5OD7wW6wK3", "reviewer": "Reviewer_yzPX", "summary": "The authors propose to integrate physics into diffusion-based methods for image-based virtual try-on, to improve the geometric fidelity and wrinkle realism without sacrificing texture detail. The authors introduce a pose-conditioned warping module to simulate fabric dynamics, introducing wavelet decomposition and frequency-aware attention, and introducing several regularizations into the diffusion sampling strategy (temporal consistency, total variation constraint for spatial consistency, and multi-scale self-similarity). The authors outperform several baselines on VITON-HD and DressCode test sets, both qualitatively and quantitatively, with more natural and physically accurate cloth wrinkles than prior art.", "questions": "- How well does the model perform on in-the-wild images, similar to the qualitative examples shown in IDM-VTON's Github page (https://github.com/yisol/IDM-VTON), where the proposed physics integration (e.g. pose-conditioned fabric warping) would be most necessary?\n- Not sure whether it is possible to ablate and visualize the impact of each of the individual regularizations (temporal consistency, total variation constraint for spatial consistency, and multi-scale self-similarity) within PRPO? To visualize, could show qualitative examples of final-timestep predictions throughout the denoising process, with each of the regularizations removed. PRPO seems to have overall the least impact according to Tab. 3, and it would be informative to understand whether all three regularizations are necessary.", "limitations": "The authors do not discuss limitations of the work in the paper.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The proposed pose-conditioned warping successfully computes warped garment features conditioned on a pose embedding extracted from the input image. The wavelet decomposition enhances high-frequency textures (as seen in Fig. 1 bottom left).\n- The results indicate more physically accurate wrinkles and cloth deformation than prior art.\n- The core innovations are systematically evaluated in an ablation study\n\nWeaknesses:\n- Similar to baselines, the method is unable to faithfully copy text from the input clothing image onto the subject (as seen in Fig. 1 top right).\n- The results in Fig. 1 are shown under rather constrained settings: all participants are in a well-lit studio environment in a standing pose. In contrast, the project page of IDM-VTON shows high-quality results on 16 diverse in-the-wild examples. Comparison to IDM-VTON and other baselines on those 16 examples would be beneficial to understand the performance in challenging in-the-wild scenarios, where physics integration would be most beneficial, such as diverse backgrounds, varying lighting, and challenging poses (e.g. hands on hips, eating with chopsticks while seated, etc).", "was_revised": true, "final_justification": "Thanks to the authors for their rebuttal, I have maintained the original rating. Please include the evaluation on in-the-wild scenarios and ablation about impact of individual regularizations in the final version", "rebuttal": "Dear Reviewer yzPX,\n\nThank you for your rigorous assessment of PhysDiff-VTON. We address your concerns below.\n\n**1. Text Preservation.**\nWhile text replication remains challenging for all VTON methods, our wavelet decomposition (Sec. 2.3) explicitly mitigates high-frequency erosion. As shown in Figure 1 (top-right), our method preserves text structures more faithfully than baselines, though not perfectly, by amplifying high-frequency subbands via frequency-gated attention. \n\n**2. ​Wild Scenario Evaluation​.**\nWe concur that diverse real-world testing is essential. We acknowledge your concern about the limited studio settings in Figure 1 and assure you that our framework is also robust in wild environments, which stems from our physics-inspired architecture. We will add the ​16 wild examples​ from IDM-VTON in supplementary material and provide qualitative comparisons for challenging poses (sitting, arm-crossing, asymmetric postures), complex lighting (backlit scenes and deep shadows), and cluttered backgrounds to demonstrate occlusion robustness. \nTo empirically validate this, we rigorously evaluate PhysDiff-VTON on the In-the-Wild dataset under non-customized settings. As shown below, our method achieves superior metrics, demonstrating tangible advantages in handling real-world complexity.\n|Method               | LPIPS    |SSIM    |CLIP-I|\n|---|---|---|---|\n|Stable-VITON     |0.260     |0.736     |0.836|\n|IDM–VTON         |0.164    |0.795     |0.901|\n|PhysDiff-VTON   |0.151    |0.810    |0.915|\n\nYour feedback is crucial, and we commit to enriching the paper with these evaluations to underscore the practical applicability of PhysDiff-VTON.\n\n**3. PRPO Regularization Analysis​.**\nWe will add in the appendix to visualize the impact of each PRPO potential. Temporal coherence removal will cause discontinuous fabric flow during denoising. TV spatial loss ablation will introduce salt-and-pepper noise in homogeneous regions. Multiscale loss disablement will break sleeve-pleat consistency with the global garment structure. The marginal gains occur because the core value of PRPO lies in ​trajectory stability​ (Sec. 3.1). The three regularizers act synergistically. The minimal quantitative drop reflects their role in error prevention rather than metric optimization.\n\n**4. Limitations.​**\nWe will add a new section that states limitations,  including scenes that our method still has difficulty handling, scenes with exacerbated deformation errors, and challenging text fidelity.\nFuture work will explore distillation and occlusion-invariant encoders."}, {"review_id": "5ua9xiHE56", "reviewer": "Reviewer_nqab", "summary": "Authors present PhysDiff-VTON, a novel diffusion based framework  for virtual try-on that aims to solve the dual problems of realistically deforming garments realistically and preserving their high-frequency texture details. In order to achieve this, authors have created a method that connects physics-inspired mechanics with spectral fidelity preservation. \n\nThe framework has three main components:\n\n1) Pose-Guided Deformable Fabric Dynamics Modeling:  \nThe geometric discrepancy between canonical garment representations and dynamically posed poses two problems: \n- Irreversible Texture Distortion: When the flat clothing image is rigidly stretched or moved to fit the body, its texture can be permanently distorted.\n- Physically Implausible Deformation: The garment's movement appears unrealistic because the simulation fails to consider the specific stress-and-strain properties of the fabric material.\n\nTo address these problems present in traditional methods, the proposed physics-aware deformation field predicts garment movement by emulating strain-dependent displacement based on the person's pose. This method simulates the dynamics of fabric by using pose embeddings to predict spatial offsets, which preserves the natural curvature of the clothing. To further enhance realism, a cross-attention mechanism between the pose maps and garment segments introduces material adaptability. This allows the model to emulate real-world drape physics, where stiffer fabrics deform less than softer ones during the same movement.\n\n2) Wavelet-Enhanced Texture Preservation.\n\nThe loss of fine texture details is a major challenge in diffusion-based virtual try-on. This \"texture erosion\" happens because the iterative downsampling process within the model's UNet architecture progressively weakens the micro-patterns and directional gradients that define a fabric's look. To solve this, the paper proposes a Haar wavelet transform module embedded within the model's skip connections. This module works by explicitly separating and reinforcing these high-frequency components throughout the image generation process. This approach is based on the insight that traditional attention mechanisms are not effective at preserving the crucial frequencies that contain the most important perceptual information about a textile's microstructure..\n\n3) Potential-Regularized Path Optimization (PRPO) \n\nThis final stage addresses errors that can build up during the diffusion process, which might otherwise create unnatural wrinkles or jerky fabric movements. PRPO treats the image generation as a physics problem, guiding the process to find the most plausible and \"energetically favorable\" result. This is achieved by handling the diffusion sampling process as a variational optimization problem. Thus, allowing the injection of physics-inspired constraints into the generative trajectory, conditioning it toward states that balance data reconstruction with physical realistic results.\n\nTogether, these three physics-inspired modules create a control loop that ensures the generated images are both geometrically accurate and rich in texture, even in complex poses.", "questions": "Check Strengths And Weaknesses", "limitations": "No", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe work directly addresses weakness of previous methods: \n- preserving high-frequency texture details while accommodating complex fabric deformations\n- enforcing physical plausibility in garment dynamics under diverse human poses.\n\n- Authors present an unified frameworks that bridge data-driven feature learning with continuum mechanics principles governing fabric deformation. There are strong novel contributions: \n\n  - Pose-Guided Deformable Fabric Dynamics Modeling\n  - Wavelet-Enhanced Spectral Fidelity Preservation\n   Clean introduction of high frequency preservation in skip connections.\n  - Potential-Regularized Path Optimization for trajectory optimization:\n    Mathematical derivation and explanation of the proposed approach is clear and well motivated. \n\n- Above state of the art results in baseline datasets\n- Ablation study is clear and highlights the importance of each contribution\n- Paper is well written and structured\n\nWeakness:\n- Missing diagram figure to clarify network architectures and proposed approach\n- Missing some qualitative(a zoom in to high frequency areas) and quantitative example to support some preservation of high frequency: could we have some measurement of this, i.e analysis of the spectrum on the area of the garment?\n - More insight in the computational cost of each one of the proposed novelties(inference time analysis or some theoretical insight).\n- Out of distribution performance:  While authors train and test in VITON-HD and DressCode, it would be interesting to see the generalization capacity of the proposed approach with some results in other datasets or images on the wild.", "was_revised": true, "final_justification": "After reviewing all the responses to authors I keep my rating of Accept.", "rebuttal": "Dear Reviewer nqab,\n\nWe sincerely appreciate your insightful feedback on PhysDiff-VTON. Your comments are crucial for enhancing the rigor and clarity of our paper. We acknowledge the limitations you highlighted and will address them comprehensively in the revised version. \n\n**1. ​Methodology Visualization.**\nWe apologize for this omission due to space constraints in the main text. To rectify this, we will include a detailed schematic in the supplementary material illustrating the integrated cross-domain physics modeling.\n\n**2. ​High-Frequency Preservation Evidence.**\nFor high-frequency preservation, we recognize the need for stronger empirical validation. (1) In the revised version, we will augment Figure 1 with zoomed-in panels explicitly showcasing micro-textures like embroidery stitches and plaid alignments under deformation, demonstrating superior detail retention compared to baselines. (2) Additionally, we will introduce a quantitative metric, Power Spectral Density (PSD) ratio, computed from the wavelet decomposition (Eq. 6)  of the garment region. Our preliminary analysis reveals a 12% improvement in PSD ratio over IDM-VTON on DressCode, highlighting the effectiveness of wavelet constraints.\n\n**3. ​Computational Cost.**\nCrucially, the added components in PhysDiff-VTON, such as pose-conditioned cross-attention (Eq. 3-4), fixed-cost discrete wavelet transforms (Eq. 6), and gradient computation for the tri-potential regularization (Eq. 11), introduce ​only marginal computational overhead​ relative to IDM-VITON. This efficiency stems from three key design principles. (1) Linear-scaling attention mechanisms​ in pose-guided warping leverage sparse pose semantics. (2) Non-learnable Haar wavelets​ execute in fixed time. (3) ​Parallelizable potential gradients​ in PRPO sampling add negligible latency per denoising step.\n\n**4. ​Out-of-Distribution Generalization.**\nWe supplement the VITON-HD and DressCode evaluations with the In-the-Wild dataset under non-customized settings. As shown below, our method achieves superior metrics, demonstrating tangible advantages in handling real-world complexity.\n|Method               | LPIPS    |SSIM    |CLIP-I|\n|---|---|---|---|\n|Stable-VITON     |0.260     |0.736     |0.836|\n|IDM–VTON         |0.164    |0.795     |0.901|\n|PhysDiff-VTON   |0.151    |0.810    |0.915|\n\nWe are committed to these revisions and thank you for guiding us toward a more comprehensive and transparent contribution."}, {"review_id": "5gDiT3ICKt", "reviewer": "Reviewer_qDiA", "summary": "The authors propose a diffusion-based method for virtual try-on. To enhance the realism of the generated images, they introduce a strategy that incorporates pose guidance, wavelet-enhanced features, and sampling guided by multiple constraints. Experimental results demonstrate that the method produces higher-quality outputs compared to state-of-the-art approaches.", "questions": "Please address the questions mentioned above for further evaluation.", "limitations": "The limitation of this paper is not discussed. More qualitative results and failure cases would help for the evaluation.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The contribution of this paper is clear. It addresses two key challenges: preserving garment details during warping and effectively controlling the diffusion process. To tackle the first issue, the authors design a pose-guided deformation model combined with a wavelet-enhanced attention module. For the second, they introduce a novel sampling strategy based on potential-regularized path optimization, incorporating temporal and spatial smoothness terms as well as multiscale similarity for additional regularization. The effectiveness of these components is demonstrated through comprehensive ablation studies and comparisons with existing methods. Overall, the paper is well-structured and easy to follow.\n\nDespite these strengths, several points need further clarification:\n- Section 2.2 is somewhat confusing. In Equation 5, what does $x$ ​represent? What is its relationship to $p_k$? Are the $p_k$ sampled around $x$? Additionally, the relationship between $F_g$ and $\\tilde{F}_g$ is unclear. Including an illustrative figure would significantly help comprehension. This ambiguity also makes it difficult to understand the discussion on occluded regions in Lines 139–140.\n- In Lines131-132, the authors claim that `the offset predictor $G$ learns material-dependent deformation patterns`. However, it is not clear how the model infers material properties from the input garment image. Estimating physical material characteristics from appearance alone is non-trivial, especially without additional input. This claim should be either clarified or supported with empirical evidence.\n-  In Section 3.2, the formulations for $E_{smooth}$ and $E_{MS}$ use different terms $z_{t+1}$ and $z_{t-1}$. Shouldn’t these be the same?\n- Limited qualitative results. The qualitative results presented are too few to fully support the results. More examples should be provided, particularly for the ablation study, to better illustrate the contribution of each component in an intuitive manner.", "was_revised": true, "final_justification": "Considering the contributions and the rebuttal from the authors, I keep my rating.", "rebuttal": "Dear Reviewer qDiA,\n\nWe sincerely appreciate your meticulous review of PhysDiff-VTON, which has highlighted critical areas needing clarification. We apologize for ambiguities in mathematical formulations and methodological descriptions. Below, we address each concern with substantive revisions.\n\n**1. ​Clarification of Eq. 5 (Deformable Convolution)​​.**\nWe regret the insufficient explanation of variables in Eq. 5. Here, $x$ denotes the spatial coordinates in the feature map $F_g$, while $p_k$ enumerates $K$ sampling locations in a regular grid centered at $x$. The predicted offset $\\Delta p_k$ (from Eq. 4) spatially adjusts each $p_k$ to $p_k + \\Delta p_k$, enabling adaptive sampling. Thus, $\\tilde{F}_g(x)$ aggregates features from these adjusted positions, modeling non-rigid deformations. Crucially, $F_g$ and $\\tilde{F}_g$ represent the original and warped garment features, respectively. \n\nTo resolve ambiguity, we will add a figure to illustrate the sampling mechanism in the revised version and explicitly annotate how occluded regions (lines 139–140) receive attenuated offsets $\\|\\Delta p_k\\|$ due to low attention responses $\\alpha_{ij}$ in Eq. 3.  \n\n**2. Material Property Inference (Lines 131–132).**\nWe clarify that material properties (e.g., stiffness) are implicitly learned from garment appearance via pose-garment cross-attention (Eq. 3). The offset predictor $\\mathcal{G}$ (Eq. 4) associates visual patterns (e.g., denim textures mean high rigidity) with deformation magnitudes $ \\|\\Delta p\\|$ without explicit material labels. For example, average $ \\|\\Delta p\\| $ for rigid fabrics (e.g., leather) is smaller compared to elastic ones (e.g., cotton) under identical pose changes, which will result in implicit supervision.\n\n**3. Temporal Indices in $ E_{\\text{smooth}} $ and $ E_{\\text{MS}} $ (Sec. 3.2).**\nThe indices differ intentionally due to distinct physical roles.  $E_{\\text{smooth}}$ enforces forward temporal coherence by minimizing $ \\|z_t - \\mathbb{E}[z_{t+1}|z_t]\\| $, ensuring smooth transitions to the next state.  While $ E_{\\text{MS}}$ ensures backward structural consistency by aligning $\\mathcal{D}_s(z_t)$ with the previous state’s expectation $\\mathbb{E}[\\mathcal{D}_s(z_{t-1})|z_t]$, preserving multiscale garment geometry.  \n\nThis design mirrors continuum mechanics principles, where $ E_{\\text{smooth}}$ governs fabric dynamics (future state), while $ E_{\\text{MS}}$ maintains static constraints (past state). We will add a footnote explaining this duality in the revised version.  \n\n**4. Expanded Qualitative Analysis.**\nWe will add more visual results in the revised version. We apologize again for the confusion caused to you by our negligence and limited paper.\n\n**5. Limitations​.**\nWe will add a new section that states limitations,  including scenes that our method still has difficulty handling, scenes with exacerbated deformation errors, and challenging text fidelity. Future work will explore distillation and occlusion-invariant encoders.\n\nThank you again for your valuable suggestions."}]

Author rebuttal:

Meta-review: The submission received 4 reviews and was thoroughly discussed.
The Reviewers appreciated the additional details from the rebuttal and the authors' responses.

After discussion, we reached the consensus to recommend an Accept. Congratulations!

As mentioned in the reviews, please include the evaluation on in-the-wild scenarios and ablation about impact of individual regularizations in the final version, as well as all suggestions.
Decision: Accept (poster)