D-Garment: Physically Grounded Latent Diffusion for Dynamic Garment Deformations
Authors: Antoine Dumoulin
Adnane Boukhayma
Laurence Boissieux
Bharath Bhushan Damodaran
Pierre Hellier
Stefanie Wuhrer
Venue: Accepted by TMLR ()

Abstract: We present a method to dynamically deform 3D garments, in the form of a 3D polygon mesh, based on body shape, motion, and physical cloth material properties. Considering physical cloth properties allows to learn a physically grounded model, with the advantage of being more accurate in terms of physically inspired metrics such as strain or curvature.
Existing work studies pose-dependent garment modeling to generate garment deformations from example data, and possibly data-driven dynamic cloth simulation to generate realistic garments in motion. We propose *D-Garment*, a learning-based approach trained on new data generated with a physics-based simulator. Compared to prior work, our 3D generative model learns garment deformations conditioned by physical material properties, which allows to model loose cloth geometry, especially for large deformations and dynamic wrinkles driven by body motion. Furthermore, the model can be efficiently fitted to observations captured using vision sensors such as 3D point clouds. We leverage the capability of diffusion models to learn flexible and powerful generative priors by modeling the 3D garment in a 2D parameter space independently from the mesh resolution. This representation allows to learn a template-specific latent diffusion model. This allows to condition global and local geometry with body and cloth material information.
We quantitatively and qualitatively evaluate *D-Garment* on both simulations and data captured with a multi-view acquisition platform. Compared to recent baselines, our method is more realistic and accurate in terms of shape similarity and physical validity metrics. Code and data are available for research purposes at https://dumoulina.github.io/d-garment/

Published in Transactions on Machine Learning Research (04/2026)
D-Garment: Physically Grounded Latent Diffusion for Dynamic Garment Deformations
Antoine Dumoulin
intoine.duumouknniinni....fff
Inria Centre at the University Grenoble Alpes
Adnane Boukhayma
adhannebouukaayna@@hrri..
Inria, University of Rennes, CNRS, IRISA-UMR 6074
Laurence Boissieux
leurence.hooaaiieunn@iinio
Inria Centre the University Grenoble Alpes
Bharath Bhushan Damodaran
bhorath. www.dddddaamiinitttiiiiio.ooooo.
InterDigital Inc.
Pierre Hellier
piervehheller@inniii.f
Inria, University of Rennes, CNRS, IRISA-UMR 6074
Stefanie Wuhrer
stefanie, wwhrer@inria.ff
Inria Centre the University Grenable Alpes
Reviewed on OpenReview: https: openrevie.. net/ per/formm? id- ArPyyooaa
Abstract
We present method to dynamically deform 3D garments, the form of 3D polygon mesh, based on body shape, motion, and physical cloth material properties. Considering physical cloth properties allows learn a physically grounded model, with the advantage of being more accurate in terms of physically inspired metrics such strain curvature. Existing work studies pose-dependent garment modeling lo generale garment deformations from example data, and possibly data-driven dynamic cloth simulation to generate realistic garments in motion. We propose D-Garment, a learning- based approach trained on new data generated with a physics-based simulator. Compared to prior work, our 3D generative model learns garment. deformations conditioned by physical material properties, which al- lows to model loose cloth geometry, especially for large deformations and dynamic wrinkles driven by body motion. Furthermore, the model can be efficiently fitted to observations captured using vision sensors such 3D point clouds. We leverage the capability of diffu- sion models to learn flexible and powerful generative priors by modeling the 3D garment in a 2D parameter space independently from the mesh resolution. This representation allows to learn a template-specific latent diffusion model. This allows to condition global and local geometry with body and cloth material information. We quantitatively and qualitatively evaluate D-Garment on both simulations and data captured with a multi-view acquisition platform. Compared recent. baselines, our method is more realistic and accurate in terms of shape similarity and physical validity metries. Code and data are available for research purposes at https: // //mmmummlll///////////// ina, thub. bo/d-garment//
1 Introduction
Dressing avatars with dynamic garments a long-standing challenge in computer graphics and learning- based modeling. Garments are used virtual applications, ranging from entertainment industries such as

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video games and animation, to fashion with cloth- ing design and virtual try-on. In applications in- volving an avatar such as telepresence and virtual change rooms, an important use case is accurately fitting a dynamic garment model observations captured using vision sensors such as 2D vidcos, or dynamic 3D point clouds.
Sequence
We consider the problem of deforming 3D gar- ment based on physical inputs, via learning con- ditional generative model of 3D geometry defor- mation. Given a garment template mesh, phys- ical parameters of the cloth material, and the wearer's body shape, pose and proceding motion, our method models the distribution over dynamic garment geometries p(garment deformation body motion, shape, matcrial), and outputs tempo- rally consistent 3D deformations.
Sequence
Existing work can categorized into two main classes. Pose-dependent garment models (Vidau- rre et al., 2024; De Luigi et al., 2023; Zheng et al., 2024; Shen et al., 2020; Corona et al., 2021) out- put garment draped over a body given char- acterization of the cloth, body shape, and a pose. These methods have been successfully tested on downstream tasks such as garment reconstruc- tion from observations and garment relargeting to different users. However, the problem is under- constrained the 3D geometry of the same gar- ment the same body performing the same pose the velocity and acceler-
(
Figure For two different motions (lines) and three varying cloth materials (colors), D-Garment uses la- tent diffuusonn model generate dynamic garment defor- mations from physical inputs defined by a cloth material and the underlying body shape and motion. Our model can represent large deformations and fine wrinkles.
can vary depending ation of the body parts. Existing work outputs a motion-independent solution, which prevents these methods from generating temporal garment details caused by different dynamics.
In contrast, dynamic cloth simulation methods (Bertiche al., 2022; Tiwari et al., 2023; Santesteban al., 2022; Chen et al., 2024; Grigorev al., 2023) realistically animate dynamic clothes humans in motion. They produce physically plausible deformations given garment in rest-pose, body shapes and motions. However, these models leverage cloth motion descriptors to predict the next cloth state. Hence, they are not designed for fitting to observations captured using vision sensors.
To allow for dynamic garments that can be fitted to observations using optimization, we present a physically grounded conditional generative model of 3D garment deformation. We define cloth matcrial the combi- nation of the three physical quantities bending, stretching and density. To work at high resolution, we take advantage of a latent diffusion model (Ho et al., 2020; Rombach et al., 2022) in a two-dimensional uu-space to conditionally deform arbitrary fixed garment template. Hence, our model is template-specifie and needs to be trained per garment template. The model provides fine-grained control over input conditions related to the person wearing the garment. and the garment's 's material. The difflusion model defines distribution over plausible garment deformations, enabling the generation multiple consistent outputs conditioned on the same input.
To efficiently condition our model on motion and body shape information, leverage parametric body model and condition the garment deformation body shape and sequence of poses describing the motion. While our model is agnostic to the parametric body model as long as it decouples identity and pose e.g. Neo- phytou & Hilton (2013); Pishchulin et al. (2017); Loper et al. (2015); Xu et al. (2020); Mihajlovic et al.
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(2022), SMPL (Loper et al., 2015) as it is easy to integrate. For physical grounding, condition model on parameters controlling cloth material by training the model with the output of a physics-inspired simulator. While any physics-inspires simulator that allows controlling material via parameters is applicable e.g. Baraff & Witkin (1998); Provot (1995); Narain et al. (2012); Bouaziz et al. (2014); Li et al. (2018), we use a projective dynamics simulator (Ly et al., 2020) due to computational efficiency and a low number of parameters.
For training and evaluation, we use the baysics-based simulator to generate synthetic dataset of dynam- ically deforming wide dress with complex geometry. The dress is simulated using different body shapes, mo- tions, and material parameters. The resulting dataset contains 172 unique body motions from AMASS (Mah- mood et al., 2019). Each motion is randomly performed by 3 body shapes and simulated with 3 cloth materials. The sewing pattern the dress used for simulation was chosen to correspond to a dress in 4DHu- manQutfit (Armando et al., 2023); a dataset of reconstructed dressed 3D humans motion acquired using a multi-viww camera platform. This allows for evaluation on acquired data.
We apply model to registration task, where optimize our model's parameters to fit the result to dynamic 3D point cloud sequence of 4DHHumanOutit. We build two stage pipeline that first fits a parametric body model to the sequence, and subsequently optimizes the latent vector of our model by minimizing the Chamfer distance.
Comparative experiments show that our approach outperforms state-of-the-art baselines in terms of geomet- ric shape fidelity and physically inspired metrics due to its capability of disentangling body motion and cloth material.
In summary, the main contributions of this work are:
A physically grounded method to dynamically deform a fixed 3D garment template to match input cloth materials, body shape and motion, based on a 2D latent diffusion model.
A dataset, available at https://doi. otte 01111100777555G2TTT0 of dynamic 3D dress with complex sewing pattern simulated for different body shapes, motions and cloth materials. It contains simula- tions of 172 motions with variations of body shape and cloth material, totaling over 1500 sequences.
A registration method fitting our model's parameters to dynamic point cloud sequences of captured dressed humans in motion.
2 Related Work
Dynamic garment modeling is growing research area in computer vision. Current learning- models can be categorized into two classes: pose-dependent garment modeling and dynamic cloth simulation. Achar et al. (2024) provide an exhaustive review on cloth draping methods. We focus on works that learn garment models, which in particular excludes works that reconstruct possibly animatable garments.
Pose- Dose-Dependent Garment Modeling. Modeling 3D garments is traditionally time-consuming and re- quires expertise. Data-driven cloth models alleviate this process by enabling parametric 3D modeling and automatic cloth-body draping. These models can be applied to various tasks, such as 3D model recon- struction from images or point clouds. SMPLicit (Corona et al., 2021) is a parametric model capable of generating diverse garments with a fine-grained control over cloth cut and design. Follow-up works modeled diverse garments given different inputs such as image (Sarafianos et al., 2025), 2D masks (Zheng et al., 2024), sketches (Wang et al., 2018) and more recently lext (He et al., 2024; Nakayarna et al., 2025; Bian el al., 2025). To fit the generated 3D model over different body shapes, a parametric body model (Loper et al., 2015) commonly usod. The garment can be posed using the body's Lincar Blend Skinning (LBS) weights. This straightforward approach needs to compute LBS weights of the garment w.r.t. the body and does not model pose-dependent high-frequency details.
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Table 1: Positioning our work w.r.t. existing learning-based 3D garment models that generalize over multiple body shapes and poses. Our model additionally generalizes across materials, models dynamic details, and allows fitting to observations in the form of dynamic 3D point clouds.
Generalization across cloth material
Models dynamic details
Allows fitting to observations
Pose- Deeeneepnddnnt Garment Modeling
TailorNet (Patel et al., 2020)
DiffusedWrinkles (Vidaurre et al., 2024)
Laczkó et al. (2024)
Cape (Ma et al., 2020)
Shen et al. (2020)
DrapeNet (De Luigi et al., 2023)
SkiRT (Ma et al., 2022)
Shi et al. (2024)
Dynamic Cloth Simulation
Santesteban et al. (2021)
SNUG (Santesteban et al., 2022)
GAPS (Chen et al., 2024)
HOOD (Grigorev et al., 2023)
ContourCraft (Grigorev et al., 2024)
MGDDG (Zhang et al., 2022)
Ours
Yang et al. (2018) studied cloth deformation across different body poses and motions by analyzing statistics of clothing layer deformations modeled w.r.t. an underlying paramctric body model. While this allows to generalize to new factors, the model has limited capacily.
TailorNet (Patel et al., 2020) proposed a scalable model that generalizes across body poses by predicting low and high frequency wrinkles driven by body shape, pose and garment style. DiffusedWrinkles (Vidaurre et al., 2024) leveraged a diffusion model in uv-space to generate wrinkles conditioned like TailorNet. Another line of works (Ma et al., 2020; Laczkó et al., 2024; Shen et al., 2020) models deformations directly over the body surface learning pose-dependent information. DrapeNet (De Luigi et al., 2023) splits the problem into 3D generative model and draping model. A recent approach based on point clouds (Ma et al., 2022) learns dynamic I.BS weights to model humans in loose clothing but it is not adapted for generative modeling. Shi et al. (2024) built a transformer model capable of synthesizing dynamic garments from body sequences.
Most of these approaches represent static deformations driven by single pose, omitting dynamic deforma- tions driven by body motion and the physical properties of the garment. Loose garments pose significant challenges due to reliance on either pre-computed LBS weights or deformations defined on the body surface. In contrast, we propose a latent diffusion model conditioned on physics-informed parameters, body pose and motion, enabling the generation of temporally coherent dynamic garments. Our approach is capable of capturing both static and dynamic wrinkles which are traditionally predicted by cloth simulators.
Dynamic Cloth Simulation. Physics-based simulation a traditional approach in computer graphics for garment animation (Nealen al., 2006; Baraff & Witkin, 1998). While state-of-the-art simulators are capable of accurately reproducing cloth dynamics (Li et al., 2018; Romero al., 2021), these methods are computationally expensive. Learning- based simulation has been introduced to accelcrate computation (Pfaff et al., 2021; Gancalz et al., 2020; Kairanda et al., 2024). The line of work most closely related to ours focuses on body interactions to animate dynamic garments over humans (Li et al., 2024; Santesteban et al., 2022; Chen al., 2024; Grigorev et al., 2023; 2024; Zhang et al., 2022). The use of a paramctric body model, instead of a generic rigid-body mesh allows to predict dynamic deformations from canonical space leveraging LBS for pose and shape deformations.
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Training
Template
Inference
Latent Diffusion Model
Body motion
Cloth material
Bend Density
Stretch
Figure 2: D- D-Garment generates garment deformations conditioned on body shape, motion and cloth material. It builds upon a 2D latent diffusion model (Sec. 3.2) to learn how to deform template in wu-space (Sec. 3.1). 3D mesh vertex displacement from template is parameterized by the wv displacement map, and our model is trained on it along with the conditional inputs. At inference, our model generates the deformed garment by ileratively denoising the Gaussian noise w.r.t. its conditional inpuls.
Recent works focus on deforming garments in response to body motion. Santesteban et al. (2021) formulated the motion given current pose and body part velocities and accelerations. SNUG (Santesteban et al., 2022) introduced a sell-supervised model to prune computationally expensive data generation. GAPS (Chen et al., 2024) improved SNUG by predicting LBS weights adapted to loose garments. HOOD (Grigorev et al., 2023) built a graph neural network enabling froe-flowing motion without relying on LBS for posing. ContourCraft (Grigorev et al., 2024) added new losses to HOOD reducing cloth-body collisions and cloth self- penetration. Motion Guided Deep Dynamic Garments (MGDDG) (Zhang et al., 2022) predicted garment deformations in generative way given previous states of garment and body.
Note that to achieve consistent cloth dynamics, current learned- based cloth simulation methods leverage previous cloth states either explicitly with vertices velacity (Grigorev et al., 2023; 2024; Zhang et al., 2022) or implicitly using recurrent architecture (Santesteban et al., 2021; 2022; Chen et al., 2024). In contrast, our approach is not auto-regressive, thereby enabling dynamic garment deformations at any given frame facilitating tasks such as fitting observations.
Positioning. Table positions work w.r.t. scalable 3D garment models that generalize over body shape and pose. Our work combines advantages of existing works and allows for generalization across materials, dynamic detail modeling, and fitting to observations.
3 Method
Given a garment template represented as 3D triangle mesh T, our goal is to learn a conditional generative model g, called D-Garment, that deforms into new mesh instances M while keeping the mesh topology fixed. We condition the generation on the underlying body shape, pose, pose velocity, and physical material properties of the garment. Conditioning on physical material propertics allows to physically ground the model. At test time, D-Garment can be used for generation and fitting.
D-Garment represents a dynamic garment top of parametric human body model that decouples body shape and posc parameters. In our implementation, we use SMPL (Loper et al., 2015), and represent body shape 3 and pose sequence 0.-2, 0z-1, O, as concatenation of the two preceding poses and the current one, where t denotes a discrete time step and I = 2 the pose sequence length. The representation of cloth material inspired by traditional works in physics-inspired simulation (Baraff & Witkin, 1998) and includes stretch coefficient (in N/m), mass density coefficient d (in kg/m²) and bending coefficient b (in N m) 7 (s. b). Parameter controls resistance to stretching compression, d controls the influence
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Given an inferred displacement map D, the inverse ue-map o allows to lift pixels to mesh vertices as Published in Transactions on Machine Learning Research (04/2026)
of inertia, and b controls resistance to bending or curvature changes. These parameters will be used to physically ground the model.
Inspired by the recent success of diffusion models in 2D domain generation, our 3D mesh generator G consists a 2D latent diffusion model, based on the latent diffusion architecture of Rombach et al. (2022). Our model, illustrated Figure 2, directly generates mesh deformation
Mr ~ G(0, 00.
This formulation is independent of intermediate body-driven skinning, unlike prior works e.g. (Patel et al., 2020; Zhang et al., 2022; De Luigi et al., 2023; Vidaurre et al., 2024), which removes the need rig T.
3.1 Garment Representation
To leverage powerful diffusion models in the 2D domain for generation, we reframe our 3D generation problem by representing garment deformations in 2D domain encoding (u, coordinates. Inspired by geomelry images from Gu et al. (2002), we encode relative 3D mesh vertex displacements from T into 2D geometric displacement map D via pro-computed parametrization Ø R + f1,2....,m) ..) where is the number of vertices of T.
T (R),
(2)
where M is the resulting mesh and is the vertex index. Inversely, forward mapping, combined with triangle barycentric coordinate-based interpolation, computes a continuous displacement map for deformed mesh M relatively to its template T for a given 2D coordinate (u, as
D -Mola,)) 7d(u.))
(3)
3.2
Diffusion Model
Using canonical 2D reprosentation of 3D garment meshes allows to benefit from well established scalable 2D latent diffusion architectures and their pre-trained weights Being the state-of-the-art in text to image generation, diffusion models have been extended to various applications (Po et al., 2024). The latent diffusion model introduced by Rombach et al. (2022) is made of a variational auto-encoder (VAE) that maps high resolution images to lower resolution latent space where reverse diffusion is learned based on denoising diffusion probabilistic models (Ho et. al., 2020) by a denoising network ég- The key strength of diffusion models lies in their reversibility. The forward process involves learning to predict artificially added noise in images. The reverse process, formulated noise from the latent space, reconstructing high-quality data step by step. We adapt this model to learn generation of geometric displacement maps with conditions that allow for physical grounding.
iterative refinement, enables the model to gradually remove
Training. First, we adapt the VAE of Podell al. (2024) to our mesh displacement map data distribution {Me Dih.. Since the statisties of this displacement map differs from natural images, pre-trained VAE would be sub-optimal. We finctune the decoder via an extended training loss combining L2 reconstruction error | - DIl2 and the displaced vertex-lo-veriex error || Mll2. Next, the conditional denoiser eg is trained with denoising score matching using samples [Dr, 0.- 0,- 0,, B, 7) from our training data corpus as
Ez,,, [[]c ca (Z, s 0 0, B.
where z E 644x61x is the latent for Dt, ~ N(0. I), the diffusion time step, and the noisy latent obtained with forward diffuusin 2, = a,Z + o.e. The scaling factor and standard deviation of the forward diffusion are
a II (1 Ba), a, Va., a, = vI ass
(5)
where variance Bn determined by the noise schedule.

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Inference. At test time, latent Gaussian noise Zr is iteratively denoised with ée to generate latent displacement map 2o, conditioned on body shape, poses and cloth material. The VAE then decodes into a displacement map D, which allows to compute mesh M with Equation (2). Several algorithms can be used to reverse the latent diffusion (e.g. DDPM (Ho et al., 2020) or DDIM (Song et al., 2021)) with varying levels of inference speed, sample quality and stochasticity. We use the + (Lu et al., 2022) sampler thanks to its quality for the number of denoising steps required.
Fitting. Inspired by a registration method (Guo et al., 2024), we use our model to deform T to observations represented as a 3D point cloud sequence P. As P typically contains additional points belonging the body or other garments, we fit our model to using Chamfer Distance LCD with the adaptive robust function p (Barron, 2019), cloth-body collision loss Le and regularisation loss Crey consisting of Laplacian smoothing (Nealen et al., 2006), normal consistency and edge length minimization. This can be viewed as generating sample meshes M that match P, leading to the optimization of the latent 27 as:
zj arg p (CCDp( + Le + Gregs 2z
(6)
where M is generated in a differentiable way through our sampler from a latent z7. conditioned on "y and the body motion 3,0,'s. s. The latent z is optimized with Adam (Kingma & Ba, 2014).
Inspired by the clothing energy term in Yang et al. (2016), Le defined
C.
Σ (1, B) min ||e bll2.
(7)
DEB
where 6m is an indicator function for points inside mesh and B contains the body mesh vertices.
4 Dataset
Prior works often use datasets containing tight garments, with little dynamic effects. To test our approach, we created a dataset of simulated 3D loose dress. We use design similar to one of the outfits in 4DHumanOutt fit (Armando et al., 2023), allowing to test our model on multi-view ructions of real dress. The dress is simulated over 172 motion sequences, performing walking and running motions from AMASS (Mahmood et al., 2019). For each sequence, three body shapes were uniformly sampled following B 1), where u is uniform distribution. Furthermore, for each sequence, three cloth materials (bending b, stretching 8, density d) were also uniformly sampled following ~ 4(100=8 10-1), [40, 200), (0.)), Fach sequence was simulated for all combinations of the sampled parameters.
Simulation. We used a simulator based on projective dynamics (Ly el al., 2020) which is less physically accurate but 10 times faster than implicit solvers. It requires a single value for each cloth material parameter: bending, stretching, density. Other simulation paramcters, which do not only depend on the cloth (friction, air damping, collision tolerance), were fixed across all sequences. The simulation frame rate was 50 fps. To initialize the simulation, the garment size was manually draped over the canonical body model, avoiding intersections. The garment was simulated over 100 frames, linearly interpolating from the zero pose and shape to the first frame of the sequence. It was then stabilized for 100 frames to reduce noisy motion.
Data cleaning. SMPL can contain self- seff-intersections, which cause unsolvable cloth-body intersections for the simulator. To solve this, we pre-processed AMASS using an optimization used in Tzionas et al. (2016) to minimize self-penetration. We optimized for body joints that are the most. disruptive in simulation i.e. arms and shoulders. Angular velocity of poses and the distance to the initial poses were also minimized to regularize the output and maintain temporal consistency. We post-processod simulated cloth sequences with remaining cloth-body intersections by pushing vertices outside following the nearest body normal. Some simulation failures have been manually removed from the dataset.
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5 Experiments
Implementation details. We implemented the inverse uo mapping Φ-1 using bi-linear interpolation. We found that the interpolation technique (e.g. nearest, bi-linear bi-cubic interpolation) is marginally impacting the mesh quality thanks to our high resolution image (512x512). By averaging texture coordinates that correspond to a single mesh vertex, we did not notice any discontimuity on the surface geometry across seams. To avoid remaining collision artifacts, the vertices are pushed outside the body mesh in a simple post-processing step ablated in Table 3.
We used a pretrained VAE from SDXL (Podell al., 2024) downscaling the images latents as R512x51223
R61x61X4 The decoder part was finetuned on 40k steps and then frozen during eo training. The denoiser network Eg was built with U-net (Ronneberger et al., 2015) that takes the encoded latents to 5 con- volutional layers of output channel size (64, 128, 256, 512, 512) and conditions via cross-attention (Vaswani et al., 2017). The following conditional input dimensions were used: y E R2, B E I,, 0 E R using 6 dimensional rotations (Zhou et al., 2019) of 18 body joints and a global translation. Both decoder and co weights were optimized using AdamW optimizer (Loshchilov & Hutter, 2019). We have trained eq on 50 epochs using a batch size of 32 lasting 10 days on single NVidia A6000. The noising schedule was set to cosine variance (Lin et al., 2024) increasing from 31 = 10-4 By = 0.02 sampled in 1000 diffusion steps during training and 20 steps during inference using SDE-DPM++ (Lu et al., 2022). The inference achieves interactive frame rate of 7.5 lps. We have used the Diffusers (von Platen el al., 2022) library to implement the diffusion model and PyTorch3D (Ravi et al., 2020) for the regularization loss Creg.
5.1 Evaluation Protocol
The garment geometry and body poses are normalized according to the current body global rotation and translation for each frame. The template T is the mean geometry of the normalized dataset. We compute its ue parametrization using OptCuts (Li et al., 2018) which jointly minimizes the distortion and the seam lengths, limiting under-sampling and discontinuities induced by seams.
Dataset.
We take 3 motions from the training dataset with corresponding variations of body shape and cloth material and change each factor at a time to an unseen value. For the body motion factor we use 3 unseen motions, namely " (C19-runtohoptowall " "B16-walktuue hof--ualttuucchanedrreetttt 3 test sets for unseen materials, body motions and body shapes, which allows to assess how well models generalize for each factor independently, We also build a challenging test set with all combinations of unseen factors. We randomly leave out 5% of the remaining training data for evaluation purposes.
and "1/7-uakktohoopoualk. This results in
Metrics. We quantitatively compare the geometric shape fidelity of the results to the test set with common 3D computer vision metrics. Vertex error E, shows the accuracy between the prediction and the ground truth. Chamfer Euclidean distance ECD assesses the shape similarity of the prediction and the ground truth. Chamfer normal distance E,, allows to compare the wrinkling between the prediction and the ground truth. Nearest vertices corresponding normals are compared using absolute cosine similarity. In the following, we denote by U the set of uniformly sampled points over the mesh surface M.
E. E11 11u- NEM
ECD
1 U
Σ
min ||0 NEU BEU
r|l + 10
1
Σ BED
min ||0 we
v/},
[M
These shape similarity metrics allow to estimate how well a method reproduces deformations compared to the simulator. However, they fail to evaluate physical quality. To address this limitation, we propose several physically inspired metrics inspired by Santesteban et al. (2022). The cloth-body collision percentage E, measures the amount of clothing predicted inside the body. Higher values indicate physically implausible clothing or simulation failure. Wrinkling error Es, related to bending b, compares dihedral angles to the ground truth. Area strain error E,, related to stretching s, comparcs the in-plane deformation from the rest shape the ground truth. The center of mass distance Ed, related to material density d, compares the global rigid motion to the ground truth. Note that Eb, F, and F. are independent of each other and provide
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Groundtruth
D-Garment
ContourCraft
MGDDG
Figure 3: We qualitatively compare D-Garment to HOOD (Grigorev al., 2023), ContourCraft (Grigorev et al., 2024) and MGDDG (Zhang al., 2022). D-Gameent generates visually plausible wrinkles without requiring cloth velocity or position input. Corresponding video can be viewed in the supplementary material.
Quantitative comparison to HOOD (Grigorev et al., 2023), ContourCraft (Grigorev et al., 2024) and MGDDG (Zhang et al., 2022). Best scores are in bold. Time indicates the inference time.
Table 2:
Unseen body shape
Unseen motion
Time
Shape Similarity
Physical Valldity
Shape Similarity
Physical Valldity
E.
EcD I
E.
E.
Eep
E.
E.
HOOD
14.10
1.49
0.59
1.09
0.80
4.81
9.46
21.21
2.37
0.65
1.67
0.88
4.71
16.68
0.08s
Cont. Craft
9.69
0.43
0.53
1.43
0.72
6.31
5.03
11.10
0.48
0.56
1.25
0.74
4.42
7.13
0.23s
MGDDG
8.31
0.33
0.56
1.10
0.81
27.60
4.02
9.53
0.38
0.59
1.13
0.84
36.51
4.95
0.35s
D-Garment
3.49
0.10
0.41
0.54
0.60
3.94
1.51
6.14
0.25
0.48
0.70
0.64
5.00
2.88
0.13s
Unseen material
Unseen all factors
Shape Similarity
Physical Validity
Shape Similarity
Physical Validity E,
E.
Eep
Eg
E.
E,
E,
Ex
E,
ECD
E,
E.
Eb
Ea
HOOD
25.31
3.21
0.67
1.58
0.91
4.61
20.96
19.75
2.52
0.61
1.79
0.82
7.65
14.88
Cont. Craft
10.28
0.46
0.57
1.30
0.73
5.62
5.68
9.75
0.38
0.53
1.08
0.70
7.59
6.34
MGDDG
8.68
0.34
0.58
1.00
0.84
29.08
3.77
8.51
0.34
0.56
0.96
0.81
31.23
4.28
D-Gorment
3.41
0.10
0.42
0.43
0.59
5.08
1.64
4.94
0.19
0.45
0.72
0.59
7.02
2.34
measure of isolated charactcristics.
They are definod as
E, 100
Σ 5m (v, B), DED
Es = Σ -(()))
E. -=-
FE
EEE
where the set of edges of mesh M, ((E) the signed dihedral angle between adjacent faces, and V {MM) is the integral area strain deformation from the rest pose.
5.2 Comparative Evaluation Baselines
We compare D-Garment to methods that allow for generalization across cloth materials and that model dynamic details. While one of the first approaches allowing for varying motions falls in this category (Yang

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Bending
Stretching
Density
Figure 4: Varying one cloth material parameter at a time. For each parameter, the color map shows per- vertex distances between minimal and maximal values.
10 cm
First component
Second component
Figure 6: For a given body motion and cloth ma- tcrial, our method can generate subtle yet plausible variations, by varying (from left right) the ran- dom latent noise of the diffusion model.
Figure 5: Results for variations of body shape. Our method adapts the garment to the body morphology.
al., 2018), this method does not decouple the influence of multiple factors once due to its limited capacity. For this reason, compare to the three strong baselines MGDDG (Zhang et al, 2022), HOOD (Grigorev et al., 2023) and ContourCrafe (Grigorev et al., 2024). MGDDG is trained on sequence of 175 frames at 50 fps containing walking and running. The training uses the code provided by MGDDG. HOOD's training on diverse garments generalizes well to our dress without the need of fine-tuning its original pretrained model. ContourCraft is based on HOOD with additional losses to overcome self-collisions. To align timings across methods with varying framerates, we measure all metrics at 10 fps.
D-Garment outperforms all baselines across most metrics in Table 2, with HOOD and ContourCraft achieving comparable results only in terms of strain error E., Notably, approach generalizes well to unseen body shapes and materials, though accuracy is slightly reduced for unseen motions. Given that the test set comprises examples with all factors unseen, our results demonstrate state-of-the-ae performance for unseen generation tasks across different cloth matcrial and body motion.
Figure 3 shows visual comparison to HOOD, ContourCraft and MGDDG simulated on a body motion. The results of D-Garment are visually closer the ground truth simulation. HOOD is struggling to handle pleats around the collar and the wrists because of its assumption of a flat rest shape. ContourCraft shows loss intersections than HOOD but suffers from the same limitations. MGDDG's results are visually pleasing but do not follow the expected material behaviour. More visual results are provided in supplementary material.
5.3 Ablation Study
We perform ablations on the input parameters of the model, the mesh subdivision and the technique in Table 3. For material and motion ablations, we remove y and 0 in the input vector of ca. respectively, and train the full model. The motion ablation keeps the current pose but omits the prior ones
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Table Ablation study w.r.t. different conditioning and processing strategies. Best scores are in bold.
Shape Similarity
Physical
Validity
E,
ECD
En
E
Es
Eg
Ea
w/o material
7.0097
0.2421
0.4740
0.6677
0.6341
8.6457
3.5387
w/ motion
5.2396
0.1985
0.4576
0.7087
0.6013
8.4377
2.3849
W post process
4.9589
0.1888
0.4583
1.3632
0.5839
7.2277
2.3163
W/ subdivision
0.1854
0.4585
0.8145
11.1532
2.3414
Garment
4.9447
0.1863
0.4525
0.7180
0.5951
7.0201
2.3384
which indicate body velocity. The full model outperforms all ablations in most metrics. D-Garment can benefit from the post-processing reducing collision artifacts from 1.3% to 0.7% of intersecting vertices with a marginal physical quality trade-off. Variations in mesh resolution have a negligible impact on the deformed shape, demonstrating the robustness of the we representation. Note that Ey and Es are not applicable on meshes with different topology, while the physical validity metrics E. and Es are topology sensitive.
Next, we study the influence of material parameters and body shape in the D-Garment outputs. Figures 4 and 5 show results for extreme parameter values used during training. Figure keeps all inputs constant while only varying a single cloth material parameter at a time. Note that each parameter influences the resulting geometry of the dress by subtly changing wrinkling patterns. This allows to generate rich set of results by controlling cloth material. Figure shows results when keeping all inputs constant while only changing single parameter of the body shape B, along the first two principal components. Note that the same dress is draped on different morphologies, while adapting its shape.
Figure shows results obtained for the same conditions using different random noise for each latent. D-Garment allows for high variability, while staying in the space of plausible dynamics.
5.4 Garment fitting to 3D observations
We apply our method to fit temporally consistent garments on humans captured using multi-view videos, represented sequence of unstructured 3D point clouds. This task presents challenges as the input data is noisy, and the points represent the human wearer in addition to the garment.
Using a recent method Toussaint et al. (2024), we first reconstruct point clouds of the multi-view dataset 4DHumanQuthit (Armando et al., 2023). We then fit a human motion prior (Marsot et al., 2022), modeled as discrete set of SMPL paramoters, to the reconstructed point clouds for each sequence. The body fitting process uses one-sided Chamfer loss, distances to manually placed landmarks and the clothing energy Ealtth from Yang et al. (2016) enforcing the body shape to stay inside the capture. Points in the capture are filtered for the cloth fitting process by removing points that near undressed body parts (feet, logs, hands and head) and isolating the largest cluster of points. We test two material optimization strategies. The first uses fixed material parameters initialized to values with minimal constraints (10-8, 2,00, 0.01). The second strategy optimizes y by optimizing 7 in addition to 2 in Equation (6). We optimize for a single input latent of our model following Section 3.2 to generated garments to point cloud sequences.
Figure shows qualitative result for two of the sequences of 4DHumanOutft (Armando et al., 2023). The input point cloud is shown in grey, and the fitted garment in orange. Visual results indicate that D-Gorment can robustly recover the garment from dynamic 3D point clouds. Figure 8 shows quantitative results of the fitting application these two sequences. We measure distances from vertices of D-Garment fittings to their nearest neighbors on reconstrueted frames of the capture. All fitting results achieve an accuracy inferior to 2.4cm on 90% of garment vertices. A distance of 2.4cm between our result and the input point cloud is small compared to the highly dynamic garment motions present in 1DHumanQuthi Furthermore, the error is overestimated for some parts as the dress model is complete, while the reconstructions in "rHumanQuthi are missing parts due to occlusions in some frames, as shown in Appendix A, where the lower arm and parts of the torso not reconstructed. Note that D-Garment fitting results achieve high accuracy despite
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this domain gap. Optimizing for material parameters allows for smoother fitted results with slight loss in surface to target distance.
6 Conclusion
This paper presented D-Garment, a template-specific 3D dynamic garment deformation model based on a 2D latent diffusion model enabling temporally consistent generation given the body shape, pose, motion and cloth material. The model was learned dataset of dresses with physical grounding by conditioning the result cloth material parameters. Code and dataset are available at https:/ http:://dmmuliaa github. io/ d- garameet...... Experimental evaluations on both simulated and real data confirm the versatility of D- Gorment for diverse tasks while providing state-of-the-art results. These findings highlight. the potential of diffusion- based gencrative models to realistically deform 3D dynamic garments, as well as solving the challenging task of extracting garments from 3D point cloud observalions.
7 Statement of Broader Impact
This rescarch advances garment animation within creative industrics, facilitating the rcalistic rendering of dressed avatars in diverse virtual environments. Furthermore, it holds significant potential for virtual try-on applications, aiming to mitigate high product return rates. However, the technology raises ethical concerns regarding potential misuse, particularly the generation of non-consensual realistic imagery and the associated privacy implications. Additionally, the reliance on substantial computational resources entails a negative environmental impact and limits accessibility for segments of the population.
(a) Fixed material
(b) Optimized material
D- D-Garment used to fit a captured 3D sequence. For each time frame of the corresponding motion (lines), we show the raw capture (left blue), the sampled point cloud (middle grey), and the estimated garment (right orange). We accurately recover garment pose and fine wrinkles using two different material optimization strategies and b. Corresponding videos are in supplementary material.
Figure 7:
12

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Vertices below threshold
Vertices below threshold
100
100
75
75
50
50
2.14 cm
2.34
25
25
0
5
10
0.0
2.5
5.0
7.5
10.0
Surface to target distance (cm)
Surface to target distance (cm)
(a) Fixed material
(b) Optimized material
Figure 8: Cumulative distances of D-Garment fittings to input 3D point clouds for two sequences of 4DHu- manOutfit with different material optimization strategies and b. The vertical bar indicates the maximum distance at 90% of the vertices.
Acknowledgments
This work was partially funded by the Al laboratory by InterDigital and Inria. We thank João Regatciro and Abdelmouttaleb Dakri for helpful discussions, and Rim Rekik Dit Nkhili and David Bojaníć for help with the SMPL fittings.
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A Dress template and capture
Figure 9: The dress model (left) used for D-Garment dataset differs slightly from the captured dress (right) from 4utumanOuthit (Armando et al., 2023) in terms of length and wrinkle details.
B Garment generalization
D- Garment allows to realistically deform fixed template. It therefore requires a separately trained model for each garment design. To show the model's applicability different garments, we generated dataset by simulating T-shirt from Korosteleva et al. (2024) Sec. 4. This dataset is significantly smaller than the original with 67 simulated sequences instead of 1519, resulting in 10089 training data points instead of 385448. We simulate one more lest sequence with all input parameters unseen to provide unbiased results in Fig. 10 and Tab. 4. Fig. 10 shows that the new model can generate visually pleasing dynamic T-shirts. Quantitative results Tab. are similar to the results for the dress in the main paper, showing that D-Garment applicable to different garments.
subsample of the original dataset described in
Figure 10: Visual results of D- D-Garment on 4 frames of test sequence for the T-shirt model.
Table 4: Quantitative results of D- D-Garment trained for a T-shirt model using the test sequence.
Shape Similarity
Physical Validity
E,
ECD
E,
E. E, E, Eg
T T-shirt
3.59
0.05
0.42
0.03
0.46
1.21
2.13
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C Influence of the pose sequence length
The pose sequence length I defines the number of poses 0 input to the model. Tab. shows quantitative results of different pose sequence lengths. Here, 1 corresponds to the static case, where no temporal information is available. The pose sequence length used throughout the paper is is =3 3, which is the minimal length that allows to learn about dynamics caused by acceleration. We further show results for D- Garment can benefit from longer input sequence in terms of shape similarity metrics, while maintaining the same level of physical accuracy.
Table Comparison of pose sequence length conditioning. Best scores are in bold.
Shape Similarity
Physical Validity
E,
ECd
En
E
Es
E,
Ea
5.2396
0. 0.1985
0.4576
0.7087
0.6013
8.4377
2.3849
3
4.9447
0.1863
0.4525
0.7180
0.5951
7.0201
2.3384
5
4.8651
0.1811
0.4508
0.7450
0.5985
8.2247
2.2235
D Ablation of seam processing
The we paramctrization computed by Optcuts (Li et al., 2018) minimizes combination of distortion, seam length, and the number of seams. However, remaining seams can cause discontinuities where multiple locations in the uv-map correspond to a single mesh vertex. To smooth boundaries, we average the values of all locations in the ut-map corresponding the same vertex. We further interpolate the texture using bilinear filtering smooth the texture globally. As demonstrated in Fig. 11, these techniques effectively preserve coherent geometry.
(a) without processing
(b) with seam processing
Figure 11: Visual comparison of the seam processing. seams represented in red. Fig. 1la samples the nearest texel for each w--coordinate and randomly picks between seam vertices, Fig. 11b averages seam vertices and interpolate the texture with bilinear filtering.
18


Reviews:
[{"review_id": "KcTmbGhdnJ", "reviewer": "Reviewer_asHP", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes D-Garment, a method for dynamically deforming 3D garments using a conditional latent diffusion model. The core contributions are:\n- A 2D Latent Diffusion Model (LDM) that takes conditional inputs: body shape, pose, pose velocity, and physical material of the garment and deforms the input 3D garment in the UV-space. \n- Dataset Contribution:  A synthetic dataset of dynamic 3D dress with over 172 motions with variations of body shape and cloth materials.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The authors claim that they present a physically grounded conditional generative model of 3D garment deformation. This claim is supported by the description of the method (Sec. 3) where the human-boyd motion is parametrized by the SMPL model and clothing material is inspired by physics-grounded simulation (Baraff & Witkin, 1998).", "audience_yn": "Yes", "audience_explanation": "This work is relevant for people working in graphics. Further, it has applications in the AR/VR industry (virtual try-on, gaming, etc.).", "requested_changes": "- **Comparison with missing baselines:** The authors did not compare with recent work Gaussian Garments [R1].\n- **Generalization across garment types:** The authors report results for only a limited number of garment types. Does the method work on short jackets, long woolen cloths, loose trousers etc.? Is this a limitation of the method?\n- Does the proposed method work for real-world images? \n- Missing comparison of inference time with the baseline methods. \n\n\n[R1] https://eth-ait.github.io/Gaussian-Garments/", "broader_impact_concerns": "As this technology can be used to generate images with different clothing, I request that the authors add a statement regarding broader impact concerns.", "additional_comments": "**Strengths**\n- The proposed method uses a 2D diffusion model instead of a 3D Diffusion model by employing uv-maps. \n- The proposed method used body motion cues for garment deformation, which is a step forward in garment modeling.", "extra_scores": {}, "extra_text": {}}, {"review_id": "jAgWaqNEkh", "reviewer": "Reviewer_zDhU", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors present a method to generate 3D garment deformations by mapping them to 2D UV space and applying a Latent Diffusion Model. Unlike many existing draping methods, this approach explicitly conditions on physical parameters like stiffness and density, allowing for disentangled material control. To support this, they release a synthetic dataset of motion sequences created with Projective Dynamics. Finally, they show how the generative model can be used as a prior to fit noisy, real-world data from the 4DHumanOutfit dataset.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper presents both geometric and physics-based evaluations. Comparison against HOOD and others in Table 2 looks good, with a fairly big drop in vertex error (4.9 compared to 19.7 for HOOD) and improved collision handling. Figures 4 and 5 demonstrate that the model actually disentangles material from shape. The application to real data in Figure 8 also shows decent results, fitting real scans within ~2cm. My main issue is that the generalization claim is somewhat overstate. It works on new motions/materials but seems limited to just one dress topology.", "audience_yn": "Yes", "audience_explanation": "This work fits right into the current trend of mixing physics simulation with generative diffusion, which is obviously a hot area. It addresses some real-world bottlenecks in avatar creation, so I think it will be of interest to researchers working on neural rendering and geometric deep learning.", "requested_changes": "Critical adjustments:\n\n1. My main issue is the scope of generalization. The method is presented as a general deformation model, but the authors only show results on a single dress topology. It wasn't clear if this works out-of-the-box for different garments or if it requires per-category retraining. If it's the latter, the claims in the abstract need to be toned down to reflect that it's template-specific.\n2. Using 2D diffusion often leads to seam artifacts. I see the paper mentioned averaging to fix this, but visual proof is necessary. Can the authors show a close-up of the mesh boundaries? To make sure the \"averaging\" isn't just blurring out bad geometry?\n\nSuggestions for improvement:\n\n1. Regarding the fitting to 4DHumanOutfit, do the inferred physical parameters make sense? I'm curious if the optimized stiffness actually matches the visual material of the real garment, or if the model is just using those parameters to overfit the geometry. A short discussion here would strengthen the physical grounding argument.\n2. The ablation in Table 3 shows a massive increase in strain error when using subdivision. This looks a bit weird. Is this due to high-frequency noise? An explanation of this behavior would be good.\n3. Please compare the 7.5 fps speed against the baselines. Is it competitive? Also, is there a sweet spot for diffusion steps that balances speed and quality?", "broader_impact_concerns": "The paper focuses on garment simulation and reconstruction. No specific negative impact unique to this method stands out that would require a mandatory revision, but a standard statement on the responsible use of realistic avatar technology would be appropriate.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "64jATWx7ya", "reviewer": "Reviewer_oXuo", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents D-Garment, a learning-based method for generating dynamic 3D garment deformations conditioned on body shape, motion, and physical cloth material properties. It encodes 3D mesh deformations as 2D displacement maps in UV space and uses geometry images approach to leverage powerful 2D diffusion models. It outperforms HOOD, ContourCraft, and MGDDG across all metrics (Table 2) and successfully fits to 4DHumanOutfit captures with <2.4cm error at 90% vertices.", "claims_and_evidence_yn": "No", "claims_explanation": "Only tested on one dress type, generalization to pants, shirts, jackets unclear.\nDataset diversity is limited compared to methods like HOOD.\nTest set has unseen motion + unseen shape + unseen material - extremely challenging but makes it hard to assess generalization on each factor independently.\nSequence length: Why only 3 poses (l=2)? Did you experiment with longer histories?\nMaterial range: How did you select the uniform sampling ranges for γ? Are results sensitive to this?", "audience_yn": "Yes", "audience_explanation": "To me using 2D latent diffusion for 3D mesh generation via UV mapping is an interesting design pattern that could transfer to other 3D domains.\n\nPhysical grounding: The conditioning on material parameters (rather than just pose) addresses a real limitation in prior work - interesting for physics-informed ML community.", "requested_changes": "Limited real-world testing, unclear generalization about dress type, motion, material range, lacking discussion of limitations and failure cases.", "broader_impact_concerns": "Missing Broader Impact Statement:\nThe paper does NOT include a Broader Impact Statement, which is concerning given: Direct human-facing application (virtual try-on, avatars).  Socioeconomic implications (labor, accessibility). Use of human body data.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)