Generative Human Geometry Distribution
Authors: Xiangjun Tang
Biao Zhang
Peter Wonka
Venue: ICLR 2026 Oral (2026)
Keywords: 3D Generation
Human Generation
Geometry Encoding
Primary area: generative models
TL;DR: We introduce the first method that integrates geometry distributions into generative modeling.

Abstract: Realistic human geometry generation is an important yet challenging task, requiring both the preservation of fine clothing details and the accurate modeling of clothing-body interactions. To tackle this challenge, we build upon Geometry distributions—a recently proposed representation that can model a single human geometry with high fidelity using a flow matching model. However, extending a single-geometry distribution to a dataset is non-trivial and inefficient for large-scale learning. To address this, we propose a new geometry distribution model by two key techniques: (1) encoding distributions as 2D feature maps rather than network parameters, and (2) using SMPL models as the domain instead of Gaussian and refining the associated flow velocity field. We then design a generative framework adopting a two-staged training paradigm analogous to state-of-the-art image and 3D generative models.
In the first stage, we compress geometry distributions into a latent space using a diffusion flow model; the second stage trains another flow model on this latent space. 
We validate our approach on two key tasks: pose-conditioned random avatar generation and avatar-consistent novel pose synthesis.
Experimental results demonstrate that our method outperforms existing state-of-the-art methods, achieving a 57% improvement in geometry quality.

Published as a conference paper at ICLR 2026
GENERATIVE HUMAN GEOMETRY DISTRIBUTION
Xiangjun Tang
Biao Zhang" KAUST
Peter Wonka
KAUST
KAUST
xiangjun.tang @ kaust.edu.sa
biao. z outlook.com
pwonka @ gmail.com
ABSTRACT
Realistic human geometry generation is an important yet challenging task, re- quiring both the preservation of fine clothing details and the accurate modeling of clothing-body interactions. To tackle this challenge, we build upon Geometry distributions- recently proposed representation that can model single human geometry with high fidelity using flow matching model. However, extending a single-geometry distribution a dataset non-trivial and inefficient for large- scale learning. To address this, we propose new geometry distribution model by two key techniques: (I) encoding distributions as 2D feature maps rather than net- work parameters, and (2) using SMPL models as the domain instead of Gaussian and refining the associated flow velocity field. We then design a generative frame- work adopting two-staged training paradigm analogous to state-of satie-of-hhhe sate-of-theeart image and 3D generative models. In the first stage, we compress geometry distributions into latent space using diffusion flow model; the second stage trains another flow model on this latent space. We validate our approach on two key tasks: pose- conditioned random avatar generation and avatar-consistent novel pose synthesis. Experimental results demonstrate that our method outperforms existing state-of- the-art methods, achieving 57% improvement in geometry quality.
INTRODUCTION
3D human geometry generation is a critical yet challenging task. The human body exhibits high- frequency clothing details, which are inherently difficult to synthesize. Therefore, the core of this task the design of a representation that can capture both the underlying structure and fine details, posing two key challenges: encoding high- freuureeyyncy details into a low-dimensional manifold without losing fidelity; 2) modeling the relationship between clothing wrinkles and poses to preserve realistic details.
Existing methods represent the human body in different ways. NeRFs (Men et al., 2024; Zhang et al., 2022; Xiong et al., 2023; Jiang et al., 2023; Xu al., 2023; Zheng et al., 2024) focus on the rendering results but neglect underlying geometry and are constrained by the rendering speed and resolution. Implicit functions (Xu et al., 2022), such as signed distance functions, struggle to synthesize thin structures and tend to oversmooth results. Point clouds (Zhang et al., 2024c:d) and volume-based representations trade off between memory efficiency and quality. Recently, geometry distributions (Zhang et al., 2025) model single 3D shapes as distributions of points on their surfaces. Samples X from a Gaussian distribution V are transformed via flow-based diffusion network {(X) into the target geometry, capturing fine-graing ine-grained surface details (see Fig. 1). This formulation enables infinite point sam- pling, allowing for a high-fidelity representation of individual shapes. However, extending a single- geometry distribution to an entire dataset is non-trivial because of two reasons: 1) single-geometry formulations store the geometry in the parameters memory consumption and limits their scalability for generative tasks, and 2) while learning the flow velocity fields from Gaussian distributions to a single shape is feasible, extending this to multiple
Flow model
X N~N
E(X)
Figure 1: Geometry distribution.
flow network, which leads to prohibitive
"Corresponding Author

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Published as a conference paper at ICLR 2026
(a)
(b)
Figure 2: (a) Denoising process of human geometry distribution. (b) Random avatar generation for given pose. (c) Novel pose generation of given avatar. Results are rendered from point clouds.
shapes dataset becomes computationally expensive and inefficient. To this end, propose a novel human geometry distributions model by two key techniques:
We encode each human geometry distribution into a feature map rather than the network weights of flow network directly, providing generalized way represent geometry distributions.
We adopt the SMPL (Pavlakos et al., 2019) model distribution instead of Gaussian and refine the associated flow velocity field for efficient learning. Specifically, we construct spatially efficient mappings from the velocity field and reformulate them into regularized dense space.
Based on these designs, we propose the first 3D generative method geometry distributions, mod- eling the distribution of individual human geometry distributions. Our framework is formulated in two stages: first, we use flow model to compress each human geometry distribution into a compact feature map, from which high-fidelity human geometry be sampled through a denoising pro- cess (Fig. 2 (a)), and then, we train another flow model to generate human geometry distributions (i.e., the feature maps). While the currently top-performing methods (Xu et al., 2022; Zhang al., 2024d; 2023b) obtain visually plausible geometric details by enhanced rendering techniques, our method synthesizes high-fidelity geometry directly, leading large improvements. We design two generative tasks to evaluate the effectiveness of our method (Fig. 2 (b) and (c)). The first task gen- erates random 3D human geometries conditioned on given pose, while the second enables novel pose generation of given avatar. Our quantitative results show that our method improves the ge- ometry quality by 57% (42.9 to 16.2) and the visual appearance by 7% (17.4 16.2) compared to the state-of-the-re
RELATED WORK
2.1 GENERAL 3D GENERATION
3D generation methods employ various representations, such as meshes, voxels, point clouds, and implicit functions, each offering distinct advantages but also facing inherent limitations (Zhang et al., 2025). Here, we focus on compact representations that scale efficiently to large datasets.
Given one or more images, direct inference of pixel-aligned positions (Yu et al., 2021a; Tang et al., 2024; Xu et al., 2024b;a) avoids intermediate representations, which unsuitable for modeling intrinsic human shapes. A popular alternative is to synthesize tri-planes as intermediate represen- tations (Wang et al., 2023; et al., 2023; Hong et al., 2023), from which implicit functions or points (Zou et al., 2024; Han et al., 2024) can be sampled. However, tri-planes their resolution, restricting their ability to capture fine- grained details. GaussianCube (Zhang et al., 2024a) transfers Gaussians into regular voxel grid via optimal transport, limiting its flexibility for
limited by
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Table 1: Comparison of 3D human representation methods.
Method
Loose Clothing
Scalability
Fine Details
Tri-plane + NeRF (Noguchi et al., 2022; Zhang et al., 2022)
Template-hased (Hong al., 2022; Hu al., 2024)
X
Primitive Volumes (Chen et al., 2023)
/
X
Mesh-based (Sanyal et al., 2024; Xu et al., 2024c; Feng et al., 2023)
/
x
Point-based (Abdal et al., 2024; Zhang al., 2024d)
/
x
Implicit Function (Xu et al., 2022; Zhang et al., 20236)
/ X
Goometry Distributions (Ours)
Gamba (Shen et al., 2024) and VectorSet (Zhang al., 2023a) employ cross- attention mechanisms to inject 3D embeddings or image tokens, albeit constrained by memory con- incorporate sumption. Trellis (Xiang et al., 2024) leverages sparse voxels but lacks mechanisms human-specific priors. In contrast, 2D maps (Rai et al., 2025; Elizarov et al., 2024; Yan et al., 2024), offer a memory-efficient alternative that aligns naturally with human priors.
intricate articulation.
2.2 3D HUMAN RECONSTRUCTION AND GENERATION
Features extracted from images (Saito et al., 2019; 2020; Alldieck et al., 2022) provide rich view- dependent information, enabling high-fidelity human reconstruction. Some works incorporate ad- ditional cues such as voxel grids (He et al., 2020), human templates (Zheng et al., 2021), or multi- view images (Shao et al., 2022) to mitigate the depth ambiguity of single-view image. For example, ICON (Xiu et al., 2022) and ECON (Xiu et al., 2023) predict the frontal and back normals from the SMPL template and reconstruct the human using these maps. Building on these reconstruction techniques, generative networks such as GANs (Xiong et al., 2023; Jiang et al., 2023) and diffu- sion models (Sengupta et al., 2024; Shao et al., 2022; Zhang et al., 2024b)-aara been employed to synthesize mixel-aligned features or generate multi-view images (Kolotouros et al., 2024) for human generation. However, these methods are inherently limited by their reliance view-dependent fea- tures, sensitivity to multi-view viel inconsistencies, and often require post-processing to fuse information from different views (Zhang et al., 2024b), failing capture the intrinsic 3D shape.
To learn intrinsic 3D representations, a popular approach (Noguchi et al., 2022; Zhang et al., 2022; Yang et al., 2024; Wu al., 2023; 2024) is to train a GAN model that synthesizes tri-planes and uses NeRF for rendering. However, these methods are constrained by rendering speed and resolu- tion, often requiring super-resolution modules (Bergman al., 2022: Dong al., 2023), multi-part structures (Xu et al., 2023), or refinement stages (Men et al., 2024; Zheng et al., 2024) to achieve high-fideling results. addition, Gaussian (Lan et al., 2024) employs diffusion model to learn the tri-pl plapp representation for 3D heads, providing an alternative to GAN-based approaches. Rather than employing tri-planes, alternative methods incorporate human templates, alleviating sparse spa- tial occupancy, but struggle with loose clothing (Hong et al., 2022) or still rely on super-resolution modules (Hu et al., 2024). Explicit representations, such as primitive volumes (Chen et al., 2023), provide efficient rendering but face challenges in extracting detailed geometry. Mesh-based meth- ods, using displacement (Sanyal et al., 2024) or layered surface volumes (Xu et al., 2024c), are limited by fidelity and struggle with loose clothing. Point-based representations (Abdal et al., 2024; Zhang et al., 2024d;c) offer flexibility for modeling thin structures but are constrained by point den- sity. Furthermore, can be difficult produce accurate 3D geometry under indirect supervision from rendering. In contrast, gDNA (Xu et al., 2022) proposes learning implicit functions directly from 3D data, but synthesizing high-fidelity geometry remains challenging due to inherent repre- sentation limitations (Zhang et al., 2025). Our approach directly learns from 3D data while enabling "infinite" sampling, leading to high-fidelity geometry synthesis. See Tab. I for comparison.
PRELIMINARIES
Flow Matching Flow matching (Lipman et al., 2023) is a variant of diffusion models. It constructs a flow that transforms samples from a source distribution p to a target distribution q. Specifically, the training objective loss is defined as follow:
arg min Exoxp.xxr..te0... [x,,(x;, () (x) - xo)
where x, = (1 -1-68 t)x, + X1 After training, an ordinary differential equation is solved to transition from Xo to X1, allowing to sample x1 q by first sampling Xo p.
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Based on the flow matching formulation, the core of modeling is to identify suitable source distribution and a target distribution that aligns well with our problem. For brevity, we omit E [0.1] in subsequent equations.
Geometry Distributions Geometry distributions model a surface M C R as a probability dis- tribution M.. where any sample X1 PM corresponds to a point on the surface. To achieve this, Zhang et al. (2025) adapt diffusion model to learn the transformation from Gaussian distribution (0, 1) to the target distribution of surface points. point x1 E M on the target surface can then be obtained by solving an ODE with an initial point (0, 1).
4 METHOD
4.1 OVERVIEW
In this section, we first introduce our human geometry distribution formulation by identifying suit- able source and target distributions (Sec. 4.2). Next, we describe encoding this distribution into feature map using an auto-decoder architecture, as depicted in Fig. 4 (a) (Sec. 4.3). Finally, we present generative tasks aimed at synthesizing the feature map, guided by the SMPL template, op- tionally incorporating additional conditioning inputs, as illustrated in Fig. 4 (b) (Sec. 4.4).
4.2 HUMAN GEOMETRY DISTRIBUTION
Our insight is to replace the Gaussian distribution (0, 1) in the prior geometry distributions (Zhang et al., 2025) with the SMPL template shape distribution &ss aligning the source distribution closer to the target geometry distribution 4 naive approach can be formulated as:
arg min Ex xi
llug (Xe, t) (x1 - xo)]l.
(2)
Building upon this, we propose two strategies reduce the model- ing complexity and improve training efficiency, including explicitly constructing training pairs and normalizing samples from distributions.
where (1- ()xo +
Training Pair Construction. Since the learned probability flow by Eq. approximates a con- ditional optimal transport (Lipman et al., 2024), one strategy for improving training efficiency is avoiding learning extrancous paths between distant points. To achieve this, we prioritize short flow by constructing training pairs set {xx]) x1)rT where x, 4s is geometriallyy close to x1 Ly. Specifically, we first sample a set of points {xo}s on the SMPL. template and set of points {xI]T on the target geometry. Then, for each X1 E {x1 J,, the corresponding x is obtained by:
x arg
min ||x| (xo)s
Notably, because multiple points x₁ may share the same nearest SMPL points xy,, especially in loose or wrinkled regions, directly learning path from x1 to X1 leads to under-sampled geometry (similar the artifacts shown in the second (000, σ) to X4. introducing randomness to enhance sample diversity. In this formulation, the source distribution becomes N(x,, o)) and the target distribution is the human geometry. Sampling from the constructed training pairs, the flow matching optimization objective is denoted by:
of Fig. 6). Therefore,
add perturbation drawn from
arg min B
Exo~N(x (x) (()))))) II-I1
(3)
Distribution Normalization. When training the trans- formation from the SMPL template to the human geom- etry of dataset (Eq. 3), the network receives spatially imbalanced supervision due to unevenly sampling points, which arises from two factors. First, points lie only the surfaces, making them extremely sparse and unevenly distributed relative to the full 3D space. Second, varia- tions in human pose and body shape further exacerbate this spatial imbalance, some regions more dynamic and thus sampled less consistently across
E(X)
SMPL templates
Human geometries
Figure 3: Aggregated samples (x() X1).
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Conditional Distribution Encoding
Human Geometry Generation
map
Conditioning
(R.)
Decompression
Images
Text
Representations
1 NgtR
Bilingar sample
Denoiser
SMPL verex map
map
SMPL veroox
Figure 4: Overview of our method. (a) We encode a geometry into a feature map, which is decom- pressed with a SMPL vertex map. The decompressed feature serves as a condition for our denoising network. (b) The human generation task is formulated as the conditional generation of feature maps, guided by the SMPL vertex map, optionally incorporating additional conditioning inputs.
geometries. Fig. 3 visualizes this effect by aggregating sampled points from multiple training it- erations, showing that some spatial regions have high point density while others remain sparsely sampled. This spatial imbalance leads to inefficient training, the model pays more attention to high-density regions while neglecting low-density regions. To address this, we normalize both source and target distribution by subtracting x/- The source distribution becomes zero-centered Gaussian distribution .V (00, 1), with a set to 1, and the target distribution is modeled as a regular- ized dense displacement field Ax X1 X/- Note that this subtraction removes the positional information of X6. To retain this information without suffering from imbalanced sampling, we rein- troduce x conditional signal that scales the network hidden feature, indirectly influencing the feature representation. By building flow between regularized dense spaces, we reduce the modeling complexity and achieve improved training efficiency, as demonstrated in Sec 5.2. The optimization objective is denoted by:
arg min (x,,)x,),) |lu (x,, (xx)) (Ax n)ll,
where (O0 1), (x/, x1) E ((x6,x1)) and x, (1 f)n tAx.
4.3 CONDITIONAL DISTRIBUTION ENCODING
The dataset for our task consists of a set of pairs D = (S. each associated with a latent representation s,ss, which is used to recover T (target geometry) by conditioning on S (SMPL). This design aligns with our generative task, i.e., S-conditioned T generation. The training loss can be written as,
arg min (s |u(, zT(s) (Ax - n)|l 0,f
(5) To enhance the expressiveness, we model IT E CCCHW as a compressed 2D feature map (height H and width W), shown in Fig. (a). The representation aligns with recent advancements in UV representations for 3D objects (Yan et al., 2024). Furthermore, we use a decoder network (-c to decompress ZT|S to higher resolution. By leveraging the UV coordinates associated with x denoted as (ec s) (x)). Thus, the revised optimization can be written as
then obtain per-point latents the surface S by sampling the high-resolution map,
arg min ((S,T)E [uo t/x/ Dec (ZTT)) (Ax -n]|| (271()))))
(6)
Decoder. As illustrated in Fig. (a), the network (.c (-) UNet-style network that contains several downsampling and upsampling layers. We render SMPL vertex positions into UV maps and concatenate them with hidden features in the convolution layers.
Denoising network. The design of the denoiser ug is adapted from (Zhang et al., 2025). In this case, we have two additional conditioning signals, SMPL point x and latent (2c15) Ad- ditionally, we augment x,, by concatenating normals and canonical coordinates. Surface normals provide directional cues for clothing inference, while canonical coordinates encode body part se- manties (e.g., distinguishing limbs from the torso).
4.4 GENERATIVE TASKS
After finishing learning the latents {zT(s). train generative models in the latent space. We investigate two tasks: pose-conditioned random generation and novel pose generation given avatar, both built U-Net (Karras al., 2024; 2025) architecture, illustrated in Fig. (b).
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Pose-conditioned random generation synthesizes diverse human geometries conditioned SMPL template mesh. To encode pose information, we render SMPL vertex positions into UV maps and inject them residual connections into the U-Net architecture. For novel pose genera- tion, the model additionally takes a frontal normal image as an extra condition to indicate the avatar identity. Specifically, for each animation sequence in the dataset, provide the normal image condition and another frame to provide the SMPL pose condition. We leverage the frozen DINO-ViT model (Caron al., 2021) to extract image features, which are then fused into the U-Net via cross- cross-attention layers.
randomly select one frame
The pose-conditioned generation networks are trained on the THuman2 dataset (Yu al., 2021b), while the novel pose generation is trained on 4DDress (Wang et al., 2023). See the Appendix for implementation details.
EXPERIMENTS AND RESULTS
In this section, we first conduct ablation studies on the training pair construction strategy and net- work architecture. Next, we compare different formulations of geometry distribution, including the formulation proposed by Zhang et al. (2025) and our proposed alternatives. Finally, we compare our method with state-of-the-art generative methods regarding the two selected generative tasks.
5.1
ABLATION STUDY
5.1.1 TRAINING PAIRS CONSTRUCTION
Rather than searching for the nearest points on the dense SMPL mesh, we first sample a relatively sparse set of points from the SMPL mesh and then determine the nearest points x/ from this sampled set. distributes the mapping workload across different Xy, which improves sampling efficiency. To validate this, we train our auto-decoder under both strategies and present the results in Fig. 6.
The first row shows the results using the sparse points set sampling strategy, while the second row corresponds to directly identifying the nearest points the SMPL mesh. The normal maps in the second row exhibit noticeable holes, indicating insufficient sampling. These artifacts are particularly pronounced for loose clothing (middle case in Fig. 6) but also appear in regions of relatively tight clothing (left and right case in Fig. 6).
5.1.2 NETWORK ARCHITECTURE
We experiment to validate that the auto-decoder outperforms the existing auto-encoders for our task. The auto-encoder learns features from the input, requiring
computations and being potentially constrained by the input's represen- tation capacity. We adopt the 3D representation network VecSet (Zhang et al., 2023a) as the en- coder for the auto-encoder and take 50, 000 in- put points, balancing representation capability and memory consumption. Since VecSet uses independent embeddings rather than feature map, we treat each pixel of the feature map 2212218 as individual embedding, yielding 576 embeddings (FeatureMap). To further ensure a fair com- parison, introduce additional model where replace our feature map decompression module (Fig. 4 (b)) with the one proposed by VecSet, maintaining as much of the VecSet architecture as pos- sible (VecSet).
Zhang al.
wro Pairs
wio DistNorm
Ours
Figure 5: Single-geometry fitting visualization.
The experiment is conducted on the THuman2 dataset (Yu et al., 2021b). As shown Tab. 3, we report the average distance between the synthesized points and the surface. While converting inde- pendent embeddings to a feature map improves reconstruction accuracy, the results remain inferior to our proposed auto-decoder.

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Zhang DistNorm
Figure 6: Comparison of strategies for construct- ing training pairs. The first row shows our proposed strategy, the second row finds the nearest points on the SMPL mesh
Visualization results of differ- ent geometry distribution formulations at the 30, 000th training iteration. GT represents the ground-trut result.
Figure 7:
5.2 COMPARISONS OF GROMETRY DISTRIBUTION FORMULATIONS
To evaluate the training efficiency of different geometry distributions, we first conduct a single- geometry fitting task and then extend our analysis to dataset. We compare method with several alternatives, including the geometry distribution proposed by Zhang et al. (2025), the naive for- mulation in Eq. 2 (w/o Pairs), and the formulation sampling from N(x σ without distribution normalization (w/o DistNorm), proposed in Eq. 3.
For the single-geometry fitting task, train each formulation for 10, 000 iterations and visualize the synthesized points at the final step in Fig. 5. Our method produces the smoothest and most accurate reconstructions, while all other methods generate points that deviate from the surface. The Chamfer distances at the final step are presented in Tab. 2 (Single). The distribution proposed by Zhang et al. (2025) performs the worst because samples from Gaussian noise and requires more iterations to capture the coarse human shape. The "w/o Pairs" formulation also exhibits suboptimal performance as it must learn mappings between distant points, leading to slower convergence. The approach that samples from (X σ)) (w/o DistNorm) achieves the lowest Chamfer distance in this task. With a fixed pose, each sample can focus on learning specified local features, thereby simplifying the optimization. However, as shown in Fig. 5, its reliance dispersed Gaussian centers leads noisy output for some regions. Furthermore, this approach negatively impacts convergence when scaling to datasets with diverse poses, reflected in its degraded performance in Tab. (Dataset).
For dataset-scale evaluation, we train each model on the THuman2 (Yu et al., 2021b) dataset for 30, 000 iterations. Our method consistently achieves significantly higher fidelity compared to the other approaches within the same number of training iterations, as shown Fig. 7. Notably, the results do not represent the final outcome, as training not fully completed. The evolution of Chamfer distance over iterations is provided in the Appendix.
Table 2: Chamfer distance of different distribu- tion formulations.
Table 3: Surface distance comparison between various designs.
Model
Surface Distance
Zhang al.
w/o Pairs
wio DistNorm
Ours
VecSet
0.0018
Single
0.0083
0,0040
0.0020
0.0032
FeatureMap
0.0014
Dataset
0.0101
0.0706
0.0071
0.0032
Ours
0.0012
5.3 POSE DOSDITONDI RANDOM GENERATION
This experiment measures the geometry quality of pose-conditioned random generation. Consis- tent with previous work (Zhang et al., 2023b; 2024d), we utilize the THuman2 Dataset (Yu et al., 2021b) for training and evaluation. We calculate the Fréchet Inception Distance (FID) metrics be- tween the rendered normal images and ground truth normal maps. Notably, some works (Xu et al., 2022; Zhang et al., 2023b; 2024d) enhance their rendering results using information such normal
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Table 4: Comparison of FID scores. The results adopted from E3Gen (Zhang al., 2024d). For some methods, results are rendered directly from their raw geometries, so the numbers both rows are identical.
ENARF*
GNARF"
EVA3D*
E3Gen
GetAvatar
gDNA
Ours
Raw Geometry
223.72
166.62
60.37
65.32
56.07
42.90
16.16
Enhanced Rendering
223.72
166.62
60.37
28.12
22.77
17.43
16.16
E36en
GetAvatur
Enhanced Rendering
Goumetry
Enhanced Rendering
Raw Geornetry
Onis
Enhanced Rendering
Raw Geometry
Geomerry
Raw Goometry
Figure 8: Qualitative comparisons human generation results. Existing methods are displayed with both rendering results and raw geometry outputs, typically organized as two columns each. We include two geometry samples generated by our method for comparison.
field/map or predicted points' rotations. Since our goal compare geometry quality, we primar- ily evaluate these methods based on their raw geometry outputs. To ensure fairness, we also report their performance on their enhanced rendered results. For each subject, we render 50 images from different views and synthesize 25, 000 normal images for the dataset.
We compare our method with representative human generation methods employing different 3D rep- resentations, including Gaussian splatting (E3Gen (Zhang et al., 2024d)), implicit function (GetA- vatar (Feng et al., 2023), gDNA(Xu et al., 2022)), and Nerf (ENARF (Noguchi et al., 2022), GNARF (Bergman et al., 2022) and Eva3D (Hong al., 2022)). As shown in Tab. 4, our method significantly outperforms other approaches in terms of raw geometry outputs. In comparison to the Mfate-oftthe-ar (gDNA), our method improves geometry quality by 57% (42.9 to 16.2). Addition- ally, when compared to the enhanced rendered results from other methods, our raw geometry shows improvements well (7% from 17.4 to 16.2). As shown in Fig. 8, E3Gen (Zhang et al., 2024d) synthesizes unnatural shapes with inconsistent normal colors. GetAvatar (Zhang et al., 2023b) gen- erates cloth wrinkles with unnatural directional patterns, while gDNA (Xu et al., 2022)'s normals produce wrinkles that appear random, unrealistic, and not coordinated with the human pose. In contrast, our method generates realistic and pose-consistent clothing details, demonstrating superior geometric fidelity and detail preservation.
5.4 NOVEL POSE GENERATION
Previous methods (Zhang et al., 2023b; Xu et al., 2022; Zhang et al., 2024d) synthesize 3D humans in canonical space and deform them via rigging, which limits pose-dependent clothing details (e.g., wrinkles and folds). In contrast,
Table 5: User study on quality and physical plausibility.
Metrie
GetAvatar
gDNA
E3Gen
Ours
Quality Plausibility
2.16
2.54
2.12
4.04
2.20
2.66
2.08
4.36
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Figure 9: Generating different poses for given avatar. We showcase challenging cases, including exaggerated poses, skirts, and loose outfits.
our approach directly synthesizes points on the deformed human body given the pose-aware feature map, enabling the generation of pose-dependent clothing details, as shown in Fig. 9. More visual results can be found in the Appendix.
For quantitative comparisons, FID metries reflecting geometric quality are provided in Tab. 4. Note that since prior methods' geometric details remain static across poses, their FID results correspond closely to those reported in the above table. In terms of physical plausibility of geometric details, due the absence of standard metrics, conduct user study to demonstrate that method uniquely enables pose-aware deformations that appear physically reasonable. For each method, we generate 2 identities under poses and ask 25 participants rate geometry quality and physical plausibility (1-5 scale), as shown in Tab. 5.
Our superior physical plausibility mainly stems from our synthesized pose-aware feature maps. Nev- ertheless, even when using a feature map that is incompatible with the generates visually reasonable results, which demonstrates the robustness of our model in handling novel poses (see the Appendix for details).
body pose, our method still
LIMITATIONS
A limitation of our method lies in non-uniform sampling on the target geometry surface, the num- ber of target points x, associated with each SMPL point x/ varies. Although sufficient sampling and removing points that advanced strategies for training pair construction. Besides, similar to other generative methods, our approach is constrained by the diversity of the training datasets. Specifically, our model generalizes well to various body types, as the dataset includes reasonable diversity in this aspect. However, it cannot generate clothing styles that are entirely absent from the training data. Furthermore, the use of UV maps can cause seam artifacts in some randomly generated results due to discontinuity seg- mentation. A better approach would use UV segmentation aligned with real-world garment patches. Since our focus is on a modeling representation, addressing this issue is left for future work.
close mitigate this issue, encourage future work develop more
7 CONCLUSIONS
In conclusion, introduce the generative human geometry distribution, the first method that inte- grates geometry distributions into generative modeling. This distributioonove--diittrbbuuuiiu design is novel, unique, and particularly effective, making our approach the only one capable of producing fine-grained geometry in generative framework. Specifically, using flow matching, we learn flow that transforms from the SMPL template to the target geometry, significantly improving training efficiency. Following this, our method encodes geometry into compact feature maps, facilitating various downstream generative tasks. Experimental results demonstrate that our approach achieves state-of-the-a quantitative performance compared to existing generative methods. Furthermore, our method is able to synthesize fine- ine-grained clothing details that conform to human poses and exhibits strong robustness, generating plausible results even when provided with a feature map mismatched to the target pose. These results highlight the effectiveness our representation and its potential for advancing 3DD human modeling and synthesis.

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8 ACKNOWLEDGEMENTS
This work was supported by funding from King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI, under award number 5940.
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9
APPENDIX
Denoiser
Fenture Map Decompresion (Decoder)
MPBlock
MPCond
Feature map (B
Input
Condition
Inpul
Normalize
PointEmb
Sample
MPCond
MPSILU
MPBleck
X
MPCond
MPBlock
MPCond
MPliineur
MPLinear
MPSiLU
MPLinear
MPBlock
SMPL
MPAdd
map
(a)
(b)
(c)
Figure 10: Details our auto-decoder. (a) We sample noise from a Gaussian distribution (IR3) and generate the flow that transforms from the SMPL. template distribution the human geometry distribution. (b) convolutional network used to interpret and decompress the compact feature map with the SMPL vertex map. (c) The architecture details of model.
9.1 IMPLEMENTATION DETAILS
Our training requires an infinite set of training pairs (x,, X1). In practice, we sample 2 surface points X per human geometry. We observe that many points in loose clothing regions share the same nearest SMPL template location, requiring multiple samples from that location to reconstruct these regions. To alleviate this, we sample a slightly sparser set of 2 points on the SMPL template and apply the k-nearest neighbors (KNN) algorithm to associate each X₃ with the nearest template point. This distributes the mapping workload across different template locations, improving the sampling efficiency for loose clothing regions. During training, randomly sample 30, 000 pairs per iteration.
As shown in Fig. 10, the compact feature map 2T is set R8x24x24 We adopt the linear and convolutional layers from EDM2 (Karras et al., 2024; 2025), using 256 channels in all layers, For optimization, use AdamW (Loshchilov, 2017) with a learning rate of 10-3 following cosine learning rate scheduler across all models. We apply L2 regularization with weight le to the feature map during training.
We train both the auto-decoder and the gen- eration model on the THuman2 dataset (Yu et al., 2021b) for the pose-conditioned gen- eration task, following previous methods (Xu et al., 2022; Zhang et al., 2024d) to ensure fair comparisons. The auto-decoder model is trained for two days and the generation model for one day on a machine with two A100 GPUs.
Original
JV (Xp'.
Ours
Chanfer distance
eato
To enhance the model's ability generalize across diverse poses, we employ the classifier- guidance that randomly drops the pose condi- tion with probability of 0.5. This encourages the network to learn the underlying data distri- bution rather than overfitting to specific poses. For the novel pose generation task, we utilize the 4DDress dataset (Wang et al., 2024), which comprises 64 outfits across 520 human motion sequences, totaling upproximately 50, 000 human geometries with diverse poses and clothing styles. We train the auto-decoder on a machine with 4 A100 GPUs for two days and train the novel pose generation model for one day. We don't utilize the classifier-guddacc strategy on this dataset as its larger scale naturally supports robust training.
3330
10000
13083
20000
23003
steps
Figure 11: The evolution of the Chamfer distance of training different distribution formulations.
The auto-decoder model has 56.02M parameters. On A100 GPU, the feature map decompression takes 81 ms, which executed only once for the entire diffusion process. Each denoising step re- quires 529 for million points, with peak memory usage of 9522 MB. On memory-constraine
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E3Gin GetAvater
EDNA Ours
fixed
Figure 12:
Demonstrations of different methods with fixed geometry features across varying poses. All results show identical geometric details in different poses.
devices, the model can sample multiple times with significantly fewer points per iteration. For in- stance, sampling 10, 000 points takes 71 ms with a peak memory consumption of 1420 MB.
We visualize results by sampling million points for Gaussian Splatting (GS) (Kerbl al., 2023) rendering. Specifically, we render GS's depth maps first, from which we then extract normal maps. All points share the same scaling value, determined by the average minimum distance between points.
Figure 13: Novel pose generation results.
9.2 GEOMETRY DISTRIBUTION FORMULATION
For the experiment of different formulations in training dataset, the evolution of the Chamfer distance across 30, 000 iterations is illustrated in Fig. 11. Our proposed human geometry distribution achieves superior reconstruction quality, with consistent decrease in the Chamfer distance.
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GT
Ours
GT
Ours
GT
Ours
GT
Ours
Figure 15: This figure shows challenging examples with loose clothing and hair. Our point-based representation may exhibit small local sparsity in extreme cases, but nevertheless, we maintain high geometric quality overall. To demonstrate this, we apply Poisson surface reconstruction to our results, shown on the right.
9.3 NOVEL POSE GENERATION
Previous methods (Zhang et al., 2023b; Xu et al., 2022; Zhang et al., 2024d) synthesize 3D humans in canonical space and obtain novel pose appearances via deformation. Their lim- itations illustrated in Fig- 12, where deform avatars to different poses, resulting in identical clothing details across all poses. In the main paper, we have showcased that our method can generate pose-aware deformations by synthesizing
cose-compatible feature map. Fig. 12 demonstrates that, although the wrinkle details may not be entirely natural, our method still produces reasonable results even when us- ing incompatible feature map.
Ours
This figure shows the results of our method after full training, using the same identi- ties and poses in Fig. 7.
Figure 14:
Furthermore, we present additional novel pose generation results in Fig. 13. Specifically, we showcase challenging cases featuring intricate poses and complex clothing deformations, such as loose garments and skirts, which significantly deviate from the SMPL surface.
9.4 DISCUSSION ON OUR REPRESENTATION CAPABILITY
Although our representation built upon the SMPL template, it differs fundamentally from prior methods whose generated geometries strictly confined by SMPL. In contrast, representation models the actual target geometry distribution, without being limited by the SMPL template. As illustrated in Fig. 15 and Fig. 16, our method successfully captures loose clothing and hair diir-details that cannot be defined or represented by the SMPL model.
Our representation naturally generalizes different body shapes and genders, it models the dis- tribution of displacements between the SMPL template and the target geometry, as illustrated in Fig. 17.
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Ours
GT
Ours
GT
Ours
GT
Figure 16: This figure illustrates large deformations of loose clothing under extreme poses.
Original
Less roundness
More roundness
Extreme roundness
Change gender
Figure 17: Given the same feature map, the figure shows the generated results for different SMPL body shapes and genders. Since the underlying identity feature map remains unchanged, the gender differences mainly appear as subtle variations in body shape.
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Figure 18: The figure shows interpolation results between the leftmost and rightmost identities; note how new clothing wrinkles, styles, and hairstyles naturally emerge during the transition.
9.5 IDENTITIES/GARMENTS INTERPOLATION
In addition to training another generative network, our method also enables the generation of novel clothing and identities through latent-space interpolation, as shown in Fig 18 and Fig. 19.
9.6 USE OF LARGE LANGUAGE MODELS
During the preparation of this manuscript, we employed large language model (LLM) sist with grammar checking and minor language refine- ments. All scientific content, ideas, and claims were con- ceived and written by the authors. The final version of the manuscript was carefully reviewed, modified, and ap- proved by the authors to ensure accuracy and clarity.
Hand
auxiliary tool to as-
Upper body
Pace
Figure 20: UV parameterization of the SMPL template.
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Figure 19: Interpolations. This figure presents interpolations between identities wearing outerwear.
Figure 21: Given our high-quality geometries, textures can be synthesized in various ways. Here, we first generate multi-view images with a depth-conditioned video generation model (Wan2.1), and then optimize point colors via Gaussian Splatting.
20


Reviews:
[{"review_id": "m89O91b5tp", "reviewer": "Reviewer_P4Gu", "summary": "This paper proposes to learn the generative human geometry distribution from 3D human dataset, which can further be applied for random human generation and animation. At the core of the method is a diffusion model which is conditioned on the UV space of SMPL model to predict the geometry distribution. The authors conducted experiments on different dataset to verify the effectiveness of the proposed method.", "questions": "The method utilizes SMPL model. How does this handle loose clothing?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper is easy to follow.\n\nGenerating 3D humans is an interesting task with practical applications.\n\nThe method is technically sound by leveraging a UV-conditioned diffusion model to learn the geometry distribution of humans.", "weaknesses": "Insufficient experiments. The paper only evaluates the method on studio 3D data, and does not illustrate whether the proposed method can generate out-of-domain 3D humans. The paper does not answer the question of whether the method overfits to the training dataset, and whether it can sample humans with novel clothing styles. Only random sampling and novel pose generation are shown. \n\nIn addition, in Fig. 4, the authors claim that the pipeline supports multiple condition,s such as images/texts, which, however, are not illustrated in the paper. Can the method generate 3D humans given single in-the-wild images, or generate text-conditioned humans like joint2human?\n\n\nExisting methods can learn 3D human generations from 2D data such as images and videos, including GAN-based (e.g., EVA) and diffusion-based method (e.g., Primitive Diffusion [Zhao et al. NeurIPS, StructLDM), or generate both geometry and textures using 3D Gaussians in a feed-forward way, such as LHM, AniGS. Compared with these work, what are the advantages of the proposed method which require 3D ground truth data in training and can only generate 3D geometry?\n\nThis paper (video demo) claims that it is the first method that learn the human geometry distribution of a dataset. However, this statement is not true. Previous 3D human generation methods such as GDNA [Xu et al 2022] for geometry reconstruction, or EVA/PrimDiff for both geometry and texture reconstruction all learn the distribution of human geometry.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HKa0TZ7Acy", "reviewer": "Reviewer_epmJ", "summary": "Building upon the method Geometry Distribution, this paper introduces a human generation method via a flow-based diffusion model. To enable efficient training over the existing human datasets, the authors first propose to encode the human geometry distribution into a feature map. Leveraging the pre-trained feature map, the authors further adopt the SMPL model distribution as the learning base while refining the flow velocity field for more efficient training. The paper also introduce an efficient strategy for constructing training pairs, further improving the generated quality. Extensive experiments have been conducted to demonstrate the performance of the proposed method across various tasks.", "questions": "1. In Figure 7, we could still observe some artifacts in \"Ours\", especially for the frontal view. However, in Figure 6, it's presented that the proposed strategy for constructing training pairs could address (or actually minimize) this issue. THe reviewer would like to ask the potential and actual factors that lead to this problem, and also the possible solutions.\n\n2. Considering the method employs the SMPL model as the base, will that be possible to generate different shapes (tall, short, fat, thin) or even genders for a human subject?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The strengths of the method can be summarized as:\n\n+ The adoption of geometry distributions and the introduction of feature map \"representation\" is novel and demonstrated to be useful.\n\n+ Both the qualitative and quantitative evaluations are thorough and demonstrate the performance of the proposed method, establishing a new state-of-the-art.\n\n+ The paper is well-written and easy to follow.", "weaknesses": "The reviewer would like to point out several weaknesses of the paper:\n\n- The method may still inherit some limitations from the SMPL template, such as the modeling of fine details like hair, accessories, hats, etc.\n\n- Based on the first weakness, the reviewer would like to see the rotating results of loose clothing, complex poses.\n\n- Considering the method is trained on THUman2 dataset, it might be beneficial to compare with the 3D reconstruction methods (like PIFu, ECON, ICON) in terms of both qualitative and quantitative quality to further demonstrate the method or present the gap. Additionally, more metrics like P2S and chamfer distance may be useful to help demonstrate the 3D quality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vKeFI1xRek", "reviewer": "Reviewer_2RbH", "summary": "This paper has proposed a novel 3D human generative model built on top of Geometric Distribution. Specifically, it learns a conditional flow model from the SMPL parameter to the loose cloth real geometric distribution. Some training tricks (pair sampling, distribution normalization, and auto-decoder) are proposed to improve the performance. Extensive experiments have demonstrated the effectiveness of the proposed method.", "questions": "1. If autodecoder is used, when new data comes, does it mean that this proposed method cannot handle this case? Or it requires some test-time optimization, which is unscalable.\n2. The UV-space diffusion + autodecoder pipeline for 3d avatar generation is very relevant to Gaussian3Diff (ECCV 24), and should be discussed in the related work.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The method is novel, and the problem is worth exploring.\n2. The proposed training tricks are reasonable and effective.\n3. The final quality is surprisingly good.", "weaknesses": "1. Since some baselines (Like EVA3D) also model the texture distribution, directly comparing the geometry with it is somewhat unfair. But this is not a big issue.\n2. Some of the proposed training tricks are actually already well established in the previous works. E.g., the SMPL -> final distribution idea is similar to the diffusion bridge, and the DistNorm is very popular in the current diffusion model training (vae rescaling). \n3. The proposed method has not demonstrated the performance on the animation / temporal consistency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "R6DQYEkU1l", "reviewer": "Reviewer_aJPB", "summary": "This paper addresses the challenge of 3D human geometry generation by proposing a new geometry distribution model that extends this representation from single characters to large-scale dataset learning.  \n\nSpecifically, its proposed representation incorporates two key designs:  \n(1) Encode geometry distributions as 2D feature maps instead of network parameters to enable dataset-level generalization.  \n(2) Replace the Gaussian source distribution with the SMPL human template (plus refined flow velocity fields) to align the source closer to the target geometry distribution.\n\nBased on this representation, this paper also proposes a two-stage generative framework:  \n(1) Compress each human geometry distribution into a compact 2D feature map using a flow model, from which a high-fidelity human geometry can be sampled through a denoising process.  \n(2) Train a second flow model on the latent feature space to support generative tasks.  \n\nThe method is evaluated on two tasks: pose-conditioned random avatar generation (THuman2 dataset) and avatar-consistent novel pose synthesis (4DDress dataset). Quantitative results show a 57% improvement in geometry quality and a 7% improvement in visual appearance compared to SOTA.", "questions": "1. I'm kind of uncertain about the application scenario. This method is built upon you already have the SMPL parameters, right? If SMPL can already provide human geometry information, what's the role of this proposed method? Is it more like a refiner?\n\n2. I'm a little confused about the decoding process. Looks like you're optimizing the flow model $\\theta$ and the condition (i.e. the 2D feature map $\\textbf{z}_{T|S}$) simultaneously. Will the training process be stable since I recall that normally in diffusion training, the condition should be fixed, right?\n\n3. I'm wondering do you have a mathematical analysis about why recovering target geometry by conditioning on SMPL can be modeled as a 2D feature map? What does each pixel in the feature map mean? Maybe the author should give more introduction or preliminary knowledge on the 2D feature map representation of 3D objects like SMPL.\n\n4. I'm curious about the influence of feature map size. The paper fixes the feature map to 8×24×24. Are 576 embeddings too few to encode 50,000 points?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The technical designs in the proposed geometry distribution are solid:  \n(1) Replacing Gaussian with SMPL (a human-specific template) reduces the \"distance\" between source and target distributions, minimizing the need to learn extraneous flow paths. This, combined with training pair construction (matching target points to nearest SMPL points + Gaussian perturbation) and distribution normalization (zero-centered Gaussian for source, dense displacement fields for target), solves spatial imbalance in training and accelerates convergence.  \n(2) Unlike prior geometry distributions that store shape information in network weights, encoding into 2D feature maps enables efficient dataset-level learning. Plus, the idea of using the decompressed feature map to serve as a condition to generate the final geometry distribution makes sense to me.\n\n2. The quantitative and qualitative experiments are comprehensive, showcasing the effectiveness of the proposed method as well as each design component.", "weaknesses": "Please refer to the question section.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)