Title: SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D Human Reconstruction

Diffusion Model

Generation Model
Abstract: Photorealistic 3D full-body human reconstruction from a single image is a critical yet challenging task for applications in films and video games due to inherent ambiguities and severe self-occlusions. While recent approaches leverage SMPL estimation and SMPL-conditioned image generative models to hallucinate novel views, they suffer from inaccurate 3D priors estimated from SMPL meshes and have difficulty in handling difficult human poses and reconstructing fine details.In this paper, we propose SyncHuman, a novel framework that combines 2D multiview generative model and 3D native generative model for the first time, enabling high-quality clothed human mesh reconstruction from single-view images even under challenging human poses.Multiview generative model excels at capturing fine 2D details but struggles with structural consistency, whereas 3D native generative model generates coarse yet structurally consistent 3D shapes. By integrating the complementary strengths of these two approaches, we develop a more effective generation framework. Specifically, we first jointly fine-tune the multiview generative model and the 3D native generative model with proposed pixel-aligned 2D-3D synchronization attention to produce geometrically aligned 3D shapes and 2D multiview images. To further improve details, we introduce a feature injection mechanism that lifts fine details from 2D multiview images onto the aligned 3D shapes, enabling accurate and high-fidelity reconstruction.Extensive experiments demonstrate that SyncHuman achieves robust and photorealistic 3D human reconstruction, even for images with challenging poses. Our method outperforms baseline methods in geometric accuracy and visual fidelity, demonstrating a promising direction for future 3D generation models.

SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction

Abstract

Photorealistic 3D full-body human reconstruction from a single image is a critical yet challenging task for applications in films and video games due to inherent ambiguities and severe self-occlusions. While recent approaches leverage SMPL estimation and SMPL-conditioned image generative models to hallucinate novel views, they suffer from inaccurate 3D priors estimated from SMPL meshes and have difficulty in handling difficult human poses and reconstructing fine details. In this paper, we propose SyncHuman, a novel framework that combines 2D multiview generative model and 3D native generative model for the first time, enabling high-quality clothed human mesh reconstruction from single-view images even under challenging human poses. Multiview generative model excels at capturing fine 2D details but struggles with structural consistency, whereas 3D native generative model generates coarse yet structurally consistent 3D shapes. By

integrating the complementary strengths of these two approaches, we develop a more effective generation framework. Specifically, we first jointly fine-tune the multiview generative model and the 3D native generative model with proposed pixel-aligned 2D-3D synchronization attention to produce geometrically aligned 3D shapes and 2D multiview images. To further improve details, we introduce a feature injection mechanism that lifts fine details from 2D multiview images onto the aligned 3D shapes, enabling accurate and high-fidelity reconstruction. Extensive experiments demonstrate that SyncHuman achieves robust and photorealistic 3D human reconstruction, even for images with challenging poses. Our method outperforms baseline methods in geometric accuracy and visual fidelity, demonstrating a promising direction for future 3D generation models.

1 Introduction

Reconstructing 3D clothed humans from a single RGB image is a fundamental yet challenging task. It has broad applications in AR/VR, virtual try-on, gaming, and film production [33, 36]. Compared to parametric body reconstruction [54], clothed human reconstruction [83] requires capturing not only the underlying body shape but also the diverse topology, geometry, and dynamics of garments.

Recent progress in implicit representations and generative models has led to significant advances in this area. PIFu [45] pioneered this direction with predicted neural implicit field, followed by methods such as ICON [63], ECON [62], and PaMIR [82], which introduced improvements in SMPL priors, normal estimation, and feature representation, respectively. With the advancement of generative models techniques [30, 31, 24], recent works [13, 78, 25] have introduced multiview generative model for novel-view human image prediction, enhancing 3D reconstruction fidelity, detail preservation, and robustness.

However, accurately reconstructing 3D clothed humans from a single 2D image is still challenging, especially for images with challenging poses. The reason is that most methods [13, 25] strongly rely on human shape priors, i.e., SMPL estimation, to provide structural information to generate multiview images. Unfortunately, existing single-view human pose estimation methods [7, 72, 1, 39, 3] often lack sufficient accuracy, especially when dealing with occlusions or challenging poses, as shown in Fig. 2 (a). Moreover, the estimated SMPL meshes represent only naked human bodies and fail to accurately model loose clothing. Thus, conditioned on inaccurate SMPL meshes, the multiview generative models often generate images with incorrect body topologies and mismatched details, leading to reconstruction artifacts, as shown in Fig. 2 (b).

An alternative approach employs native 3D generative models [73, 61, 27, 79] to generate the 3D human shapes directly. However, these methods often produce results lacking in detail and fidelity. By training on large-scale 3D datasets, recent 3D native generation methods [61, 79, 27] demonstrate improved capability for constructing 3D human meshes from single-view images, even for challenging human poses, as shown in Fig. 2 (c). However, these 3D native generation methods typically generate only coarse, low-fidelity shapes that poorly match the input image characteristics. Enhancing both the geometric detail and input-consistency of 3D-native generation outputs remains an open challenge.

In this paper, we propose SyncHuman , a novel framework that combines 2D multiview generative model and native 3D generative model for the first time, leveraging their complementary strengths to address these challenges, as shown in Fig. 2 (d). Instead of simply relying on the SMPL estimation, we utilize the more accurate

Input

(a) SMPL

(b) PSHuman

(c) Trellis

(d) Ours

3D shapes generated by the native 3D generative model to guide the generation of 2D multiview images, greatly improving the multiview consistency. At the same time, the detailed multiview images also guide the 3D generative model to carve the 3D shapes with detail and high fidelity.

In implementation, SyncHuman consists of two main components. First, we design a unified 2D-3D cross-space generative model with two branches, i.e., a 2D multiview generation branch and a 3D sparse structure generation branch, which interact via 2D-3D synchronization attention layers. The 2D-3D attention layers align the multiview images with the generated 3D shapes, which simultaneously utilize the 3D shapes to improve the cross-view consistency and employ the multiview images to enhance the fidelity of the generated 3D shapes. Next, to obtain high-quality 3D meshes, we design a multiview guided decoder to incorporate the pixel-aligned information of generated multiview images into the 3D generation during the decoding process, which not only carves fine geometric detail but also greatly improves the texture fidelity.

We conduct extensive experiments on multiple datasets to evaluate the effectiveness of SyncHuman. The results demonstrate that the proposed method outperforms previous single-view human reconstruction methods [62, 13, 25] while even achieving higher fidelity and texture quality than the large-scale 3D generative models [61] trained with datasets hundreds of times larger than ours. SyncHuman unifies 2D multiview generative model and native 3D generative model within a unified framework, enabling higher-quality image-to-3D generation with improved fidelity. This demonstrates significant potential for future 3D generation model development.

2 Related works

Single image human reconstruction. Prior to the advent of generative approaches, singleimage human reconstruction primarily followed either explicit or implicit representation paradigms. Explicit methods, including voxel-based techniques [55, 83], visual hull approaches [34], and depth/normal [8, 50, 10] prediction frameworks, offer computational efficiency but often sacrifice local geometric details. The explicit normal integration in ECON [62] made a significant advancement in reconstruction robustness for the explicit paradigm. In contrast, implicit methods emerged as the dominant approach due to their continuous representation capabilities. The field was revolutionized by PIFu [45, 6, 67], which established pixel-aligned implicit functions for detailed geometry recovery from single images. Subsequent approaches enhance the robustness [11, 63, 82, 68] through parametric body model integration and additional supervision from surface normals [46] and depth information [70, 81]. Most recent works [77, 78, 18, 69, 42, 41] incorporate transformer architecture and utilize large-scale human datasets to reduce inductive bias, enhancing the generalization capability. While these methods demonstrate exceptional performance in handling complex clothing and topological variations, they remain inherently constrained by their reliance on the input image, struggling with photorealistic appearance and detail recovery.

3D Generation. 3D generation has been significantly advanced by generative models, which can be roughly categorized into multiview generation approaches and native 3D generation methods. Multiview generation techniques [49, 31, 30, 24, 25, 17, 56, 85, 52, 65, 64, 57, 59, 53] typically employ a two-stage pipeline: first generating consistent multiview images, followed by either optimization-based reconstruction [37] or feed-forward generation [22, 23]. The multiview generation stages involve fine-tuning an image generative model [43] or video generative models [2] by incorporating view-aware attention layers to ensure cross-view consistency. Native 3D generative models [80, 61, 27, 79, 5, 71, 60, 26] operate directly in 3D representation spaces (e.g., 3D Volume [61] or Signed Distance Field [38]), typically comprising a large variational autoencoder combined with a latent diffusion transformer (DiT) [40]. Trained on extensive 3D datasets, these models demonstrate exceptional geometric quality and strong generalization capabilities. Building upon these foundations, our work adapts and fine-tunes such a native 3D generator specifically for human body shape while preserving its generalization capacity.

Generative Human Reconstruction Generative models, such as Stable Diffusion, have emerged as a powerful tool for 3D human reconstruction. Pioneering works [28, 16, 15] employ score distillation sample (SDS) to optimize textured human mesh per case, which is time-consuming and typically only text-constrained. Feed-forward methods [13, 4] leverage pose-guided ControlNet [74] to predict plausible back views for neural reconstruction or Gaussian splatting, but their robustness suffers from

Multiview

Generative

Model

Enc

Dec

Structure

Generative

Model

Multiview Guided Decoder(MVGD)

Condition

2D-3D Cross-Space Generative Model

Trainable Module

Structured

Latents

Dec

Dinov2

Structured Latents

Freezed Module

Textured

Mesh

Noise Latent

pixel-wise

injection

Dec

2D-3Dsynchronization

IMV

attention

Dvoxel

Vgrid

M

D

0

limited multiview cues. Other approaches [20, 25] address this problem by fine-tuning 2D generative models to produce sparse multiview human generations. Despite improved performance, these models struggle with cross-view consistency, leading to inevitable appearance artifacts. Human3Diff [66] attempts to enhance multiview coherence by integrating 3D representations as intermediate constraints during the denoising process. However, reliance on 2D denoising generative models often leads to anatomically implausible human structures due to the absence of body prior. Unlike prior work, this study aims to align the pretrained 2D multiview and 3D native generative models, enabling producing geometrically consistent and robust 3D human models without reliance on any human prior.

3 Method

Overview. SyncHuman aims to reconstruct a 3D clothed human mesh from a single color image. As shown in Fig. 3, given a full-body human image, we first propose a 2D-3D Cross-Space generative model (Section 3.1) to synthesize multiview color and normal maps, along with an aligned sparse 3D voxel grid, which is further transformed to an aligned structured latent through a pretrained flow transformer. Then, a Multiview Guided Decoder (Section 3.2) is introduced to decode the structured latents into a high-quality, detailed, textured mesh with the help of generated multiview images.

3.1 2D-3D Cross-Space Generative Model

Multiview generative models have shown powerful novel-view generation and generalization capability. Given a human image as input, they could hallucinate multiple views with high-resolution details such as identity, skin texture, and clothing wrinkles, but often struggle with cross-view consistency. In contrast, native 3D generative models naturally maintain 3D structural consistency, yet typically lack fidelity. In this section, we introduce 2D-3D Cross-Space Generative Model, which combines the strengths of 2D multiview generative models and native 3D generative models.

Multiview Generative Model. Taking the input image I as the front view, we use the network structure from PSHuman [25] to generate color and normal maps on four predefined orthogonal viewpoints, front, back, left, and right, which employs an efficient row-wise multiview attention to enhance cross-view consistency. This module could be formulated as

where I MV is the generated multiview images and normal maps. Previous methods [25, 78, 13] usually use the estimated 3D SMPL meshes to improve the multiview consistency in I MV, but often

2D to 3D attention

q

k,v

k,v

q

3D to 2D attention

suffer from inaccurate SMPL estimation. Thus, we introduce the native 3D generative model to provide 3D structural guidance for the multiview generation in the following.

3D structure Generative Model. Our native 3D generative model follows Trellis [61]. A 3D noise grid is first used to produce a sparse structure latent through a DiT-based flow transformer G grid. The sparse structure latent is subsequently decoded into an occupied voxel grid V grid via a Conv-based decoder D voxel ,

where the input image I is fed into the generative model layer by cross-attention layers. The sparse structure generated by Trellis [61] produces reasonable 3D shapes but loses fidelity and details. We add a novel 2D-3D synchronization attention to improve the fidelity and retrieve more details from multiview images when transforming the 3D structure to textured meshes.

2D-3D synchronization attention. We introduce a 2D-3D synchronization attention mechanism between the 2D multiview generative model and the 3D generative model to let them benefit each other in the generation. This consists of 2D to 3D attention and 3D to 2D attention layers as follows.

(1) 2D to 3D attention. As shown in Fig. 4, for each 3D voxel feature, we first sample the corresponding 2D features on generated four normal maps. Then, the 3D voxel feature is used as the query token, and the concatenated 2D features from four views serve as keys and values for cross-attention. The cross-attended features are processed by an output MLP with zero initialization, and the resulting features are added to the original 3D voxel feature for refinement.

(2) 3D to 2D attention. Then, for each 2D feature on multiview images, we query the corresponding 3D voxel columns as in Fig. 4. Then, the 2D feature is used as the query while the 3D voxel feature serves as keys and values for cross-attention. The cross-attended features are processed by an output layer with zero initialization, and the results are added to the 2D features.

Discussion. Our method establishes an explicit correspondence between the 2D and 3D generative models, which benefits each branch. Through this synchronized attention, 3D generative model provides 3D structural guidance for the 2D generative model to improve the multiview consistency while the 2D generative model regularizes the 3D generative model to generate shapes that are more aligned with the input image with better fidelity. This integration enables our model to combine the advantages of both approaches: the 2D generative model provides detailed, high-fidelity results, while the 3D generative model ensures structural integrity and robust handling of complex human poses.

2D-3D joint training. We employ the flow matching [29] objective to train our 2D-3D cross-space generative model with the training loss defined by

where ϵ 2 d and ϵ 3 d is the 2D noise maps and 3D noise grid, x 2 d t and x 3 d t is the latent features at timestep t and v 2 d θ and v 3 d θ are the corresponding predicted velocity during denoising process, respectively. Note that the multiview generative model is based on the Stable Diffusion 2.1 [44], and we retarget it to the same flow matching model as Trellis for jointly training.

3.2 Multiview Guided Decoder (MVGD)

This section utilizes the generated multiview images and sparse voxels to recover textured 3D meshes.

Structured latent generation. We first apply another DiT-based generative model G latent in Trellis [61], which is named as Structured Latents Generative Model in Fig. 3, to generate a set of structured latents V latent. Each of them is attached to a previously generated 3D voxel. These structured latents can be processed by either a mesh decoder D m or a 3D Gaussian Splatting [19] (3DGS) decoder D gs to generate a mesh or a 3DGS representation. For simplicity, we unify these decoders as D o . However, directly decoding these latent to mesh or 3DGS leads to a lack of reconstruction details, particularly noticeable in areas such as the face and clothing wrinkles, as demonstrated in Fig. 8. To address this, we propose a multiview feature injection mechanism to incorporate the generated high-resolution multiview images into the original decoder.

Multiview feature injection. Specifically, we extract DINOV2 [35] features of generated multiview images, and process them with several trainable MLP layers. For each 3D voxel, we query the corresponding four-view image features and concatenate them with the generated structure latent. The concatenated features are first passed through a MLP, and the resulting representations are subsequently fed into the original decoder D o to produce a high-quality mesh and 3DGS representation. This simple but efficient feature injection allows for preserving the geometry fidelity and appearance realism to a great extent, as shown in Fig. 8.We render images from 3DGS and then bake onto the mesh to obtain the final textured human mesh M . The overall decoding process can be formulated as

Training Loss. We train the multiview guided decoder for the 3DGS branch and the mesh branch separately. For the 3DGS branch, we use L1 loss, Structural Similarity Index (SSIM), Learned Perceptual Image Patch Similarity (LPIPS) loss between renderings and ground-truth images, and a regularization loss to avoid extremely large or small opacity. For the mesh branch, we render the foreground mask, depth maps, and normal maps from the generated 3D meshes. Then, we compute the L1 or Huber loss between the ground truth and the renderings to train the decoder. More architectural design and training details are given in the supplementary material.

4 Experiments

4.1 Experiment Setup

Dataset. Our models are trained on several widely used 3D human scanning datasets, including THuman2.1 [70], CustomHumans [12], THuman3.0 [51], and 2K2K [10]. To construct training images, we render 8 ground-truth images using orthographic cameras with evenly distributed azimuth angles and a fixed 0 ◦ elevation with a resolution of 768 × 768 . For quantitative evaluation, we utilize 100 scans from X-Humans [47] and 150 scans from CAPE [32]. X-Humans contains 233 sequences of high-quality textured scans from 20 participants. We randomly selected 5 textured scans from each of the 20 participants in the X-Humans dataset, resulting in 100 test samples. Following ICON's partitioning criteria, we subdivide CAPE into "CAPE-FP" (50 samples) and "CAPE-NFP" (100 samples) to test the generalization ability in real-world examples. We conduct comparison with the baseline methods on the aforementioned X-Humans subset and CAPE subset, and perform ablation experiments on the same X-Humans subset.

Metric. To evaluate reconstruction capability, we employ three primary metrics: 1-directional point-to-surface ( P2S ), L 1 Chamfer Distance ( CD ), and Normal Consistency ( NC ). For geometry evaluation, we align the centers of the reconstructed mesh and the ground truth mesh and then scale them so that the coordinate range of the longest axis is 1. For appearance evaluation, we render front, back, left, and right views and compute PSNR [58], structural similarity index ( SSIM ) [75], and perceptual image patch similarity ( LPIPS ) [76].

Ours

PSHuman

SIFU

Human3Diff

ECON

ECON

Human3Diff

SIFU

PSHuman

Ours

Input

GTA

Human3Diff

SIFU

PSHuman

Ours

Input

MM

IMN

Method

CAPE-NFP

CAPE-FP

X-Humans

↓

P2S

↓

NC

↑

↓

P2S

↓

NC

↑

↓

P2S

↓

NC

↑

PSNR

↑

SSIM

↑

LPIPS

↓

ICON [63]

1.5966

1.4171

0.7974

1.2698

1.2018

0.8330

1.4971

1.3920

0.8133

-

-

-

ECON [62]

1.8335

1.5391

0.7731

1.3729

1.2962

0.8225

1.6425

1.4398

0.8054

-

-

-

GTA [77]

1.6311

1.5053

0.7890

1.2980

1.2457

0.8277

1.5050

1.4662

0.8044

20.0084

0.8502

0.1129

SIFU [78]

1.6573

1.5130

0.7895

1.2759

1.2275

0.8289

1.5391

1.4331

0.8093

20.6747

0.8455

0.1104

SiTH [13]

1.6461

1.2043

0.7914

1.0377

0.9767

0.8516

1.5104

1.4345

0.7972

19.8245

0.8204

0.1182

Human3Diff [66]

1.5991

1.2016

0.7427

0.9666

0.9340

0.7914

1.5034

1.4219

0.7468

19.7181

0.8065

0.1334

PSHuman [25]

1.3726

0.9863

0.8276

0.7764

0.6527

0.8850

1.4377

1.1385

0.8393

20.8405

0.8523

0.0980

TRELLIS [61]

2.0877

1.5678

0.7521

1.1155

1.0663

0.8353

2.0043

1.5053

0.7718

17.0786

0.7238

0.1529

OURS

0.9127

0.8113

0.8483

0.6409

0.5962

0.8958

0.8353

0.7593

0.8872

21.8385

0.8741

0.0786

Trellis

Trellis(tuned)

PSHuman

Ours

4.2 Comparison with baseline methods

Baselines. We conducted a comprehensive comparison of our method against state-of-the-art singleview human reconstruction approaches, including classic implicit function based methods (ICON [63], GTA [77], SiFU [78]), explicit work (ECON [62]), and other baselines with generative priors ( SiTH [13], Human3Diff [66] and PSHuman [25]). All evaluations are conducted with the official open-source codes, applying a unified evaluation method. More comparisons and visual results are provided in the Appendix.

Comparison of geometry quality. Combining the advantages of accurate 3D coarse structure from the native 3D generative model and the rich details of the multiview generative model, our method outperforms existing approaches in geometry quality as shown in Tab. 1. The qualitative comparison in Fig. 5 highlights that our method also handles complex human poses correctly, demonstrating significant improvements in structural integrity, correctness, and detail richness over baseline methods.

Comparison of appearance quality. We render four views with resolution of 768 for each sample and evaluate the appearance quality by reporting average PSNR, SSIM, and LPIPS. The results presented in Tab. 1 demonstrate that our method significantly outperforms existing approaches on all metrics. As illustrated by the qualitative results in Fig. 6, our method generates high-quality appearances on novel viewpoints, delivering natural and photorealistic reconstruction quality. In contrast, existing methods exhibit notable limitations in both unseen views and occluded regions, including blurred colors and artifacts.

original decoder

original decoder(tuned)

MVGD

Ours

4.3 Ablation Study

2D-3D cross-space generative model. We ablate the effectiveness of 2D-3D cross-space generative model on a X-Humans subset by removing the 2D-3D synchronization attention. For a fair comparison, we fine-tuned all the models on the same dataset. Compared with PSHuman (2D multiview generative model + remeshing) and Trellis (a native 3D generation model), this cross-space attention significantly enhances geometric accuracy and texture fidelity, as shown in Tab. 2 and Fig. 7.

Multiview guided decoder (MVGD). To evaluate the effect of MVGD, we compare three types of structured latent decoders: (1) the original Trellis decoder, (2) the Trellis decoder fine-tuned on human scans, and (3) the decoder guided with multiview images (our MVGD).

We conduct the comparison on the same X-Humans subset, evaluating both geometry and appearance. We apply mesh normalization and ICP registration to align the output meshes with the round truth scans to ensure a fair comparison. For each mesh, we render four views with a 768 resolution and report the average PSNR, SSIM, and LPIPS. The results in Tab. 3 show that multiview guided decoding significantly enhances geometric accuracy and texture quality. Fig. 8 also clearly illustrates that incorporating multiview image information improves the details and fidelity.

Comparison between our structure and SMPL estimation. To demonstrate that our structure handles some complex human poses better than the SMPL estimation, Fig. 9 shows the SMPL estimation from 4D-Humans [9] and the 3D structure generated by our method given the same input image. The SMPL estimation has obvious errors like self-intersection, while the structure generated by our method aligns better with the inputs without artifacts.

Input

SMPL

Our Structure

5 Limitation and Conclusion

In this work, we propose SyncHuman, a novel framework for robust 3D human generation from a single image. By introducing a 2D-3D crossspace generative model, we generate high-fidelity 3D structures and cross-view consistent multiview images. Then, we employ a multiview guided decoder to obtain detailed and structurally completed 3D human textured meshes. Extensive experiments

Input

Generated

demonstrate that SyncHuman can generate 3D humans with intricate geometric details and lifelike appearances, outperforming existing methods.

Limitations. Our method inherits certain constraints from the training data. First, since our training dataset is rendered with uniform light source, reconstructed textures may exhibit artifacts under extreme lighting conditions (e.g., localized overexposure or shadows, as shown in Fig. 10) Moreover, our multiview generation model is fine-tuned from SD 2.1 using only ∼ 5,000 human scans, so its generation quality is still constrained. It will be promising to scale up our model using video generative models or large-scale multiview human datasets in future work.

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A Details

A.1 Training Details

2D-3D Cross-Space Generative Model. Our 2D-3D Cross-Space generative model was trained on 8 NVIDIA H800 GPUs. For the multiview generative model branch, we adopt the architecture of PSHuman [25] but retrain it using flow matching from the open-source pre-trained text-to-image generation model, SD2.1-unclip [43]. We train the multiview generation branch separately with a batch size of 32 for a total of 30,000 iterations. We adopt an adaptive learning rate schedule, initializing the learning rate at 1e-4 and decreasing it to 5e-5 after 2,000 steps. For 2D-3D Cross-Space generative model, we initialize the network weights using: the fine-tuned weights from our multiview generation branch (as described above), a pre-trained image-to-3D model (Trellis [61]). Additionally, we perform zero-initialization on the output layer of the 2D-3D synchronization attention module. We train the 2D-3D Cross-Space generative model with a batch size of 32 for a total of 50,000 iterations. We adopt an adaptive learning rate schedule, initializing the learning rate at 2.5e-5 and decreasing it to 1.25e-5 after 2,000 steps. To enable class-free guidance (CFG) [14] during inference, we randomly omit the image condition at a rate of 0.05 during training.

Multiview Guided Decoder. Our Multiview Guided Decoder was trained on 1 NVIDIA H800 GPU. We train the decoder with a batch size of 4 for a total of 14,000 iterations, using a learning rate of 1e-4.

The loss design largely adheres to Trellis [61]'s setup. For the GS decoder, the loss includes reconstruction loss and regularization loss, with regularizations employed for the volume and opacity of the Gaussians to prevent their degeneration, specifically to avoid them becoming excessively large or transparent. L recon is composed of L 1 (L1 loss), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The full training objective is defined as follows:

where L is the total number of active voxels.For each active voxel, K Gaussians are predicted, s and α are the scale and opacity of Gaussian, respectively.

For the mesh decoder, we utilize Nvdiffrast [21] to render the extracted mesh along with its attributes, producing a foreground mask M , a depth map D , a normal map N m directly derived from the mesh, an RGB image C , and a normal map N from the predicted normals, a normal map N front m directly derived from the mesh from the front view, an RGB image C front from the front view. The training objective is then defined as follows:

where:

where L geo and L color are written as:

Here, L recon is defined identically to Eq. (7). Finally, L reg consists of three terms:

where L consist penalizes the variance of attributes associated with the same voxel vertex, L dev is a regularization.

A.2 Detailed Network Structure

2D-3D synchronization attention. In the 3D branch, we inserted two 2D-to-3D attention blocks after the 8th and 16th transformer blocks respectively. Similarly, for the 2D branch, we added two 3D-to-2D attention blocks following the 3rd CrossAttnDownBlockMV2D and the UpBlock2D modules.

(1)2D-to-3D attention. Each 3D voxel feature u i ∈ R d u with coordinates ( x i , y i , z i ) is orthographically projected onto four view normal map planes (front, back, left, right) to obtain corresponding 2D pixel features:

where p v i ∈ R d p is the projected 2D features, and π v ( · ) is the orthogonal projection function.

The 3D voxel feature u i and 2D pixel feature p v i are respectively passed through the MLP transformation:

Using 3D voxel feature as queries and concatenating four 2D pixel features along the sequence dimension as keys and values, compute the cross-attention:

Here, u ′ i represents the updated 3D voxel feature.

(2)3D-to-2D attention. Let the input consist of 2D pixel features p i ∈ R d p from a color map or a normal map, with a corresponding 3D voxel space represented by U ∈ R X × Y × Z × d u , where d p and d u denote the feature dimensions of 2D pixels and 3D voxels, respectively.

Each 2D pixel feature p i corresponds to a ray in a 3D space. Sampling H 3D voxel features along this ray forms a 3D voxel feature sequence:

Where u i,j is the 3D voxel feature at the j -th depth position along the projection ray of 2D pixel feature p i . H is the length of the 3D voxel feature sequence.

The 2D pixel feature p i is mapped to a query vector, while each 3D voxel feature u i,j is mapped to a key and a value vector:

By concatenating all key vectors and value vectors across the sequence of 3D voxel features, we construct the complete key and value matrices as:

Compute the attention output with the 2D pixel feature q i as the query, and the 3D voxel feature K i and V i as the key and value:

Here, p ′ i represents the updated 2D pixel feature.

Multiview Guided Decoder (MVGD). The 3d sparse structure V grid is first processed by the Structured Latents generative model, which denoises it into a structured latent z . For multiview color and normal images, we first upsample the images, then we extract multilevel local patch features using the DINOv2 backbone from layers l ∈ { 4 , 11 , 17 , 23 } per image:

The features from different layers are concatenated and then processed through a MLP to form the final representation:

Given a voxel position p = ( x, y, z ) , its projection onto the i -th view yields the corresponding pixel coordinates:

The corresponding image features are then retrieved via direct indexing:

The injection feature is constructed by concatenating the structured latent at position p ( z p ∈ R d z ) with multiview pixel-aligned features obtained from the color and normal maps of 4 views (8 feature vectors in total). Formally,

This injection feature is then processed by an MLP to refine the structured latent representation:

We insert a multiview injection module after each self-attention in the decoder. We apply the same multiview feature injection mechanism to both the mesh decoder and the GS decoder, resulting in refined mesh and GS representations.

Input

Hunyuan3D 2.5 [79]

IDOL [84]

LHM [41]

Ours

B More Experiment

B.1 More Results

Comparison with Gaussians-based Methods and Native 3D generative model. To further evaluate the effectiveness of our method, we conduct a qualitative comparison between SyncHuman and two Gaussians-based methods (LHM [41] and IDOL [84]), as well as a more advanced native 3D model, Hunyuan3D 2.5 [79], as shown in Fig. 11. All these methods are capable of producing structurally plausible and visually reasonable results. Since LHM and IDOL are based on Gaussians, they can only produce RGB images through rendering. For comparison, we render RGB images from the front view. Both LHM and IDOL rely on SMPL, and when SMPL estimation is inaccurate or fails, the resulting structure is correspondingly erroneous. Furthermore, as illustrated in Fig. 11, IDOL and LHM still exhibit limited fidelity. Hunyuan3D 2.5, trained on a large-scale dataset, is a native 3D model that can also produce reasonable human structures with details. However, as observed in Fig. 11, Hunyuan3D 2.5 produces human meshes with less fidelity.

Input

Ours

Without 2D-3D attention

Figure 13: Comparison of the quality of intermediate multi-view generation with and without 2D-3D attention.

B.2 Ablation of the quality of intermediate multi-view generation and 3D structure generation

We additionally report the quality of intermediate multi-view generation and 3D structure generation on a small human scan subset. IOU of the 3D structure generation: 0.5907 (with 2D-3D synchronization attention) vs. 0.4813(without 2D-3D synchronization attention). Color and normal image quality improvements by 3D-2D attention:

Because generating 3D structures or multiview images from single-view inputs has ambiguity, the generation results are not exactly the same as the ground-truth. However, our 2D-3D attention could produce results more similar to GT. This demonstrates that our 2D-3D synchronization attention could benefit both branches to improve the multiview generation quality and 3D structure quality. As in Fig. 12, after alignment using 2D-3D attention, the multi-view projections of the two branches can almost completely overlap. And as in Fig. 13, when with 2D-3D attention multiview images have a more reasonable human body structure.

B.3 Inference time.

On a single H800, the inference time is as follows: ours 38.57s vs Trellis 15.68s vs PSHuman 52.98s Our method is faster than PSHuman as it directly decodes a 3D shape without requiring additional differentiable rendering optimization. The slower speed compared to Trellis is due to our use of the 2D multi-view generation.

B.4 Unconditional Generation.

We tested our model on the unconditional generation task as in Fig. 14. The generation quality is worse than the conditional generation from a single-view image.

C discussion

C.1 Limitations about containing holes in the generation

Our method is based on Trellis [61], which uses FlexiCube [48] in the trellis mesh decoder branch does not put a water-tight constraint on the surfaces. Thus, holes may appear on the surface in some cases, as shown in Fig. 15. A possible way to make the generated meshes water-tight is to adopt another SDF fitting on the generated mesh. Alternatively, we may adopt other 3D native generative models using SDFs as targets, like Hunyuan3D [79] or TripoSG [27], to avoid this problem. We leave this for future work.

C.2 Differences from SyncDreamer.

Our method fundamentally differs from SyncDreamer in the following two aspects. First, the synchronization subjects are totally different. SyncDreamer synchronizes the generation of multiview 2D images, whereas our method synchronizes the 2D generative model and the 3D native generative model. Our method demonstrates that simultaneously generating multiview images and 3D representations benefits each other and greatly improves the 3D generation quality. We inject information in both directions, 2D to 3D and 3D to 2D. Second, the functionality and design of the volume in our method are fundamentally different from those in SyncDreamer. SyncDreamer constructs a feature volume to share information among different views. In contrast, in our method, the volume is a meaningful 3D representation generated from noise.

C.3 Ethics Statement

The objective of SyncHuman is to equip users with a powerful tool for creating realistic clothed 3D human models. By enabling 3D human generation from a single image, our method supports diverse ethnicities and populations, promoting equitable cultural representation. Our model was trained on the public datasets THuman2.1 [70], CustomHumans [12], THuman3.0 [51], and 2K2K [10], and tested on X-Humans [47] and CAPE [32]. However, there is a potential risk that these generated models could be misused to deceive viewers (e.g., adult content, political manipulation, exploitation of artists via digital replicas.). It is noted that this issue is not unique to our methodology but prevalent in other human generative methodologies. Therefore, it is absolutely essential for current and future research in the field of 3D human generative modeling to address and reassess these considerations consistently.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces method for reconstructing photorealistic 3D full-body humans. The key contributions are:\n\n- Synchronized 2D multiview diffusion and 3D native diffusion, without SMPL priors, reconstruct high-quality clothed human mesh even under challenging human poses.\n- Their unified decoder for 3DGS and mesh captures detail and fidelity from multiview features, and structural consistency from structured latents.\n\nSyncHuman is evaluated on the CAPE and X-Humans dataset, demonstrating state-of-the-art performance in reconstructing clothed 3D human in both geometric and appearance quality.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n1. The paper is well written and easy to understand. \n2. They propose a novel framework that combines 2D multiview diffusion and 3D native diffusion for the first time.\n3. SyncHuman achieves state-of-the-art performance in both geometric and appearance quality.\n4. 2D-3D synchronization attention mechanism and MVGD are intuitive and effective for the performance, as shown in Tab. 2 and Tab. 3.\n\n### Weaknesses\n\n1. SyncHuman consists of three different diffusion: Multiview Diffusion, Structure Diffusion, and Structured Latents Diffusion. They do not report the computational costs, but the expensive computation might be one of the limitations.\n2. The qualitative results demonstrate that SyncHuman is robust to self-occlusions. However, the input images are full-body images without inter-occlusions due to human-human or human-object interactions. It might be challenging to inpaint and generate missing regions without SMPL priors.\n\nQUESTIONS:\n1. How long does SyncHuman take to reconstruct 3D human compared to baseline methods? (Weaknesses 1.)\n2. The generative diffusion models can inpaint the missing regions under human-human or human-object interactions in MultiHuman or Hi4D dataset? (Weaknesses 2.)\n3. The numerics of baseline methods in Tab. 1 are different from the ones reported in each paper. Additionally, SyncHuman is trained on larger training datasets: THuman 2.1, CustomHumans, THuman3.0, and 2K2K. The baseline methods are re-trained in same training setting or not?\n4. SyncHuman is also trained without image condition to enable CFG during inference. How about the qualitative performance of unconditional generation?\n\nLIMITATIONS:\nComputational costs might be one of the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposed a framework that combines 2D multiview diffusion and 3D native diffusion, enabling high-quality clothed human mesh reconstruction from single-view images.\n\nGiving a single-view image, the method first generates 3D structures and multiview images. Then their use multi-view reconstructor to obtain 3D human textured meshes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nThe process of 2D-3D Cross-Space Diffusion and Multiview Guided Decoder improve the final results.\n\nFrom the experimental results, it can be seen that the method proposed by the author outperforms the results of the comparative methods.\n\n\nWeaknesses\n\nWhy did the author inject DINOv2 into MVGD? It requires an ablation study to demonstrate its effectiveness.     \n\nThe ablation on 2D-3D synchronization attention may unfair. The author fine-tuned the compared methods on X-Humans dataset, which only contains 100 scans from less than 20 participants. However, the propose method is trained on Thuman, 2K2K and CustomHumans which contains about more than 2,500 scans. They should fine-tuned the compared methods on all training data.\n\nThe proposed method is incremental. The author prefers to combine PShuman, Trellis, and DINOv2. The 2D-3D cross spatial diffusion simply mixes the features of two branches together.\n\nRepeat References. The [40&41], [71&72] references are same.\n\nMissing references \nDIFu: Depth-Guided Implicit Function for Clothed Human Reconstruction, CVPR 2023.\n\nIn L-269, a period is missing.\n\nQUESTIONS:\nWhy did the author inject DINOv2 into MVGD? It requires an ablation study to demonstrate its effectiveness.     \n\nThe ablation on 2D-3D synchronization attention may unfair. The author fine-tuned the compared methods on X-Humans dataset, which only contains 100 scans from less than 20 participants. However, the propose method is trained on Thuman, 2K2K and CustomHumans which contains about more than 2,500 scans. They should fine-tuned the compared methods on all training data.\n\nThe proposed method is incremental. The author prefers to combine PShuman, Trellis, and DINOv2. The 2D-3D cross spatial diffusion simply mixes the features of two branches together\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SyncHuman, a method for reconstructing 3D textured human meshes from a single image by using 2D multiview diffusion and native 3D diffusion models. The framework uses a synchronization attention mechanism to bridge the two models, then uses a multiview-guided decoder to further inject fine details from generated 2D images into the 3D latent space. Experiments on CAPE and X-Humans show that SyncHuman achieves good performance and quality.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The qualitative results are somewhat intriguing.\n2. The method avoids using SMPL, which has several known issues listed in the paper.\n\nWeaknesses:\n1. While the synchronized attention implementation is somewhat novel, the idea of enhancing geometry using a multi-view diffusion model is a widely adopted idea. The approach is primarily a well-engineered system combining two pretrained diffusion models.\n2. The numbers reported in Table 1 are different from the original numbers reported in their own paper (e.g., PSHuman). These significant downplaying remarks the potential and intentional unfavorable calibration to the evaluation, questioning the trustworthiness of certain baselines that were not evaluated on the benchmark (e.g., TRELLIS). The only interesting part of the paper is its outstanding performance shown in Table 1, but the numbers are not really reported in a responsible way. The Chamfer distances reported in the results are all unreasonably bad, which questions the generalization of the SMPL failures cases reported by the authors.\n3. The failure cases of SIFU and PSHuman in Figure 6 are significantly worse than it supposed to be. Certain distortions and limb discontinuities are so severe that iit s unbelievable.\n4. The ablation study lacks depth, and the improvements are incremental.\n5. 2D diffusion models are not very good at generating thin geometry (such as limbs and fingers), or maintain the semantic correctness (such as generating three legs, especially common in legacy StableDiffusion models). Do the authors observe similar problems? How is it addressed?\n6. The cross-view diffusion models are not robust in maintaining the geometric consistency of fine textures, such as hairs and wrinkles. Do the authors observe overly smoothened textures?\n7. The authors are extremely reckless in completing the checklist. I don't think the authors qualify to respond yes to \"7. Experiment statistical significance.\" The response to \"8. Experiments compute resources\" clearly does not read the guidelines. The answer to 10, 11, 15, simply disregards the human subject data used during the model training. Some of the training datasets do not have subject's consent. I typically don't pay too much attention to the checklist as people should have known what they are doing, but it is apparently not the case in the submission.\n\nQUESTIONS:\nSee weaknesses.\nThe authors need to justify the numbers reported in the paper. Explain why it is worse than the reported numbers by a significant margin (larger than the performance improvements). Is it an implementation error by the authors or evaluation bugs? In general, I have completely lost trust in the numbers I see in the paper, and the qualitative results of the previous method could be downplayed as well (compared to the qualitative results reported in their own paper).\n\nLIMITATIONS:\nAs disclosed in the paper, it is known that multi-view diffusion models are trained on idealized shadowless objects, so they cannot handle strong lighting. This limits its real-world applications. As mentioned in the weaknesses, the method may suffer from overly smoothened textures, but the authors did not mention relevant weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a method to reconstruct detailed 3D human from single RGB image. The idea is to combine the texture details from multi-view diffusion model with structure priors from TRELLIS. They introduce cross attention module to synchronize the 2D features from multi-view diffusion models and 3D features from the sparse grid of TRELLIS. The synchronization is done via pixel aligned orthographic projection. They further inject the multi-view features into sparse voxels before final decoding to enhance the details. Experiment resutls on two datasets demonstrate the effectiveness of the proposed pipeline. Ablations also show the importance of the proposed modules.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n- The overall writing flow is clear. \n- The proposed method works well on the evaluated benchmark datasets. \n- Ablation studies prove the effectiveness of the proposed modules. \n\n**Weakness**\n- Originality: the method overall is not very novel. The synchronized attention is pretty much the same as SyncDreamer, even the name is the same pattern -- SyncHuman. Even the MVGD module is also similar to SyncDreamer where they inject 3D volume feature to 2D multi-view but here it is in the opposite direction: 2D to 3D. \n- Clarity: Some method details are not clear. Is the 2D-3D synchronized attention done at each reverse sampling step? Eq. 3: flow matching loss is used here but in all previous text the term 'diffusion models' are used. Although mathematically they are very similar, but it is important to clarify them to be precise.\n- Clarification of the experiment setup: L240-'subset of X-humans', is it the same test set used in table 1? Also in table 2 and table 3, image metrics like PSNR/SSIM are reported, are they the generated 2D multi-view images or re-rendering of the reconstructed 3D? Although the end goal is to have better 3D, but it would be interesting to see if this cross attention mechanism would also improve the 2D multi-view diffusion model. Would this also improve the predicted multi-view normal?\n\nQUESTIONS:\n- How would this model compare to Hunyuan3D 2.0? As a more advanced version of TRELLIS, HY3D shows impressive details and on par texture details. It would be interesting to see if an off-the-shelf general object reconstruction model can already work well for the specific human reconstruction task. If not, what is the missing gap?\n\n- Strangely large errors (P2S, CD) from TRELLIS while TRELLISS (fine tuned) is much better, table 1-3. Did you do proper alignment (e.g. SiTH [1]) between reconstruction and GT mesh before reporting the numbers? Or please explain the gap between trellis and trellis-fine tuned. \n\n[1]. SiTH alignment for evaluation: https://github.com/SiTH-Diffusion/SiTH/blob/main/EVALUATE.md#1-single-view-human-3d-reconstruction-benchmark\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes SyncHuman, a novel approach for single-view 3D clothed human generation. The method effectively combines the strengths of 2D multiview diffusion models and native 3D diffusion models through pixel-aligned 2D-3D synchronization attention and a multiview feature injection strategy. After fine-tuning on a collected dataset, the proposed method achieves impressive photorealistic 3D full-body human reconstruction from a single image.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe approach successfully integrates the advantages of both 2D and 3D diffusion models. The experimental results convincingly demonstrate the effectiveness of the proposed design.\n\nWeaknesses:\nThe paper is missing relevant citations and lacks technical details and necessary discussion and analysis.\n\nQUESTIONS:\nSpecifically, I have a few questions regarding the paper and would appreciate it if the authors could provide clarification:\n1. During the 2D to 3D attention process, how are the resulting features added into the original 3D voxel features? Are the features averaged, concatenated, or combined in another way? Is this cross-attention mechanism applied at every diffusion timestep, or only at selected timesteps? Similarly, how are features incorporated during the 3D to 2D attention process? More detailed explanations of these mechanisms would be helpful.\n\n2. During joint training of the 2D and 3D models, are all models trained together, or are some model weights frozen? Is MVGD involved in the joint training process? Please provide more details on the training procedure, such as the specific process of the training, the amount of data used, the number of training epochs, and relevant hyperparameters.\n\n3. In the ablation experiments for the effectiveness of 2D-3D cross-space attention, could the authors compare the generation results of the 2D diffusion model before and after applying cross-attention? Theoretically, the 2D diffusion model with cross-attention should produce more cross-view consistent multi-view results compared to the original model. Similarly, does the 3D diffusion model show corresponding improvements after cross-attention is applied? Additional experiments to support these claims would strengthen the paper.\n\n4. It appears that two decoder branches were trained: 3DGS and mesh. Which branch’s results are shown in the experimental section? How significant are the differences between the two branches? It would be helpful to present results from both branches for comparison.\n\n5. In Fig. 6, although the overall pose and texture of the generated results are consistent with the input image, there are still noticeable inconsistencies in many details, such as the face shape in the first and third examples, and the skin tone in the last example. Additionally, from the video results, the generated humans still suffer from blurriness and white artifacts.\n\n6. The paper is missing several relevant references, including:\n1)\tConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis\n2)\tHumanRef: Single Image to 3D Human Generation via Reference-Guided Diffusion\n3)\tDINAR: Diffusion Inpainting of Neural Textures for One-Shot Human Avatars\n4)\tZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image\n\nLIMITATIONS:\nNo, the paper does not provide an explanation for the artifacts present in the results. Perhaps a related discussion could be added to the Limitations section.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}