Human Body Restoration with One-Step Diffusion Model and A New Benchmark
Authors: Jue Gong
Jingkai Wang
Zheng Chen
Xing Liu
Hong Gu
Yulun Zhang
Xiaokang Yang
Venue: ICML 2025 poster (2025)
Keywords: One-Step Diffusion Model
Human Body Restoration
Primary area: applications->computer_vision
TL;DR: A new benchmark and the first one-step diffusion model for human body restoration.

Abstract: Human body restoration, as a specific application of image restoration, is widely applied in practice and plays a vital role across diverse fields. However, thorough research remains difficult, particularly due to the lack of benchmark datasets. In this study, we propose a high-quality dataset automated cropping and filtering (HQ-ACF) pipeline. This pipeline leverages existing object detection datasets and other unlabeled images to automatically crop and filter high-quality human images. Using this pipeline, we constructed a person-based restoration with sophisticated objects and natural activities (*PERSONA*) dataset, which includes training, validation, and test sets. The dataset significantly surpasses other human-related datasets in both quality and content richness. Finally, we propose *OSDHuman*, a novel one-step diffusion model for human body restoration. Specifically, we propose a high-fidelity image embedder (HFIE) as the prompt generator to better guide the model with low-quality human image information, effectively avoiding misleading prompts. Experimental results show that OSDHuman outperforms existing methods in both visual quality and quantitative metrics. The dataset and code are available at: https://github.com/gobunu/OSDHuman.

Human Body Restoration with
One-Step Diffusion Model and A New Benchmark
Jue Gong"
Jingkai Wang" Zheng Chen
Xing Liu?
Hong Gu
Yulun Zhang
Xiaokang Yang'
Abstract
BRIQUUE
Human body restoration, specific application of image restoration, is widely applied in prac- tice and plays vital role across diverse fields. However, thorough research remains difficult, par- ticularly due to the lack benchmark datasets. In this study, we propose high-quality dataset auto- mated cropping and filtering (HQ-ACF) pipeline. This pipeline leverages existing object detec- tion datasets and other unlabeled images au- tomatically crop and filter high-quality human images. Using this pipeline, we constructed a person-based restoration with sophisticated ob- jects and natural activities (PERSONA) dataset, which includes training, validation, and test sets. The dataset significantly surpasses other human- related datasets in both quality and content rich- ness. Finally, we propose OSDHuman, novel one-step diffusion model for human body restora- tion. Specifically, we propose a high-fidelity image embedder (HFIE) as the prompt genera- tor to better guide the model with low-quality human image information, effectively avoiding misleading prompts. Experimental results show that OSDHuman outperforms existing methods in both visual quality and quantitative metrics. The dataset and code are available at: https: / //github. com/ gowunu/uuuuuuo Saauu//SDDHmaaaa
VOC
COCO
Object365
CrewdHuman
DeepFashion
iDesigner
HUSIQ
PERSONA (ours)
Figure I. Comparison of no-reference image quality assessment metrics across human-related datasets. The object detection datasets specifically evaluate subsets with humans. Our proposed PERSONA dataset outperforms others significantly. recognition human activities and the extraction of in- formation from the image. Furthermore, degraded images make other human- downstream tasks more challeng- ing, such human-object interaction detection (Wang et al., 2024d; Liu et al., 2025), human pose estimation (Samkari et al., 2023; Atmosukarto et al., 2024), and 3D reconstruc- tion (Wang et al., 2021a; Sun et al., 2024).
Despite its practical significance, progress in HBR remains constrained, primarily due to the absence of task-specific benchmark datasets, In natural scenarios, humans exhibit wide range of activities and complex interactions with their surroundings. Therefore, the benchmark dataset for HBR must be large, cover complex scenarios, and include natural activities. Datasets in the fashion domain, such as DeepFashion (Liu et al., 2016) and iDesigner (Dufour et al., 2022), focus on runway or studio scenarios. As a result, these datasets contain only limited types of human actions, making them unsuitable for HBR. Moreover, human images often involve multiple individuals interacting with each other, adding further complexity to the restoration task. Such complexity makes datasets focused on single- person image generation, such as SHHQ (Fu et al., 2022) and CosmicMan -Q (Li et al., 2024b), less suitable for HBR, as they mainly focus on single-person images.
1. Introduction
Human body restoration (HBR) aims to recover high-quality (HQ) images from low-quality (L.Q) inputs featuring human figures. Unlike nature scene pictures, human figures nat- urally attract viewers' attention in images. However, real- world images often suffer from degradation during capture and transmission, such as blur, noise, resolution reduction, and JPEG artifacts. These distortions severely impact the
Equal contribution 'Shanghai Jiao Tong University, China 2 vvvvo Mobile Communication Co., Ltd, China. Correspondence to: Yulun Zhang <yulunl Syulun100@@pp @ aal00@mmaalcom....
Proceedings of the nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Human Body Restoration with One-Step Diffusion Model and A New Benchmark
putational resources, which limits their applicability. To re- duce resource consumption, one-step diffusion (OSI) mod- els are proposed and achieve good results. By leveraging large-scale pretrained text-to-image (T2I) models (Saharia et al., 2022; Rombach et al., 2022) as foundation models, OSD models combine generation power with fast inference. Therefore, OSD models are highly competitive in HBR, However, this also requires the model incorporate an ap- propriate prompt extractor that can derive high-fidelity prompt from complex human images. Otherwise, the result- ing prompt could mislead the restoration of the model.
LQ (512x5122
OSEDiff
SinSR
Shift
OSFDiff et,, 2024a) (Wang al., 2024e) (Yue et al., 2023) Figure 2. Visual examples of diffusion-based based image restoration methods evaluated on PERSONA test. The asterisk (*) indicates methods retrained on PERSONA dataset. Our OSDHuman pro- duces more natural and faithful visual results compared to others.
OSDHuman (ours)
SinSR*
ResShift*
To address the limitations, we propose OSDHuman, a novel OSD model for HBR. Firstly, to overcome the lack of benchmark datasets in HBR, we propose high-quality dataset automated cropping and filtering (HQ-ACF) pipeline, This pipeline preprocesses both labeled and unlabeled datasets to isolate images containing humans. Then it refines human bounding boxes and crops them accordingly. Using no-reference image quality assessment (IQA) metrics, we ul- timately produce a dataset. Secondly, leveraging HQ-ACF, we develop person-based restoration with sophisticated objects and natural activities (PERSONA) dataset, which comprises 109,053 HQ 512x human images for train- ing. This pipeline also provides images for validation and testing. The PERSONA dataset includes both individual- environment interactions and multi-person interactions, av- craging 3.4154 individuals per image. Thirdly, to provide prompts suitable for HBR, we propose a high-fidelity im- age embedder (HFIE). HFIE uses an image encoder from RAM (Zhang al., 2023) and multi-head attention layer with learnable embedding as the query. This design avoids distortions introduced by tags when summarizing images, thereby preventing misleading prompts that could impair model restoration. In addition, we employ a variational score distillation (VSD) regularizer to guide the model's generative distribution toward natural image distributions.
Additionally, an HBR model can achieve optimal per- formance only when trained on sufficiently high-quality datasets. Degraded datasets could cause bias in the model's weights, as it is difficult to distinguish between degrada- tion and features in LQ images. Some existing image restoration datasets, such as LSDIR (Li et al., 2023) and DIV2K (Agustsson& & Timofte, 2017), are of high quality and cover complex real-world scenarios. However, the pro- portion of human images in these datasets is small, and they are not specifically tailored for human images. Other human-related high-level datasets, such as those for object detection (Kuznetsova et al., 2020; Shao et al., 2019) and keypoint detection (Lin et al., 2014), have a wider range of human activities and sophisticated surrounding objects due to the diversity of image sources. However, as illustrated in Fig. 1, these datasets lack dedicated quality filtering, con- taining substantial LQ samples.
However, even with high-quality benchmark, achieving excellent HBR performance still requires a well-designed model architecture. In recent studies, image restoration models with latent diffusion model (LDM) (Rombach et al., 2022) architecture achieve promising results due to powerful generative capabilities. These models combine generation and restoration to reconstruct lost parts of LQ images us- ing the features provided. They primarily use two main latent space mapping methods: variational autoencoder (VAE) (Kingma & Welling, 2014) and vector quantized VAE (VQVAE) (Oord et al., 2017). As shown in Fig. 2, models with VQVAE, such as ResShift (Yue et al., 2023) and SinSR (Wang et al., 2024e), often produce distorted structures in detail. Even with retraining, these issues are difficult to resolve due the VQVAE codebook's 's inability to capture the fine details of the human body. On the other hand, models with VAE, like OSEDiff (Wu et al., 2024a), have better generalization and detail generation capabilities, but they are still not specifically optimized for HBR.
Our contributions can be summarized follows.
We propose a person- based restoration with sophisti- cated objects and natural activities (PERSONA) dataset which provides benchmark for human body restora- tion, encompassing training, validation, and test sets.
Our PERSONA dataset surpasses other human-related datasets in quality and includes a wide range of scenar- ios that cover the majority of human activities.
We propose OSDHuman, an innovative one-step dif- fusion model for human body restoration. It features a high-fidelity image embedder (HFIE) designed for extracting suitable prompts from human images.
Our OSDHuman achieves state-of oftte-oftthear human body restoration performance, excelling in visual quality and metries while maintaining lower computational costs.
While multi-step diffusion models have strong restoration abilities for LQQ images, they often require substantial com-
2

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Human Body Restoration with One-Step Diffusion Model and A New Benchmark
Reject Accept
110
Variance Calculation
Blur Awareness
0.25
0.50
0.75
1,00
Normalized Metrics
AND
400 & W 400 Reject
CLIPIQA
0.85
MANIQA
0.65
H W: max(H, W) Crop
MUSIQ
65.0
YOLO11
NMS
NIQE 4.2
Datasets Collection & Preprocessing
Box Refinement
IQA Filtering
PERSONA Dataset
Figure 3. High-quality dataset automated cropping and filtering pipeline. The pipeline consists of four stages. First, multiple datasets are collected, comprising millions of images. Images without labels are processed using YOLO11 for human detection. Then, a Laplacian operator is applied to compute image Laplacian variance, filtering out images below threshold. Next, human boxes are adjusted to the square shape, and overly small or densely packed boxes are removed. Finally, cropped human images are evaluated using Image Quality Assessment (IQA) metries. Images ranking in the top third by normalized metrics and exceeding the metric threshold are selected. These 109,053 images constitute the person-hased restoration with sophisticated objects and natural activities (PERSONA) dataset.
2. Related Works
ing more powerful T2I models. Recently, many efforts have been made to integrate these two typical applications. The rapidly developing Stable Diffusion (Rombach et al., 2022), DALLE (Ramesh et al., 2021), and PixArt (Chen et al., 2024) have continually pushed the boundaries of realism and diversity in T2I generation. Many image restoration methods have also leveraged pretrained models to achieve more natural image recovery (Wu et al., 2024b; Yang et al., 2024; Lin et al., 2024; Wu et al., 2024a; Wang et al., 2024a).
2.1. Human Body Restoration
Human body restoration (HBR) could benefit both the fash- ion industry for display and the photography and camera producers. The purpose is evident, focusing on the human body and making it look better. Compared to general image restoration for arbitrary objects, HBR is more constrained, allowing for the use of variety of prior knowledge. Firstly, current research on image segmentation has made signifi- cant progress in generating segmentation masks for different body parts, such as hair and arms, which provide a clear description of the human body shape. This knowledge is beneficial for handling the boundaries in the HBR tasks. Since the human body composition is relatively fixed and limited, it is easier to estimate compared to the random and arbitrary objects in natural images.
Stricted the multi-step in the diffusion inference proce- dure, the diffusion models with 50 or more steps (Wang et al., 2024b; Lin et al., 2024; Wu et al., 2024b; Yang et al., 2024) cannot actually be used in practice. Many efforts have been made to faster diffusion models, such as cutting, quantizing, and compressing. Moreover, elim- inating the number of inference timesteps is convincing way, especially applied in image restoration. SinSR (Wang et al., 2024e) pioneers one-step inference for difflssio--baedd super-resolution (SR) by distilling deterministic generation functions into a student network, coupled with a consistency- preserving loss and efficient training pair generation strategy. OSEDiff (Wu et al., 2024a) adapts pretrained SD models for SR through LoRA-finetned U-Net and variational score dis- tillation, enabling direct low-quality image reconstruction in one step without noise injection. Those methods achieve fascinating performance in natural image restoration.
Lots of research has been done recently. A previous work (Liu et al., 2021a) captures body texture using sub- bands of the non-subsampled shearlet transform, while PRCN (Wang et al., 2024c) employs a pyramid residual network estimate texture and shape priors, enhancing body images. DiffBody (Zhang et al., 2024), the first to apply diffusion models, uses pose-attention, text guid- ance, and a body-centered sampler to integrate semantic information for body-region enhancement.
2.2. Diffusion Models
3. Methods
Since the diffusion model was released and popular, many efforts have been made. Two classical applications are im- age restoration and text-to-image (T2I). Image restoration, as the first and most natural application, has been developed a lot (Saharia et al., 2023; Whang et al., 2022; Avrahami al., 2022: Chen et al., 2023: Xia al., 2023). With the de- velopment of conditional diffusion models (Rombach al., 2022), numerous companies have invested heavily in train-
3.1. High Quality Human Dataset Pipeline
For image restoration tasks, large-scale high-quality datasets are required to simulate various real- world scenarios and ob- jects. There are already many high-quality image restoration datasets, such as FFHQ (Karras al., 2019) and LSDIR (Li et al., 2023). However, these datasets not ideally suited for the human body restoration (HBR) task due to their lack
3

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Human Body Restoration with One-Step Diffusion Model and A New Benchmark
Pipeline
VSD Module
High-Fidelity Image Embedder
77x1024
P
Pretrained Regularizer
Finetuned Regularizer
Ey
EB
512-512-3
VAE
64-64x4
U-Net
E
Encoder
Add Noise
VSD
1
Dg
MSE LIPIP
VAE
Decoder
Trainable
Frozen
Data
Loss
Module
Module
Flow Calculation
Figure 4. Training Framework of OSDHuman. First, the LQ image Lr. is processed through the VAE Encoder, U-Net, and VAE Decoder, ultimately producing the restored HQ image ÍN. The conditional input of the U-Net is provided by the high-fidelity image embedder (HFIE). Second, during the training process, the λµ generated by the U-Net is subjected to noise and then passed through the pretrained and finetuned regularizers. LvSD represents the distribution's 's difference between the model output and the natural image. LVsD, together with LimPs and LMSE, constitutes the training objective. In summary, during the training stage, the VAE Encoder, U-Net, and finetuned regularizer are trained with LoRA, while other modules remain frozen. During inference, the VSD module not utilized.
of focus on human-specife features. Moreover, the dataset should enable models to adapt to real-world environments' complexities. It must encompass most scenarios including interactions among people and between people and their sur- roundings. Therefore, we propose a high-quality dataset au- tomated cropping and filtering (HQ-ACF) pipeline for HBR datasets, well as person-based restoration with sophisti- cated objects and natural activities (PERSONA) dataset.
square crop. In cases where an image contains multiple overlapping boxes, non-maximum suppression (NMS) is applied, prioritizing the box closest to the image center. The cropped images are then resized to 512x512.
Finally, we obtain approximately 440,000 cropped images, which we measure no-reference Image Quality Assess- ment (IQA) metries. The metrics used are common in im- age restoration, including CLIPIQA (Wang et al., 2023a), MANIQA (Yang et al., 2022), MUSIQ (Ke et al., 2021), and NIQE (Zhang et al., 2015). To balance the evaluation performance of each metric, we normalize the obtained IQA metrics using the following standardization formula:
Automated Cropping and Filtering Pipeline. As illus- trated in Fig. 3, we first collect a series of commonly used and publicly available large-scale object detection datasets, including COCO (Lin al., 2014), OID (Kuznetsova al., 2020; Krasin et al., 2017), Object365 (Shao al., 2019) and CrowdHuman (Shao al., 2018), comprising approxi- mately 4 million images. We then filter the images by labels, selecting those containing "human" or synonymous labels, such as "Human Body" in OID and "person" in Object365. To further refine the selection, we conduct human detection on the image restoration dataset LSDIR (Li et al., 2023), using the YOLO11 model (Jocher & Qiu, 2024) for process- ing. This operation resulted in bounding boxes similar to those in object detection datasets.
N
M, - 14
Normalized Metrics
(I)
N
1=1
where μy is the mean and is the standard deviation of the metrics (Mz). Since NIQE is better when its score smaller, we first apply a negative transformation to its values. The normalized scores are then accumulated per image and sorted. The final PERSONA dataset version consists of 109,053 images with normalized metrics in the top third, and each IQA metric must exceed a predefined threshold.
Next, we apply the Laplacian operation to these datasets and compute the variance of the results. Images with a variance below the threshold are discarded, as these correspond to images with a high degree of blurriness. Before cropping, we also check the size of the human bounding boxes. Images with www-resolution human bodies are rejected. After these steps, we use the bounding boxes to crop the images. When cropping, the side length of the bounding box's longer edge is used as the side length of the cropping box, ensuring a
3.2. One-Step Diffusion (OSD) Model
Model Architecture Overview. Most image restoration tasks with OSD models are extensively studied in previous works (Wang et al., 2024e; Wu et al., 2024a; Wang et al., et al., 2024a). However, these methods struggle to achieve desirable results in human body restoration (HBR). To address this limitation, we propose OSDHuman, an OSD model specifically designed for HBR. Specifically, we adopt
2024a; Li

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Human Body Restoration with One-Step Diffusion Model and A New Benchmark
Stable Diffusion (SD) model architecture (Rombach et al., 2022) by fixing the number of steps, thereby transforming it into an OSD framework. As shown in Fig. 4, the first step uses the variational autoencoder (VAE) encoder Ee to project the low-quality (I.Q) image 1.. into the latent space, resulting in 2 = Eo(L)). Subsequently, single denoising operation F& is applied to estimate the noise, which is crucial for enabling the calculation of the predicted high-quality (HQ) latent vector yn through the equation:
Conditional
Conditional Embedding p
Tags
Tagging Head
Multi-head Attention
@
K
Leamable Embedding
Image E Encoder
Image Encoder
2H1 = Fo(21;)) = 21. VT- OTT Su(z,, Tr.)
VOTE
LQ Image 1/ (a) DAPE
LQ Image Ij (b) HFIE
where Eo represents the denoising network governed by the parameter f. p is the output of the high-fidelity image embedder (HFIE), and Tr, refers to the diffusion timestep. A predefined parameter Tr E []] is used as input to the U-Net, where T signifies the total number of diffusion steps (e.g., T = 1, 000 in SD). The VAE decoder Dg is then employed to reconstruct the HQ image In from the predicted latent vector εh, expressed as H = (*(H)) If the generator is denoted as G, the complete process can be summarized by the following equation:
Figure 5. Comparison of the architectures of HFIE and DAPE.
represent local information of images. The remaining em- bedding is obtained through the average pooling of others, which contains the overall information of images. Therefore, using a linear layer to reduce the embeddings' size to match the input of SD (e.g.. 77 1,024 for SDD-2.1) would result in the loss of distinguish ability between overall and local information. Our proposed method HFIE uses learnable embeddings Q as the query input to the multi-head attention (MHA) layer, ultimately producing the HFIE:
in (T):
Training Objective. During training, we utilize pixel-wise MSE loss and perceptual loss LPIPS (Zhang et al., 2018). Additionally, the obtained स is used to compute the vari- ational score distillation (VSD) loss, ensuring alignment between the generated images and natural images. The final overall training objective for the generator Go is the following. A and X2 are the weights for LIPIPS and LVSD-
p HFIE(L)) = (()),
(5)
Variational Score Distillation (VSD). Fine-tuning image restoration models often encounter challenges due to the limited training data available, especially when compared to large-scale foundation models like Stable Diffusion (SD). leads generated images that fail to align with the natural image space distribution. Previous studies (Yin et al., 2023; Wang al., 2023b; Dao et al., 2024) propose some VSD methods to solve this problem by aligning the distributions represented by two diffusion models. Following OSED- iff (Wu et al., 2024a), we apply a VSD module in latent space to guide OSDHuman in learning the distribution of natural images from SD The VSD loss is computed from the distribution gap in the latent space output by the pre- trained regularizer cs and finetuned regularizer ey. The gradient of the VSD loss is defined as:
Lo. = CMSE((H, in) + A1 LI IH) (4)
+ A2 LvsD(êH,1)) p).
The degradation level of LQ human images is generally significant, and the image content is often highly complex. It is necessary to consider the coordination between the human pose and the surren- dering. Therefore, we propose an HFIE that can guide the restoration direction, reducing the feature gap between HQ and LQ human images. OSEDiff (Wu et al., 2024a) employs finetuned RAM (Zhang et al., 2023) as the degradation- aware prompt extractor (DAPE). It provides tags to guide the OSD model. However, the tags generated by DAPE are often too broad and imprecise for human images, offering insufficient and even biased guidance to the OSD model.
High- Hligh-Fieelity Image Embedder.
V VoLvsD(EH, p) Vin LVSD (2n, p)
00
(6)
<,(2r; (, ex (,x;4, 1,
0ān
E. &,
mean(]lE (&c:t, - 2H
00
As shown in Fig. 5, the RAM used in DAPE consists of two parts: the image encoder and the tagging head. However, HFIE only uses the image encoder (E) in RAM, leveraging the Swin Transformer (Liu et al., 2021b), which downsam- ples the input LQ Vz E 3884x384x3 by factor of 32. The H represents the resized LQ Iz. The image embeddings are 145
where is sampled from the range [20, 980). s and 2, denotes the output after adding noise at timestep t. Besides, to ensure VSD working, the finetuned regularizer es needs to be trainable. Its training objective is:
N/ I)
Lea
E LMSE p),
(7)
Jz = (II, E (R511) k where the first 144 embeddings 1k=1,
5

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Human Body Restoration with One-Step Diffusion Model and A New Benchmark
Figure The PERSONA dataset consists human images engaged various natural activities, featuring diverse surrounding objects.
Dataset
BRISQUEL
NIQE,
CLIPIQAT
MANIQAT MUSIQT
Perple yowara 1969
OID
19.8621
3.6611
0.4775
0.5940
60.4947
VOC
21,2764
3.7155
0.6071
0.7011
68.6073
COCO
15,2091
3.7774
0.6778
0.6844
69.5428
Object365
17.6128
3.6315
0.6273
0.6817
67.7270
CrowdHHumaa
20.4306
2,9283
0,5160
0.6587
63.6830
DccpFashion
42.1884
6.9403
0,5448
0.6515
71.1873
DDssigner
25.8027
4.6227
0.6666
69.3768
PERSONA (ours)
10.3758
2.8659
0.7632
0,7198
74,7808
building (74504 397)
Table I. Quantitative comparison across different human-related
Categories
datasets, with the best results highlighted in bold.
H4222
Sport (1974221 172)
categories are identified from the dataset. And the largest proportion belongs to the "Objects" category, demonstrating the presence of sophisticated objects in our dataset. Addi- tionally, the dataset generates a total of 1,452,088 tags, with approximately half of these tags falling under the "People", "Sports", and "Activities" categories. It indicates that the dataset is centered on natural human activities. As shown in Fig. 6, most of the images relate to this theme. Finally, the average number of tags per image in the dataset is 13.32, highlighting the dataset's ease of understanding.
Craibend certac 2401
Ouddeot
Figure 7. The distribution of tags in the PERSONA dataset, identi- fied by the Recognize Anything Plus Model (Huang et al., 2023). The angles of the pic chart represent the frequency of cach tag category in the dataset, i.c., the tag count. The tag category count indicates how many tags contained within each category.
4.2. Experimental Settings
4. Experiments
Training and Testing dataset. Our OSDHuman model is trained on our PERSONA dataset, which contains 109,053 high-quality 512x512 human images. The degradation pipeline of Real-ESRGAN (Wang et al., 2021b) is used to generate synthetic degraded images for training. The test data includes PERSONA-WE and PERSONA-Tes, both generated by HQ-ACF pipeline. The HQ images in the validation set are specially selected from those that comply with the pipeline, ensuring that no images in the validation set share sources with the training set. A total of 4,216 images are used, and the degraded LQ images are gener- ated using the same degradation pipeline as during training. The test set is derived from the VOC dataset (Everingham et al., 2010) by performing partial crop using the HQ- ACF pipeline, followed by sampling under predefined IQA thresholds, yielding 3,000 images with real- real-world LQ.
4.1. Quantitative Analysis of PERSONA Dataset
High-Quality Dataset. Our person-based restoration dataset with sophisticated objects and natural activities (PERSONA) dataset, consists of 109,053 human images with resolution of 512x512. These images are obtained through the high-quality automated cropping and filter- ing (HQ-ACF) pipeline. To quantify the quality of the PERSONA dataset, we evaluated a range of no-reference image quality assessment (IQA) scores, including CLIP- IQA (Wang et al., 2023a), MANIQA (Yang et al., 2022), MUSIQ (Ke et al., 2021), BRISQUE (Mittal et al., 2011), and NIQE (Zhang et al., 2015). We compare the results with those from other human-related datasets, including the ob- ject detection datasets OID (Kuznetsova et al., 2020; Krasin et al., 2017), VOC (Everingham et al., 2010), COCO (Lin et al., 2014), Object365 (Shao et al., 2019), CrowdHu- man (Shao et al., 2018), and fashion domain datasets Deep- Fashion (Liu et al., 2016), iDesigner (Dufour al., 2022). As shown in Tab. 1, our dataset consistently outperforms these datasets across all the no-reference IQA measures.
Evaluation Metrics. For the PERSONA- we em- ploy both reference-based and non-reference IQA metrics. DISTS (Ding al., 2020) and LPIPS (Zhang et al., 2018) are used for reference-based perceptual quality assessment. while PSNR and SSIM (Wang et al., 2004) (calculated on the Y channel in YCbCr space) are used for reference-based fidelity assessment. Additionally, FID (Heusel et al., 2017) is used to measure the distribution between the restored images and GT. The non-reference IQA metrics we used includes CLIPIQA (Wang et al., 2023a), MUSIQ (Ke et al.,
Rich-Diversity Dataset. We utilize the Recognize Any- thing Plus Model (Huang et al., 2023) to obtain image un- derstanding tags for our PERSONA dataset. The visual results are shown in Fig. In total, 3,365 distinct tag
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Human Body Restoration with One-Step Diffusion Model and A New Benchmark
PERSONA-Val
PERSONA-Test
Type
Methods
DISTSL
LPIPSL
PSNRT
SSIMT
FIDE
CLIPIOATMANQQATANN
NIQEJ
MATIOAATAANIOAMMUSSIOS NIQEJ
DifIBIR
0.1475
0.5718
14.8418
0.7030
76,4751
0.7287
0,6812
73.2505
0.2851
21.31
0.6993
76.8001
0.6698
73.2985
4,0228
Multi-Step Dillusion
26.7405
67.3329
4.6249
0.6703
72.1972
3.8728
ResShin
0.6157
69.5889
69.4611
4.8438
ResShin
21.73
0.6121
71.9257
4.8061
0.6174
70.2313
4.8735
SinSR
0,1691
0.3187
21.92
0.5967
22.9041
0.6372
0.5712
70.0839
4,4392
0.5882
0.6010
69.0157
4,7510
SinSR
0.1844
0.3348
21.54
0.5766
34.5773
0,7033
0.5819
71.2943
4. 4.6294
0,6936
0.5962
69,9375
4,9873
Diffussio
One Step
OSEDif
0.1510
0.2824
21.81
0.6182
17,6308
0.6875
0.6639
74.0774
3.5858
0.6734
0.6919
73,5634
4,4600
OSEDi
0.1476
0.2756
22.23
0.6342
17.2200
0,7034
0.6976
73.7636
3,9980
0.6874
0.7052
73.1611
4,4261
OSDHuman
0.1414
0.2627
22.41
0.6363
16.5987
0.7295
0.6934
76.1256
3,5750
0.7155
0.6977
73.7694
4.1287
Table Quantitative comparisons on synthetic PERSONA- Val and real-world PERSONA- datasets. For each metric, the best and
second best results highlighted in and eyan, within both multi-step step and one-step diffusion basedd methods. Models labeled with an asterisk (*) represent versions retrained on our PERSONA dataset for reference.
LQ
DimIBR
SecSR
PASD
ResShift
SinSR
OSEDiI
OSEDiI*
OSDHuman
I.Q
SeeSR
ResShift
OSEDid
OSDHuumn
LQ
DifTBIR
SecSR
PASD
ResShift
SinSR
OSEDif
OSEDiff
OSDHuumn
Figure 8. Visual comparison of the real-world PERSONA- -Test dataset in challenging cases, Please zoom in for better view.
2021), MANIQA (Yang et al., 2022), and NIQE (Zhang al., 2015). For the PERSONA Test, we use the same non- reference IQA metrics PERSONA-Vall We utilize the evaluation codes provided by pyiqa (Chen & Mo, 2022), with the pipal version used for MANIQA.
4.3. Main Results
Quantitative Comparisons. Tab. 2 presents the evaluation metrics of our OSDHuman on the synthetic PERSONA- Val and rorl-world PERSONA --etst datasets. Our method achieves the best or second-best results across most metrics when compared with both original and retrained one-step diffusion models. Against multi-step diffusion methods, OS- DHuman outperforms in DISTS, LPIPS, PSNR, SSIM, and NIQE on PERSONA- Val, as well as CLIPIQA, MANIQA, and MUSIQ on PERSONA-Test. with other metrics being comparable. While multi-step diffussin methods excel re- constructing highly degraded regions and achieving higher CLIPIQA scores, their generated content often deviates from the original, resulting in lower fidelity and perceptual scores such as PSNR. SSIM, DISTS, and LPIPS.
Implementation Details. The OSDHuman model is trained by AdamW optimizer (Loshchilov & Hutter, 2019) with batch size of 16 and 5e-5 learning rate. The Stable Diffusion v2-1 model (Stability AI, 2022) serves as the pretrained OSD model with the timestep frozen to 999, and the prompt embedding is provided by HFIE. The LoRA (Hu al.. 2022) rank for the VAE encoder, the U-Net of the generator, and the regularizer and A2 in Eq. are set to and 1, respectively. Training is conducted for 35K iterations on 4 NVIDIA A800 GPUs. Compared Methods. We compare OSDHuman with sev- eral
all set to 4. The weighting scalars X1
Visual Comparisons. As shown in Figs. 8 and 9, represen- tative images from the real-world PERSONA- dataset and the synthetic PERSONA- Val dataset are visualized. Ex- isting state-of-the-arr image restoration methods are not well suited for human body restoration (HBR). HBR tasks, the most challenging aspects often involve parts of the human image with intricate textures and delicate structures, such faces, fingers, and surrounding objects. These methods frequently exhibit issues like over-smoothing or unnatural
methods, including DifIBIR (Lin et al., 2024), SeeSR (Wu et al., 2024b), PASD (Yang et al., 2024), ResShift (Yue et al., 2023), SinSR (Wang et al., 2024e) and OSEDiff (Wu et al., 2024a). Among them, SinSR and OSEDiff are OSD models. ResShift and OSEDiff are re- trained PERSONA dataset, referred to ResShift* and OSEDiff respectively. Additionally, SinSR is distilled using ResShift*, namely SinSR*.
7

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Human Body Restoration with One-Step Diffusion Model and A New Benchmark
HQ
LQ
DifBIR
SccSR
PASD
ResShift
SinSR
OSEDiff
OSEDift
OSDHuman
1QQ
LQ
DimBBR
SeeSR
PASD
ResShit
SinSR
OSEDilf
OSEDiA*
OSDHuman
HQ
LQ
DiMTBRR
SeeSR
PASD
ResShift
SinSR
OSEDir
OSEDiN
OSDHuman
Figure 9. Visual comparison of the synthetic PERSONA- Val datasets in challenging cases. Please zoom in for a better view.
Training Dataset
PERSONA Val
PERSONA Test
DISTSL
LPIPS
PSNRT
SSIMT
FID
CLIPIQAT MANIQA1
MUSIQU
NIQEL
CLIPIQAT MANIQAT MUSIQU
NIQEL
LSDIR
0,1521
0.2692
22.51
0.6271
17,5615
0.7229
0.6618
74.4998
3.6461
0.6781
0.6964
72.1266
4.6382
PERSONA
0.2627
22.41
16.5987
0.7295
0.6934
76.1256
3.5750
73.7694
4.1287
Table 3. Ablation studies within different training datasets. The best results are highlighted in bold.
Comparison on Prompt Extractors. We conduct exper- iments with four options evaluate the effectiveness of various prompt extractors for HBR. The first option does not employ prompt extractor but uses space as the prompt. For the second option, we use DAPE (Wu et al., 2024b) with the setting of OSEDiff (Wu et al., 2024a). In OSED iff, HQ images are inputted into DAPE during training to provide higher-quality prompts. Following this, we also input HQ images into HFIE the third option. The last option, which is default setting, involves using HFIE to extract prompts from LQ images. As shown in Tab. 4, our HFIE of default setting outperforms the other options. It demonstrates the superior capability of HFIE in providing priors to the model, guiding higher- ciaherr-ualtty restoration.
Prompt Extractor
CLIPIQAT MANIQAT
MUSIQT
NIQEJ
Type From HQ From LQ
Null
0.7016
0.7226
73,1735
5,0651
DAPE
0.6625
0,7014
72.3104
4,9455
0.6747
69.9992
HFIB
0.7155
0.6977
73.7694
4.1287
Table Ablation studies within different prompt extractors tested on PERSONA- Test. "From HQ LQ" indicates prompts are ex- tracted from HQ or LQ. The results are highlighted in bold.
color rendering, making it difficult to accurately restore fine details such as facial features. For example, DiffBIR (Lin et al., 2024) suffers from over-smoothing or non-faithful textures, OSEDiff (Wu al., 2024a) often results in over- saturated facial regions and ResShift (Yue al., 2023) hibits distorted facial features. In contrast, OSDHuman model can restore natural human actions and facial expres- sions in images, achieving high fidelity and maintaining high degree of similarity to the original image.
5. Conclusion
In this work, we propose high-quality dataset automated cropping and filtering (HQ-ACF) pipeline designed for ating human body restoration (HBR) dataset to address the lack of a benchmark. Using this pipeline, we develop person-based restoration with sophisticated objects and nat- ural activities (PERSONA) dataset, which includes training, validation, and test sets. Experimental results show its high quality and suitability for HBR. Additionally, we propose Humnu one-step diffusion model for HBR. It inno- vatively employs a high- high-fidelity image embedder (HFIE) prompt extractor. HFIE guides the model in achiev- ing high-qualin restoration by effectively extracting and utilizing rich human image features. Extensive experiments show that OSDHuman outperforms current state-of-the-art image restoration methods, which are applied HBR, in both visual quality and quantitative metries.
4.4. Ablation Studies
Comparison on Training Datasets. To evaluate the suit- ability of our PERSONA dataset for HBR- -speciccc models, we train our OSDHuman on different datasets. The first option employs LSDIR (Li et al., 2023), dataset widely utilized in the image restoration domain. During training, images the dataset are randomly cropped 512x512 as input. The second option employs our PERSONA dataset. As shown in Tab. 3, the results of the model trained our PERSONA significantly outperform that trained on LSDIR in both PERSONA- -al and PERSONA- Test. This demon- strates that our dataset provides strong prior for the human body, effectively enhancing the performance of HBR.
8

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Human Body Restoration with One-Step Diffusion Model and A New Benchmark
Impact Statement
Fu, J., Li, S., Jiang, Y., Lin, K.-Y,, Qian, C., Loy, C. C., Wu, W., and Liu, Z. Stylegan-human: A data-centric odyssey of human generation. In ECCV, 2022.
This paper presents research aimed at advancing the field of Machine Learning. While there are various potential societal implications of our work, we believe that none of
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. Gans trained by a two time-scale update rule converge local nash equilibrium. In NeurIPS, 2017.
these require particular emphasis here.
Acknowledgments
This work was supported by Shanghai Municipal Science and Technology Major Project (2021 (202IH22Z00002 and the Fundamental Research Funds for the Central Universities.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. LoRA: Low-rank adaptation of large language models. In ICLR, 2022.
Huang, X., Huang, Y.J.,, Zhang, Y., Tian, W., Feng, R., Zhang, Y., Xie, Y., Li, Y., and Zhang, L. Open-set im- age tagging with multi-grained text supervision. arXiv 2023.
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Reviews:
[{"review_id": "INQtF7dYIo", "reviewer": "Reviewer_ndyH", "summary": "This study addresses the challenge of human body restoration by introducing a high-quality dataset construction pipeline, HQ-ACF, which automatically crops and filters human images from existing datasets. Using this pipeline, the PERSONA dataset is created, offering superior quality and content richness compared to existing human-related datasets. Additionally, the study proposes OSDHuman, a novel one-step diffusion model for human body restoration. OSDHuman features a high-fidelity image embedder (HFIE) to generate more accurate prompts, reducing the risk of misleading guidance. Experimental results demonstrate that OSDHuman achieves state-of-the-art performance in both visual quality and quantitative metrics.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "`Q1-1:` The novelty seems limited, as VSD is adapted from prior work and HFIE resembles an attention-based DAPE.\n\n`A1-1:`\nThank you for your valuable comments. Our model is the first to focus on human body restoration using a one-step diffusion framework. Our model's VSD module follows the OSEDiff design, which builds on DMD [2] to optimize distribution loss in the latent state space. The DAPE, which requires additional training, inevitably introduces errors and missed predictions, as discussed in our supplementary materials. In contrast, our proposed HFIE does not require separate training. Instead, HFIE utilizes the image encoder from the Recognize Anything Model and integrates a trainable multi-head attention layer. This approach improves performance and reduces computational costs by eliminating the need for extra training and tagging heads.\n\n[1] Wu et al., One-step effective diffusion network for real-world image super-resolution, NeurIPS, 2024\n\n[2] Yin et al., One-step diffusion with distribution matching distillation, CVPR, 2023\n\n---\n`Q1-2:` The dataset uses limited degradation types. If it mainly relies on blind SR from [4], should the task be framed as \"super-resolution\" rather than \"restoration\"?\n\n`A1-2:`\nThanks for raising this concern. The RealESRGAN [4] pipeline models a broad range of realistic degradations, including blur, noise, and JPEG compression, beyond simple downsampling. After applying this pipeline, we resample images to 512×512, preserving diverse artifacts.\n\nIn addition to synthetic validation data, our test set includes real-world degraded images with motion blur, noise, and compression. Examples are shown in Figs. 5 and 6 of our supplementary materials.\n\n[4] Wang et al., Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data, ICCV 2021\n\n---\n`Q1-3:` Since low-IQA images were filtered out, does this introduce evaluation bias and weaken the value of the HQ-ACF pipeline?\n\n`A1-3:`\nThanks for raising this concern. The IQA metrics used in our HQ-ACF pipeline are widely recognized and commonly applied in image restoration tasks, ensuring the high quality of our dataset. To address potential bias, we further evaluated the dataset using additional IQA metrics not involved in the selection and compared it with other human-related datasets. As shown in the table below, PERSONA achieves the best overall quality and the highest RAM++ category diversity, indicating not only consistently high data quality but also richer semantics.\n\n|Dataset|BRISQUE[5]↓|HyperIQA[6]↑|TOPIQ_NR[7]↑|LIQE[8]↑|RAM++ Categories↑|\n|-|-|-|-|-|-|\n|VOC|21.28|0.608|0.608|4.241|2759|\n|iDesigner|25.80|0.632|0.647|4.388|1167|\n|DeepFashion|42.19|0.639|0.650|4.681|2496|\n|CrowdHuman|20.43|0.531|0.525|3.690|2220|\n|**PERSONA (Ours)**|**10.38**|**0.652**|**0.661**|**4.878**|**3365**|\n\n[5] Mittal et al., Blind/referenceless image spatial quality evaluator, Asilomar Conference on Signals, Systems, and Computers, 2011\n\n[6] Su et al., Blindly assess image quality in the wild guided by a self-adaptive hyper network, CVPR, 2020\n\n[7] Chen et al., TOPIQ: A top-down approach from semantics to distortions for image quality assessment, IEEE TIP, 2024\n\n[8] Zhang et al., Blind image quality assessment via vision-language correspondence: A multitask learning perspective, CVPR, 2023\n\n---\n`Q1-4:` Since the test set only uses VOC, does it fully reflect the diversity of the training data?\n\n`A1-4:`\nThe VOC dataset contains images that generally exhibit more severe degradations compared to more recent datasets. Thus, we consider it suitable for evaluating the model's restoration capabilities. We appreciate the suggestion and will include more diverse sources in the public release to enhance test set representativeness.\n\n---\n`Q1-5:` Evaluation is only on PERSONA. Would testing on existing datasets better demonstrate generalizability?\n\n`A1-5:`\nThank you for your thoughtful questions. Our proposed PERSONA dataset is the first publicly available benchmark specifically designed for human body restoration. Previous human body restoration methods [5, 6] have not provided public access to their test datasets. We hope that the release of the PERSONA dataset and benchmark will facilitate further contributions to the machine learning and computer vision communities.\n\n[5] Zhang et al., Diffbody: Human body restoration by imagining with generative diffusion prior, arXiv:2404.03642, 2024\n\n[6] Wang et al., Prior based pyramid residual clique network for human body image super-resolution, Pattern Recognition, 2024\n\n---\n`Q1-6:` There are some minor issues that need to be addressed:\n1. The full name of SOTA is missing in Line 107.\n2. The subcaption of Fig. 2 should be \"SinSR\" instead of \"Sinsr.\"\n3. Some citation formats should be revised.\n4. Mathematical expressions should be consistent: \"z_L\" is used in Line 226, while \"Z_L\" appears in Fig. 4.\n5. LoRA is not cited.\n\n`A1-6:`\nThank you for pointing out these issues. We'll carefully revise them.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "NA, there is not Theoretical Claim.", "experimental_designs_or_analyses": "Yes.", "relation_to_broader_scientific_literature": "Some of the key contributions of the paper are from the previous works such as [1].\n\n[1] Wu, R., Sun, L., Ma, Z., and Zhang, L. One-step effective diffusion network for real-world image super-resolution. In NeurIPS, 2024a.", "essential_references_not_discussed": "The authors have cited the related works that are essential to understanding the key contributions.", "other_strengths_and_weaknesses": "#### **Strengths**  \n\n1. The effort in constructing a large-scale dataset specifically for human body restoration is commendable. The proposed HQ-ACF pipeline effectively leverages existing datasets to curate high-quality human images, addressing the scarcity of benchmark datasets in this domain.  \n\n2. The proposed OSDHuman model achieves state-of-the-art performance on the newly introduced PERSONA dataset, demonstrating its effectiveness in restoring human images with improved visual quality and quantitative metrics.  \n\n#### **Weaknesses**  \n\n1. The novelty of the proposed approach is somewhat limited. The concept of the VSD is directly adapted from a previous work [1], and the HFIE could be seen as an attention-based variant of the DAPE framework. More justification and discussion on the unique contributions of the method would strengthen the paper.  \n\n2. The degradation types applied in the dataset are limited and not thoroughly discussed. If the dataset primarily uses blind super-resolution from [2] as its degradation process, it would be more accurate to frame the problem as \"human body super-resolution\" rather than the broader term \"human body restoration.\" A broader range of degradations would enhance the dataset’s applicability.  \n\n3. The superiority of the dataset is claimed based on its improved IQA values, yet the dataset construction process involves discarding images with lower IQA scores. This raises concerns about potential bias in evaluation and weakens the contribution of the HQ-ACF pipeline. A more transparent discussion on dataset selection criteria and its impact on evaluation would be beneficial.  \n\n4. The test set is entirely sourced from the VOC dataset, while the training set is compiled from multiple datasets. This discrepancy could lead to biased evaluations, as the test set may not fully represent the diversity of the training data. A more diverse and representative test set would provide a better assessment of model generalization.  \n\n5. The evaluation is limited to the newly introduced PERSONA dataset, without testing on existing human restoration or super-resolution datasets. Assessing the model’s performance on established datasets would better demonstrate its generalizability and highlight its advantages over prior methods. \n\n[1] Wu, R., Sun, L., Ma, Z., and Zhang, L. One-step effective diffusion network for real-world image super-resolution. In NeurIPS, 2024a.\n\n[2] Wang, X., Xie, L., Dong, C., and Shan, Y. Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In ICCV, 2021b.", "other_comments_or_suggestions": "There are some minor issues that need to be addressed:  \n\n1. The full name of SOTA is missing in Line 107.  \n2. The subcaption of Figure 2 should be \"SinSR\" instead of \"Sinsr.\"  \n3. Some citation formats should be revised. For example, \"(Liu et al., 2021a) represent the texture details of the human body using ...\"  \n4. Mathematical expressions should be consistent: \"z_L\" is used in Line 226, while \"Z_L\" appears in Figure 4.  \n5. LoRA is not cited.", "questions_for_authors": "Please refer to Weakness.", "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": "YsJw0B6kjA", "reviewer": "Reviewer_pp6W", "summary": "This paper presents a dataset automated cropping and filtering pipeline and constructs a person-based restoration with sophisticated objects and natural activities dataset. A novel one-step diffusion model is proposed for human restoration. Experimental results demonstrate the effectiveness.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "`Q2-1`: It is necessary to show the inference speed of different algorithms. This is the reason why one-step diffusion models are used.\n\n`A2-1:`\nThank you for your thoughtful suggestion. We have provided a detailed comparison of inference speed, parameter count, and computational cost for several recent diffusion models in the supplementary materials. For your convenience, the table below presents the same results:\n| Methods| DiffBIR | SeeSR | PASD | ResShift | SinSR | OSEDiff | **OSDHuman (Ours)** |\n|----|---------|-------|------|----------|-------|---------|-------|\n| Step | 50 | 50 | 20 | 15 | 1 | 1 | 1 |\n| Time (s) ↓| 9.03 | 5.05| 3.15 | 2.88| 0.19| 0.13| **0.11** |\n| Param (M) ↓| 1717 | 2524 | 1900 | 119 | 119 | 1775 | 1576 |\n| MACs (G) ↓| 24234 | 65857 | 29125 | 5491 | 2649 | 2265 | **2200** |\n\n---\n`Q2-2:` In ablation experiments, the proposed components did not always improve on all metrics. It is recommended to add visual comparisons to show the effectiveness of the proposed components.\n\n`A2-2:`\nThank you for your suggestion. Regarding the observation that the proposed component (HFIE) did not always improve on all metrics in the ablation study (Table 4), we have discussed this in detail in our response to `Q4-7`. We believe that HFIE is not inferior to other methods under the MANIQA metric.\n\nWe appreciate your suggestion and will include additional visual comparisons to illustrate the effectiveness of each component. You can view the visual comparisons at the [anonymous GitHub link](https://anonymous.4open.science/r/Submission_Number-2750-3BA1).\n\n---\n`Q2-3:` This paper aims to present the dataset and the corresponding methodology for human body restoration. However, the comparison of the visualizations in Figs. 8 and 9 is still focusing on the face region and the gaps in other human body regions are not significant. I am concerned about the value of this study and how it differs from face restoration.\n\n`A2-3:`\nThanks for raising this concern. Firstly, the reason we focus on faces in Figs. 8 and 9 emphasizes our method's effectiveness in restoring small-scale facial details. Since humans are particularly sensitive to facial perception, improvements in facial regions are noteworthy. Secondly, our method does not solely focus on faces; it also achieves significant restoration results for other body regions and background areas. Additional visual comparisons demonstrating these broader improvements can be found in the supplementary materials.\n\n---\n`Q2-4:` Comparisons with non-diffusion restoration models could be added.\n\n`A2-4:`\nThank you for your suggestion. We will add additional comparisons with classic non-diffusion restoration models. The test set comparison results are shown in the table below:\n| Methods| CLIPIQA↑| MANIQA↑| MUSIQ↑| NIQE↓|\n|---|----|---|---|---|\n| Real-ESRGAN[1]| 0.4721 | 0.6159| 67.7610 | 4.4390 |\n| BSRGAN[2] | 0.5307 | 0.6159 | 70.8345 | 4.4474 |\n| SwinIR[3] | 0.4847 | 0.6240 | 69.7636 | **4.0014** |\n| DAT[4] | 0.3194 | 0.3497 | 27.8132 | 8.3351 |\n| HAT[5] | 0.3936 | 0.5563 | 52.5818 | 6.4262 |\n| **OSDHuman (Ours)** | **0.7155** | **0.6977** | **73.7694** | 4.1287 |\n\nThese results demonstrate the effectiveness of our proposed method compared to existing non-diffusion approaches.\n\n[1] Wang et al., Real-ESRGAN: Blind super-resolution with pure synthetic data, ICCV, 2021\n\n[2] Zhang et al., Designing a practical degradation model for deep blind image super-resolution, ICCV, 2021\n\n[3] Liang et al., SwinIR: Image restoration using Swin transformer, ICCVW, 2021\n\n[4] Chen et al., Dual aggregation transformer for image super-resolution, ICCV, 2023\n\n[5] Chen et al., Activating more pixels in image super-resolution transformer, CVPR, 2023", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "No theoretical proofs involved.", "experimental_designs_or_analyses": "The experimental design and analysis is sound.", "relation_to_broader_scientific_literature": "Related to the diffusion models and blind image restoration literature.", "essential_references_not_discussed": "Essential references are discussed.", "other_strengths_and_weaknesses": "Pros:\n\n1. This work is well organized and well written. \n\n2. A new dataset is proposed which is of research value.\n\n3. Experimental presentations are extensive.\n\n\nCons:\n\n1. It is necessary to show the inference speed of different algorithms. This is the reason why one-step diffusion models are used.\n\n2. In ablation experiments, the proposed components did not always improve on all metrics. It is recommended to add visual comparisons to show the effectiveness of the proposed components.\n\n3. The aim of this paper is to present the dataset and the corresponding methodology for human body restoration. However, the comparison of the visualizations in Figures 8, 9 is still focusing on the face region and the gaps in other human body regions are not significant. I am concerned about the value of this study and how it differs from face restoration.", "other_comments_or_suggestions": "1. Comparisons with non-diffusion restoration models could be added.", "questions_for_authors": "Please see weakness.", "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": "jYj9Nth03X", "reviewer": "Reviewer_NGq9", "summary": "The paper proposes a novel approach to human body restoration (HBR) by introducing OSDHuman, a one-step diffusion (OSD) model, and a new benchmark dataset named PERSONA. The authors develop a high-quality dataset automated cropping and filtering (HQ-ACF) pipeline to create PERSONA, which comprises 109,052 high-resolution (512×512) human images covering diverse natural activities and complex interactions. This dataset outperforms existing human-related datasets in quality and richness, addressing the lack of task-specific benchmarks for HBR. OSDHuman incorporates a high-fidelity image embedder (HFIE) to extract precise prompts from low-quality (LQ) images, avoiding misleading guidance, and employs a variational score distillation (VSD) regularizer to align generated outputs with natural image distributions.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "`Q3-1:` The paper's innovation seems limited, as both the model and dataset build on existing work.\n\n`A3-1:` \nThanks for raising this concern. OSDHuman is the first one-step diffusion model applied to human body image restoration. Compared to traditional multi-step models, it achieves faster inference and lower computational cost, making it practical for real-world scenarios such as group photos or compressed chat images. To reduce bias introduced by external guidance modules, we introduce HFIE, which enables end-to-end training with lightweight and unbiased prompts. For more details on the novelty of HFIE, please refer to `A1-1`.\n\nThe PERSONA dataset is also the first large-scale benchmark for this task, addressing prior limitations such as single-person bias, fixed poses, and narrow aspect ratios. It covers diverse poses, interactions, and real-world scenarios and will be open-sourced to support the community.\n\n---\n`Q3-2:` The assumption that one denoising step can effectively restore complex human images lacks rigorous theoretical justification, and there is no formal analysis of the HFIE’s attention mechanism or its convergence properties under severe degradation. \n\n`A3-2:` \nThanks for raising this concern. One-step diffusion has been widely explored in image generation and restoration. Recent works [1, 2] distill multi-step models into one step, while methods like SinSR and OSEDiff use one-step strategies for super-resolution. Since body images share similar complexity with natural images, applying one-step diffusion is theoretically reasonable.\n\nThe attention mechanism of HFIE can be understood as follows: It encodes 145 embeddings into 77 vectors required by Stable Diffusion 2.1. A learnable query attends to these embeddings via softmax, producing a weighted sum that preserves both local and global information. As a convex combination in the original feature space, this ensures stable and effective guidance during training.\n\nThe low-quality images from our degradation pipeline are heavily degraded. OSDHuman with HFIE trains stably on them and achieves good convergence. Compared to DAPE, HFIE leads to faster loss reduction:\n\n|Step|L2 Loss (DAPE → HFIE)|LPIPS (DAPE → HFIE)|\n|-|-|-|\n|10k|0.062 → 0.043|0.843 → 0.771|\n|20k|0.049 → 0.042|0.771 → 0.757|\n|30k|0.047 → 0.041|0.759 → 0.750|\n|40k|0.047 → 0.042|0.753 → 0.753|\n\nLoss visualizations are available at the [anonymous GitHub link](https://anonymous.4open.science/r/Submission_Number-2750-3BA1).\n\n[1] Liu et al., Flow straight and fast: Learning to generate and transfer data with rectified flow, ICLR, 2023.\n\n[2] Yin et al., One-step diffusion with distribution matching distillation, CVPR, 2023.\n\n---\n`Q3-3:` The article lacks more theoretical analysis of the one-step strategy. Is such a one-step strategy better than multiple steps? Can more theoretical proof be provided?\n\n`A3-3:` \nThanks for your feedback. One-step diffusion offers a practical trade-off between performance and efficiency, achieving results comparable to multi-step models with much lower latency (see `A2-1`). This is made possible by strong base models with good generalization, which are well-suited for tasks like human body restoration.\n\nAs for theoretical reasoning, our work focuses more on introducing a new benchmark and model design rather than formal theoretical proof. We are also eager to see further theoretical development on one-step diffusion models, which would benefit the machine learning community.\n\n---\n`Q3-4:` In addition, from a visual comparison, the visual effect of OSEDiff is obviously better than the proposed method, especially it looks more natural. Why is this?\n\n`A3-4:`\nThanks for your questions. In rare cases, our results may look less natural due to specific degradations, sometimes causing color shifts. This can be mitigated via post-processing like wavelet-based correction [3], as described in more detail in `A4-1`. We will continue to refine our model to improve visual consistency, especially in challenging scenarios.\n\nOverall, our method outperforms OSEDiff in preserving fine facial details and natural tones. For example, in Fig. 2, expressions like subtle smiles are better retained, while OSEDiff may distort them and introduce unnatural reddish hues, indicating weaker perceptual consistency.\n\n[3] Mallat, Stephane G., A theory for multiresolution signal decomposition: the wavelet representation, TPAMI, 1989.\n\n---\n`Q3-5:` The lack of ablations on varied degradations (e.g., motion blur, compression) weakens the evidence for HFIE’s effectiveness.\n\n`A3-5:` The degradation model we used, Real-ESRGAN, includes several common degradation types, such as downsampling, noise, blur, and JPEG compression. We appreciate your suggestions, and we plan to explore additional degradation types to guide the model to perform better in more natural scenarios. We will experiment with motion blur and continue to explore the model’s effectiveness across a wider range of scenarios.", "claims_and_evidence": "Almost correct.", "methods_and_evaluation_criteria": "Almost correct.", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Please refer to weaknesses.", "relation_to_broader_scientific_literature": "The key contributions of the paper relate to the broader scientific literature by addressing the lack of HBR-specific benchmarks, building on diffusion model advancements, and improving one-step restoration efficiency.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths:\n\n1. The introduction of the PERSONA dataset addresses a critical gap in human body restoration (HBR) research by providing a high-quality, diverse benchmark with 109,052 images, surpassing existing human-related datasets in quality and richness.\n\n2. The paper provides a well-structured experimental evaluation with clear visual comparisons (e.g., Figure 9) and quantitative results (e.g., Tables 2–4), effectively showcasing OSDHuman’s superior visual quality and metric performance on the PERSONA dataset.\n\nWeaknesses:\n\n1. The paper’s innovation is relatively modest, as OSDHuman and the PERSONA dataset build incrementally on existing one-step diffusion techniques and dataset curation methods, offering no transformative advancements in the field of diffusion-based image restoration.\n\n2. The assumption that a single denoising step can effectively restore complex human images lacks rigorous justification, with no formal analysis of the HFIE’s attention mechanism or its convergence properties under severe degradation, potentially impacting the model’s reliability.\n\n3. The article lacks more theoretical analysis of the one-step strategy. Is such a one-step strategy better than multiple steps? Can more theoretical proof be provided?\n\n4. In addition, from a visual comparison, the visual effect of OSEDiff is obviously better than the proposed method, especially it looks more natural. Why is this?\n\n5. While OSDHuman’s performance claims are supported by experimental results, the lack of ablation studies on varied degradation types (e.g., motion blur, compression artifacts) weakens the evidence for the high-fidelity image embedder (HFIE)’s effectiveness.", "other_comments_or_suggestions": "In general, from the perspective of the contribution of the dataset, I think this article is valuable. However, from the perspective of the method, I think it lacks contribution and novelty, as well as detailed theoretical basis. But overall, I think it still has some contribution, so I give it a weak accept now, and I will adjust my score based on the rebuttal.", "questions_for_authors": "Please refer to weaknesses.", "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": "TKh9VXyeL6", "reviewer": "Reviewer_QwNC", "summary": "This paper introduces a high-speed diffusion model that can restore low-quality human body images in just one diffusion timestep. The paper presents a high-quality dataset, PERSONA, which includes diverse human body images. Additionally, the proposed OSDHuman model paves the way for incorporating visual priors into diffusion models for human body restoration. OSDHuman outperforms current state-of-the-art methods in terms of both quality and efficiency on benchmark datasets.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "`Q4-1:` However, the images restored from OSDHuman have some color shift. For example, in Figs. 5 and 6 of the supplementary materials, the teeth of the person in the 2nd and 5th sets of images are noticeably whiter.\n\n`A4-1:`\nThank you for pointing out the color shift issue. We address this by applying wavelet-based color correction [1], which combines the high-frequency details from the restored image with the low-frequency color components from the original input. This helps align color distributions while preserving structural details.\n\nWe plan to integrate this correction into the model for end-to-end learning of color consistency in future work. Your feedback is greatly appreciated.\n\n[1] Mallat, Stephane G. \"A theory for multiresolution signal decomposition: the wavelet representation.\" TPAMI 1989.\n\n---\n`Q4-2:` The LQ images in PERSONA-Val seem to have stronger Gaussian noise than those in PERSONA-Test. Could the degradation be made more realistic?\n\n`A4-2:`\nIn real-world scenarios like group photos, surveillance footage, or chat images, factors such as long shooting distance, compression, and low-end devices often lead to severe degradation. Our pipeline is designed to simulate such challenging cases, which may result in stronger noise than typical natural degradations.\n\nThank you for the suggestion. We will explore refining the pipeline by incorporating more realistic artifacts like motion blur in future work.\n\n---\n`Q4-3:` Some individuals in the dataset images occupy less than one-third of the 512×512 frame. Does this mean the resolution is too small for effective human body restoration?\n\n`A4-3:`\nThanks for raising this concern. Human body restoration is mainly used in portrait photography. In such cases, people care more about the harmony between the person and the background, not just the body or the scene alone. In everyday mobile photography, the human subject does not always take up a large portion of the image. Instead, good composition and visual balance are more important.\n\nThe PERSONA dataset uses square 512×512 images. This format works well for various poses, such as standing, sitting, crouching, or group interactions. It also helps blend the human subject with the background, requiring the model to restore both in a consistent and natural way. This is why we chose this design for the dataset.\n\n---\n`Q4-4:` Regarding the model, how scalable is it for images with larger resolutions?\n\n`A4-4:`\nThank you for your thoughtful questions. Our model scales well to high-resolution images using a tiled inference strategy. For example, we processed a 3472×4800 image by setting the VAE encoder tile size to 1024×1024 (32px overlap), latent tile size to 96×96 (32px overlap), and decoder tile size to 224×224. Inference ran on an A6000 GPU with a peak memory usage of 32 GB. You can visually inspect the detailed results using the **[anonymous link](https://imgsli.com/MzY0NzIw)**.\n\n---\n`Q4-5:`\n- In Table 2, the DISTS column shows that OSEDiff* performs better than OSEDiff. OSEDiff* should be marked in cyan.\n- In the second row of line 240, `1024` should be formatted as `1,024`.\n\n`A4-5:`\nThank you for pointing these out. We will make the necessary corrections.\n\n---\n`Q4-6:` From Table 2 and Table 4, it seems that using HFIE as a Prompt Extractor does not result in better performance for the MANIQA metric. Could the authors provide an explanation?\n\n`A4-6:`\nThank you for the observation. MANIQA is a no-reference IQA metric sensitive to the dataset it was trained on. In our paper, we used the PIPAL-trained version, which yielded lower scores for HFIE in Table 4. However, when using the MANIQA model trained on the KonIQ dataset, HFIE achieved the best performance in our ablation study. The results are shown below:\n\n|Type|From HQ|From LQ|MANIQA-PIPAL↑|MANIQA-KonIQ↑|Average↑|\n|-|-|-|-|-|-|\n|Null| | |**0.7226**|0.4430|0.5828|\n|DAPE|✔| |0.7014|0.4309|0.5662|\n|HFIE|✔| |0.6747|0.4718|0.5733|\n|HFIE| |✔|0.6977|**0.4829**|**0.5903**|\n\nThis highlights how dataset bias affects MANIQA's judgment. When averaging across both metrics, HFIE performs best, further confirming its effectiveness for human body restoration.\n\n---\n`Q4-7:` The authors froze the timestep of the OSD model to 999. I wonder whether this parameter has any effect on the results. Could the authors clarify this?\n\n`A4-7:`\nThank you for your question. In one-step diffusion models, the timestep mainly determines the initial noise level. Since the model performs only a single denoising step, this parameter has limited impact on performance. Fixing it (e.g., to 999) is a common practice, and the model is then fine-tuned to adapt to this noise level.", "claims_and_evidence": "The contributions of this paper are primarily divided into two parts: the PERSONA dataset and the OSDHuman model.\n\nRegarding the dataset, the authors propose a High Quality Human Dataset Pipeline, which uses label filtering, object detection, blur awareness, and IQA filtering to create a dataset of 109,052 high-quality images. The process for constructing this dataset is reasonable, and it would be beneficial for the authors to make it publicly available to contribute to the computer vision and machine learning communities.\n\nRegarding the model OSDHuman, the authors introduce a high-fidelity image embedder (HFIE) and use VSD regularization as guidance. The effectiveness of this approach is demonstrated through Ablation Studies in Table 4. Experimental results demonstrate it perform well in both visual quality and quantitative metrics.", "methods_and_evaluation_criteria": "Pros: \nThe proposed methods and evaluation criteria are meaningful. The PERSONA dataset fills the gap of lacking publicly available high-resolution open-source datasets for human body images. The proposed model also addresses the gap in portrait photography restoration.\n\nCons: \nHowever, the images restored from OSDHuman have some color shift. For example, in Figures 5 and 6 in the supplementary materials, the teeth of the person in the 2nd and 5th sets of images are noticeably whiter.", "theoretical_claims": "The formulas appear to be correct, with no obvious issues.", "experimental_designs_or_analyses": "Pros: \nIn the comparative experiments, the authors retrained the SinSR and OSEDiff models on the PERSONA dataset. The results in Table 2 show that after retraining with PERSONA-train data, the models achieve better performance in human body restoration.\n\nCons: \nFrom the visual images, it can be seen that the LQ images in PERSONA-Val differ in noise compared to the LQ images in PERSONA-Test. It appears that the Val dataset contains much more severe Gaussian noise than real-world situations. Although the authors mention that the LQ images in the Val dataset are generated using the same degradation pipeline as the training data, could it be more realistic?", "relation_to_broader_scientific_literature": "Human body image restoration has many applications in photography, especially in mobile photography. However, most previous image restoration research has focused on either natural images or faces, such as StableSR[1], SUPIR[2], SinSR[3], DiffBIR[4], and OSEDiff[5]. These models may not perform well for tasks specific on human body images. \n\nAs for datasets targeting human bodies, most existing datasets are for fashion purposes [6-7], and there has been a lack of high-resolution portrait datasets for real-world scenes. The dataset proposed in this paper is significant for training more specialized portrait photography image restoration models.\n\n[1] Exploiting Diffusion Prior for Real-World Image Super-Resolution\n[2] Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild\n[3] SinSR: Diffusion-Based Image Super-Resolution in a Single Step\n[4] DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior\n[5] One-Step Effective Diffusion Network for Real-World Image Super-Resolution\n[6] Large-scale Fashion (DeepFashion) Database\n[7] SCAM! Transferring humans between images with Semantic Cross Attention Modulation", "essential_references_not_discussed": "The related works cited in the paper are comprehensive.", "other_strengths_and_weaknesses": "Pros:\n1. The dataset covers many categories, as shown in Figure 7. It is of high quality and rich in diversity.\n2. The efficiency of human body restoration is crucial for applications. The proposed method addresses a practical problem with an efficient solution and favorable qualitative results.\n3. Unlike image-to-tag models that generate tags as textual prompts, HFIE directly tokenizes each image, eliminating information loss during the image-tag-embedding process, which can improve fidelity, as mentioned in the supplementary material.\n4. The paper is clearly written, easy to follow, and presents a well-motivated and reasonable argument.\n\nCons:\n1. The datasets presented in the article consist of 512x512 size images. Since some of the images do not have the person occupying a large area of the image, does this mean the resolution of these images is too small for the human body? For example, in Figure 6, most individuals occupy less than one-third of the image. Does this imply that the dataset is still not high definition enough?\n2. Regarding the model, how scalable is it for images with larger resolutions?", "other_comments_or_suggestions": "1. In Table 2, the DISTS column shows that OSEDiff\\* performs better than OSEDiff. OSEDiff\\* should be cyan.\n2. In 2nd row of line 240, `1024` should be `1,024`.", "questions_for_authors": "1. From Table 2 and Table 4, it seems that using HFIE as a Prompt Extractor does not result in better performance for the MANIQA metric. Could the authors provide an explanation?\n2. The authors froze the timestep of the OSD model to 999. I wonder whether this parameter has any effect on the results. Could the authors clarify this?", "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:

Meta-review: The paper received all accept recommendations. AC agrees with this recommendation and therefore is happy to accept the paper. Please address reviewer comments to the camera-ready version of the paper.
Decision: Accept (poster)