HAODiff: Human-Aware One-Step Diffusion via Dual-Prompt Guidance
Authors: Jue Gong
Tingyu Yang
Jingkai Wang
Zheng Chen
Xing Liu
Hong Gu
Yulun Zhang
Xiaokang Yang
Venue: NeurIPS 2025 poster ()
Keywords: Low-level Vision
Diffusion Model
Human Body Restoration
Primary area: applications
TL;DR: A novel one-step diffusion model for human body restoration, efficiently handling human motion blur and generic noise in human images.

Abstract: Human-centered images often suffer from severe generic degradation during transmission and are prone to human motion blur (HMB), making restoration challenging. Existing research lacks sufficient focus on these issues, as both problems often coexist in practice. To address this, we design a degradation pipeline that simulates the coexistence of HMB and generic noise, generating synthetic degraded data to train our proposed HAODiff, a human-aware one-step diffusion. Specifically, we propose a triple-branch dual-prompt guidance (DPG), which leverages high-quality images, residual noise (LQ minus HQ), and HMB segmentation masks as training targets. It produces a positive–negative prompt pair for classifier‑free guidance (CFG) in a single diffusion step. The resulting adaptive dual prompts let HAODiff exploit CFG more effectively, boosting robustness against diverse degradations. For fair evaluation, we introduce MPII‑Test, a benchmark rich in combined noise and HMB cases. Extensive experiments show that our HAODiff surpasses existing state-of-the-art (SOTA) methods in terms of both quantitative metrics and visual quality on synthetic and real-world datasets, including our introduced MPII-Test. Code is available at: https://github.com/gobunu/HAODiff.

HAODiff: Human-Aware One-Step Diffusion via Dual-Prompt Guidance
Jue Gongl Tingyu Yang"; Jingkai Wang', Zheng Chen', Xing Liu?, Hong Gu², Yulun Zhang'? Shanghai Jiao Tong University
Xiaokang Yang' vvivo Mobile Communication Co., Ltd
Abstract
Human-centered images often suffer from severe generic degradation during trans- mission and are prone human motion blur (HMB), making restoration challeng- ing. Existing research lacks sufficient focus on these issues, as both problems often coexist in practice. To address this, we design degradation pipeline that simulates the coexistence of HMB and generic noise, generating synthetic degraded data to train our proposed HAODiff, a human- aware one-step diffusion. Specifically, we propose ciiple-branchl dual-prompp prompt guidance (DPG), which leverages high-quality images, residual noise (LQ minus HQ), and HMB segmentation masks training targets. It produces positive-negative prompt pair for classifier-free guidance (CFG) in a single diffusion step. The resulting adaptive dual prompts HAODiff exploit CFG more effectively, boosting robustness against diverse degradations. For fair evaluation, we introduce Mest-Test, benchmark rich in combined noise and HMB cases. Extensive experiments show that our HAODiff surpasses existing state-of- oftth--rttttttttt (SOTA) methods terms of both quantitative metrics and visual quality synthetic and real-world datasets, including introduced MPII-Test Code is available at: https: //gi tithu.. com/ het//gobuuu/HAADO /HAODiff.
Introduction
Human body restoration (HBR) focuses on recovering high-quality (HQ) images from luw-iuality (LQ) inputs that contain human images. When human subjects appear prominently in image, naturally attracts more viewer attention. However, real-world images frequently undergo degradation during capture transmission, including human motion blur, noise, resolution loss, and JPEG compression artifacts. These distortions significantly hinder the recognition of human activities and limit the usefulness of such images in broader applications. As result, many downstream tasks related to humans are negatively affected, such as 3D reconstruction [46, 64], human pose estimation [41, 71], and human-object interaction detection [53, 31].
To achieve better performance in practical scenarios, current blind image restoration (BIR) models typically rely on large number of paired LQ and HQ images accurately learn the complex mapping between LQ and HQ domains. Models in the HBR field follow the same principle. However, collecting large number of real LQ-HQ image pairs is challenging. Real-world LQ images often undergo various unknown degradations, which may occur during transmission or even at the time of capture. Therefore, BIR models usually adopt degradation pipeline to simulate real-world LQ conditions. This strategy allows models to fully leverage large-scale high-quality image restoration datasets, such as LSDIR [27], FFHQ [18], and PERSONA [1]]. In the context of HBR, existing approaches [11, 70] generally use generic degradation types, including downsampling. compression, noise, and low-pass blur. A commonly adopted pipeline is from Real-ESRGAN [51], which is originally developed for blind super-resolution tasks. However, this pipeline may fail to cover the diverse degradation types that frequently occur in human-centrie images.
"Equal contribution.
Correspondence author: Yulun Zhang,
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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CLIPOA
Among the various degradation types in human- centric images, human motion blur is particu- larly prevalent, yet it remains significantly un- derrepresented in existing degradation pipelines. Motion blur can be broadly categorized into two types. The first category is global motion blur, typically caused by camera movement dur-
MANIGA
HMBBR R
ing exposure. The second is local motion blur, caused by the rapid movement of objects in the scene. In human images, local motion blur mainly arises from human movement, known as
SeeGR
SUPIR
PASD
RasShnn
RAIGE
SinSR
MICE
human motion blur (HMB). Extensive research is conducted on the removal of global motion blur. For example, DeblurGAN [22] not only proposes effective model for motion deblur- ring, but also provides a simulation pipeline that can generate realistic global motion blur. This enables training with large-scale synthetic data. In contrast, the restoration of local motion blur is typically based on real datasets, with ReLoBlur [24] being representative example that covers diverse object motion. However. these datasets often focus on general objects and pay less attention to human-specific motion blur. Moreover, HMB in real-world human images often co-occurs with other degradation types. Existing deblurring models are usually designed to handle single degradation, which limits their applicability in more complex, realistic scenarios.
OSEDif
InSRR
OSDHumen
HAODM (ours)
LIQE
Figure I: Performance comparison on introduced MesI-Test. HMB-R denotes the ratio of human motion blur detection instances before and after restoration. Lower-is-bette metries are inverted.
Multiple factors, including training data and degradation pipelines, influence the quality of image restoration. Among them, the model architecture plays a critical role. In BIR tasks, models require strong generative capabilities to reconstruct damaged regions. Generative adversarial networks (GANs) [12] are considered one of the major starting points for modern image generation models. They have led to the development of many powerful restoration methods [65, 22, 51]. However, GANs often suffer from training instability and difficulties in controlling the generation process. Diffusion models and latent diffusion models (LDMs) [39] have further improved the framework for both generation and restoration. Some models [29, 59, 55, 62] use multi-step diffusion processes to restore high-quality images from heavily degraded inputs. Recently, one-step diffusion models [54, 49, 52, 61] also demonstrate strong BIR performance while significantly reducing resource consumption compared to multi-step diffusion. These models commonly extract object features from images and convert them into text image embeddings. They are used as positive prompts to guide the model toward faithful restoration. To better leverage the guidance capabilities of text-to-image (T2I) foundation models, some methods [63, 60] introduce negative prompts using classifier-fi ffree guidance (CFG) [15]. These prompts help steer the model away from undesired content. However, these methods adopt fixed negative prompts, which limit their guidance effectiveness.
To address these limitations, we propose HAODiff, novel one-step diffusion for human body restoration (HBR) that integrates CFG via dual-prompt guidance (DPG). Firstly, to compensate for the lack of human motion blur (HMB) in existing pipelines, we propose a new one. In the preprocessing stage, we apply human body-part segmentation model to the HQ training images to obtain part-specific masks. These masks are then combined with a motion blur simulation module to synthesize HMB. Integrated into generic two-stage pipeline, our approach allows the inclusion of HMB. Secondly, we design a triple-brance branch dual dual-prompp guidance, named DPG, which is based on the Swin Transformer [33]. During training with the degradation pipeline, one branch of DPG predicts the HQ image to produce the positive prompt. The other two are used to predict the residual noise (LQ minus HQ) and the human motion blur segmentation mask, both serving sources for the negative prompt. Thirdly, we propose a human-aware one-step diffusion via dual-prompt guidance, named HAODiff. The model accepts both positive and negative prompts generated by DPG. With the CFG strategy, the negative prompt replaces the unconditional input and guides the model to learn how to approach HQ features while avoiding LQ characteristics. In addition, further evaluate performance under both generic and HMB degradations, we construct a new benchmark, MPII- Test. It is curated from the MPII Human Pose dataset [1] and contains 5,427 real-world degraded human images, many of which include rich motion blur patterns. As shown in Fig. 1, the results demonstrate our model's 's strong ability to restore human images with both HMB and other degradations.
2

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In summary, we make the following four key contributions:
We propose new degradation pipeline for human body restoration. It explicitly incorporates human motion blur into the degradation process, enabling the model to learn from more realistic degradation scenarios and improving its generalization in real-world applications.
We propose HAODiff, a one-step diffusion model that integrates the classifier-fres guidance strategy. By replacing fixed input with adaptive negative prompts, the model is guided toward HQ features and away from I.Q signals, enhancing robustness to noise.
We design a prrmmp guidance (DPG), which efficiently generates distinct positive and negative prompts for each LQ image. This addresses the challenge of constructing targeted negative prompts and provides spatial guidance on the location of human motion blur.
Our proposed method, HAODiff, achieves significant state-of-the-ard performance on both existing test datasets and our newly introduced benchmark. It delivers strong visual quality and competitive quantitative results while maintaining high computational efficiency.
2 Related Work
2.1 Human Body Restoration
Human body restoration (HBR) constitutes specialized subfield that applies blind image restoration (BIR) techniques specifically to the restoration of LQ human images. Contemporary mainstream BIR methods [28, 51, 65, 29] show remarkable efficacy across a wide range of natural scenarios. However, their direct application to human body images frequently causes joint misalignment and limb distortion. Previous models [32, 50, 70, 11] have addressed this task with varying methodologies. Among them, DiffBody [70] pioneers the application of diffusion model in body-region image enhancement under the guidance of the body attention module. A notable contribution to the field is OSDHuman [11], which proposes a one-step diffusion model for human body restoration using a high-fidelity image embedder while introducing the PERSONA benchmark dataset.
Human motion blur represents one of the most challenging degradation modalities in HBR contexts. Current deep-learning based deblurring research primarily focuses on objects and scenes [36, 6, 45, 22, 3]. DeblurGAN [22] represents the inaugural application of conditional adversarial network to blind motion deblurring and introduced a random- trajectory simulation pipeline for synthesizing motion- blurred data. More recently, OSDD [30], a one-step diffusion model for motion deblurring that substantially enhances the computational efficiency of diffusion based restoration. Moreover, ReLoBlur [24] provides the first real-world locally blurred dataset captured with synchronized light-field cameras, while developing a Blur-Aware Gating network to restore these regions.
2.2 Diffusion Models
Text-to-image (T2I) diffusion models are repurposed for image restoration tasks due their powerful prior knowledge in image generation [48, 29, 55, 60, 59]. For instance, Stable Diffusion (SD) [43], with scalable networks and controllable generation, demonstrates the capability to inject vivid details into LQ images. Building on this foundation, StableSR [48] enhances image restoration via a fine-tuned time-aware encoder and progressive sampling strategies. By implementing degradation- aware prompt extractor, SeeSR [55] guides diffusion models to generate semantically accurate HQ images with precise prompt control. DiffBIR [56] first employs an initial restoration module before incorporating SD for detail refinement. Furthermore, SUPIR [60] leverages SDXL [38] as its prior, achieving impressive results through high-quality datasets and innovative positive- sample strategies. However, their adherence to conventional T2I diffusion paradigms necessitates numerous sampling steps, resulting in computational inefficiency and excessive parametric complexity.
Contemporary research increasingly focuses on addressing the inefficiencies of multi-stpp diffusion processes in BIR tasks by leveraging more efficient one-step diffusion [52, 54, 63, 61]. SinSR [52] advances this direction by distilling deterministic mapping from a teacher diffusion model. OSED- iff [54] injects LQ images into the latent space as the diffusion starting point and employs variational score distillation to align with the image prior of SD. More recently, based on diffusion inversion, InvSR [61] uses noise prediction to create optimal intermediate sampling states. Nevertheless, they insufficiently exploit the semantic information inherent within the LQ images themselves. The global semantic content of LQ images can be effectively processed and utilized as prompt embeddings for diffusion models, substantially enhancing one-step diffusion restoration capabilities.
3

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1st Order
O (1
Ws)
HMR
M
Segment M Module
Morpho- logical
I
PSP
O W3
anndom Trajectory
B lur
Resize
Noise
Compression
Compression
Noise
Resize
lur
2nd Order
Figure 2: Degradation pipeline overview. The first order contains three possible cases: (i) no degradation, (ii) human motion blur (HMB), and (iii) generic degradation. The HMB branch conducts body-part segmentation to obtain masks, then morphs them to yield the spatial weight map W,. This map is applied the motion blur image In. which generated by convolving the clean image Ir with point spread function (PSF) derived from a random trajectory. The result is combined with I create the synthetic HMB image HMM The second applies conventional generic degradation.
Method
3.1 Degradation Pipeline with Human Motion Blur (HMB)
Obtaining paired training data where degradation incorporates HMB remains a significant challenge. Prior works [36, 37, 24] construct global local motion blur by capturing high-quality continuous frames from high frame-rate videos and averaging them to synthesize motion blur. However, this strategy struggles to cover the diversity of real-world human activities, limiting the generalizability of the dataset. Another method involves convolving natural images with blur kernels generated from complex motion trajectories [4, 44, 57, 21, exemplified by DeblurGAN [22]. Building upon these methods, we design a degradation pipeline that simulates human motion blur and incorporates generic degradation processes based on
[51] to train for human body restoration.
The pipeline is illustrated in Fig. 2. the first order degradation, we use Sapiens [19] for body segmentation HQ images In. yielding part masks. These masks grouped into six categories (head, left/right upper limbs, left/right lower limbs, and the whole body), from which one category is randomly selected for motion blur simulation in subsequent steps. Since segmented regions do not inherently correspond to realistic motion patterns, we apply morphological operations, including erosion, dilation, and Gaussian blurring, the selected masks. These processed masks are then normalized to generate spatial weight map W., which can be formalized as:
W (Norm o Morph Se with Morph = G D F,
(1)
where (-) refers the segmentation operation, and Morph(.) denotes morphological operations: Gaussian blur (G), dilation (D) and erosion (E). Norm(.) denotes scaling values to [0, 1].
In parallel, we generate globally blurred image using strategy similar to DeblurGAN [22]: continuous random trajectory is simulated via Markov process and converted to a discrete point spread function (PSF) through bilinear interpolation. The PSF is then convolved with the HQ image via FFT (fast Fourier transform, denoted as FF) convolution to produce global motion blur. Finally, we blend the original and blurred images using the spatial weight map, yielding the HMB image ДНмв:
JuMB W. IF (1 W.) @ Tn with IB J- (F(PSF) @ F(In)).
(2)
Notably, HMB is typically caused by the subject's movement during capture and should logically precede all other degradations in the simulation process. Therefore, to maintain logical consistency. we place HMB in the first-order stage. Since motion blur and severe degradations do not always occur, we define the first-order degradation HMB, or generic degradation. After this stage, the image undergoes second-order degradation, which includes common types such as blur, resizing, noise, and compression. The generic degradations in both the first and second orders follow the strategy of Real-ESRRAN [51]. Upon traversing this comprehensive degradation pipeline, obtain synthesized LQ images 1 for subsequent processing.
one of three possible conditions: no degradation,

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HAODiff
Stage 1. DPG
Hus
Enye Www.- Muиe
Hoa
Huy
4y (6)
RSTB
Hoo
4
Hoz.
4,00 (x)
Has
Stage 2. OSD
Prompt Embedder
Prompt Embeddeer
Thp
AMSE AEA
Aig
UNel
Zara
CFG
Pat
Decoder
Apos
VAE
Posttive Fath Trainaste
De
Nagative Path
Frazen
Loss Function
We train a triple-branch dual-prompt guidance (DPG). The core structure consists of downsampler and upsampler (H Hus), as well as feature extraction and reconstruction modules (HE and HR).. Both HE and H Ri are composed of two residual Swin Transformer blocks (RSTB). The three branches are individually trained with the human motion blur segmentation masks (MuMB), residual noise (4 - /t), and high-quality images (Iz). Stage 2: We leverage DPG combined with prompt embedder to provide positive and negative prompt pairs to the one-step diffusion (OSD) model. The UNet generates 2pos and Zneg, used to obtain the predicted latent vector & through classifier-fres guidance (CFG) and denoising operations.
Figure 3: Model structure of our HAODiff. Stage 1:
3.2 Stage 1. Dual-Prompt Guidanee (DPG)
key challenge in prompt extraction is effectively predicting high-quality (HQ) features from low-quality (LQ) images. OSDFace [49] tackles this via HQ-LQ embedding alignment. However, alignment alone insufficient because predicting HQ features from LQ involves restoration, which is challenging for prompt extractors. Directly training the prompt extractor with LQ inputs and HQ targets embeds HQ features within intermediate model layers. Additionally, for restoration models, defining the negative prompt is essential: the restored images should not completely diverge from the LQ input but rather specifically from the residual noise (LQ minus HQ), which contains degradation without structural information. Otherwise, the restored images may lack fidelity. Furthermore, local noise (e.g., human motion blur) hidden in global noise is hard to locate and remove. Existing methods [24, 25] use segmentation supervision to locate motion blur regions. A dedicated module that supplies explicit positional information can further enhance noise localization and removal.
DPG Structure. To provide the model with image embeddings as prompts, the Vision Trans- former [10, 33] framework suitable. can convert RGB images into embeddings for feature extraction. Furthermore, SwinIR [28] in image restoration, using composed residual Swin Trans- former blocks (RSTB) to recover HQ from LQ, achieves great success. Inspired by SwinIR, our prompt extractor structure is shown in Fig. 3. First, we use a convolutional structure (Hp). It downsamples the LQ image (IL) size 512 x 522 by 4. And it increases channels to match the sequence length by the Swin Transformer, which we set 150. Next, the backbone network (denoted Hg) extracts features through two RSTBs, with six Swin Transformer layers (STL). Afterward, the model splits into three reconstruction branches (denoted as HR, E [1, 3). Each branch also contains two RSTBs but only three STLs. Each branch then passes through convolutional upsampler (denoted as Hu., i E [1, 3)). The three branches are designed to predict the HQ image (f the residual noise between LQ and HQ (IR), and the HMB segmentation mask (ΛНмB). This process can be formulated as follows:
(ip R, (AMB) = ((Hv, (R)) with F (11 o (It)
(3)
Training Objective of DPG. The predicted HQ image (if) and the residual noise between LQ and HQ (1x) are optimized jointly using the pixel-wise L.1 loss L1. The HMB-aware branch, responsible for predicting the human motion blur (HMB) segmentation mask (Λμнм)) uses the Dice [8] loss LDke- The overall training objective can be formulated as follows:
L Li Li In) + Li (In, (Ir. In))
Liice ( (MHMB, MHMB),
(4)
where MuMn is obtained by binarizing W, from Sec. 3.1. The Dice loss is defined as:
Loie (MMMB. MμMB) = 2 M O MnMa!
2 summ MAMR @ MHMB
1
(5)
| + |
sum MAMB) + sum MMMM)
5

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3.3 Stage 2. One-Step Diffusion (OSD) Model
Model Structure. The latent diffusion model (LDM) [39] adds noise in latent space at timestep t as 2, v 2 v1 dr where AN 1) and ū; denotes the cumulative product of a, up to timestep I: 04 = [ - 8.). The reverse process predicts noise with parameter During inference, the LDM employs the DDIM [42] to accelerate the reverse process, simplified as follows:
24 v1 ai Eo (2)
+ ( â - of (z; () + o
2 V
V@z
where - 1 represents the next step in the DDIM sampling sequence. The of is defined as follows:
d4-1 (1
of 1/ 1 0z
7 E [0.
0/-1
In restoration tasks, for stability, we set 7-0, so 0,=0. With a specific timestep т. the latent vector 2n should correspond the noisy representation 25, encoded from the LQ image 1, by the variational autoencoder ( (VAE) [21] encoder Eg. t 1 should correspond to step 0 in OSD. Thus 0,- since the output is the noise-free latent vector 2g, also denoted as åh: 2H = (2t vI- de 2 Vāt. When the classifier-fre guidance (CFG) [15] is employed, the predicted noise 2g is defined as:
Zneg + Aefg (2pos Zneg) with 2pos (=(=Li T), Zneg EB (Z Pnog. T)
(8)
where Ppas and Pheg denote the positive and negative prompts provided to the model. The parameter Aetg controls the intensity of CFG, balancing the influence between dual predictions. The resulting
latent vector सØ is then passed through the VAE decoder Dy to obtain the restored image I
As for the prompt extractor part, after training the dual- prompt guidance (DPG) in Sec. 3.2, we further adapt it to be compatible with providing prompt embeddings. We extract the output feature from the last layer of the Hm: to obtain em- bedding features with the most informative representation. However, the size of this output significantly differs from the embedding size required by SD, and its feature space distribution also deviates from standard text embeddings. Therefore, before integrating into Stable Diffusion (SD), we apply nonlinear mapping and feature compression using a linear complexity Performer [7] Encoder and an attention pooling [23] module, denoted prompt embedder, as shown in Fig. 4. The features of the two negative branches are concate- nated and, with the positive branch, fed into different prompt embedders. This process yields dual-prompt embeddings Ppos and Pneg- Finally, by concatenating these embeddings along the batch size dimension, the receiving UNet [40] can efficiently obtain both zpos and zneg parallel.
Pos./ Neg. Prompt Pous Paos
Multi-Head Attention
Learnable Embedding
K
Linear
Feed-Forward
Performer Encoder O(N)
Liner Atlention
Hei Feature
Figure 4: Structure of the prompt em- bedder. The Attention Pooling uses learnable embedding as Q, while K and V from the output of Performer Encoder, whose depth N is set to
Training Objective. The human body restoration model aims to recover high-quality human images with rich details from degraded inputs. During training, we use pixel-level mean squared error loss LMSE to minimize reconstruction errors. To enhance edge responses, we also employ an edge-aware DISTS perceptual loss, denoted as LEA It is calculated by feeding both the original image and its edge-enhanced version, obtained using the Sohel operator S(.), into the DISTS [9] function:
LEA = Laists (i J + Laisss (Sisss(((
(S(im) S(T)))
Previous study [63] shows that even when the restored image is similar to the original, distortions may still occur in the latent vector distribution. To address this, utilize pre-trained SD UNet downsampling module Dj as a discriminator and calculate the generator loss Lg. It helps the model learn more realistic data distribution. The total loss function Lival is defined follows:
Liotal = LMSE( (i I1) + LEA (EA In) + 3 Lo(ân).
(10)
The generative adversarial network (GAN) [12] loss consists of the generator loss Lg and the discriminator loss LD. Following previous works [63, 49], we define them as follows:
(zH = EE, [log D. (F(εH, ())], (D( ²H) -E, [log (1 De (F(SH, f))) E, [log Dv (F(2H, t)]],
(11)
here, 2 represents the latent vector a high-quality human image Tr, and F(-) denotes the diffusion noise addition process, which is related to a randomly chosen timestep e [0,

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ResShif Figure 5: Visual comparison of the synthetic PERSONA-Val. Please zoom in for a better view.
HQ
SUPIR
SeeSR
PASD
OSEDiN
OSDHuman HAOD
Methods
Step
Time (4)
DISTSL
T.PPSS
TOPIQ1
FID,
C.IAA
M.IQAT
NIQEL
LIQ
II-NIQEL
SUPIR [60]
50
26.67
0.1415
0.2929
0,4080
13.8357
0.7908
0.6811
3,7769
4.2303
21.3221
DiMBIR [29]
50
9.03
0.1402
0.2797
0,4319
12,9306
0.7792
0.6965
3,9404
4,4211
21.4859
SeeSR [55]
50
5.05
0.1295
0.2555
0,4476
12.8225
0.7620
0.6979
3,5744
4,5722
25.1
PASD [59]
20
3.15
0.1469
0.2910
0,4383
15.6758
0.6121
0.6477
3.8022
4.0878
25.4919
ResShif [62]
15
2.88
0.1638
0,2848
0.4237
15.6502
0.5365
0.5650
5,2973
3.3929
25.3706
SinSR [52]
1
0.19
0,1579
0.2840
0.4037
16.9203
0.6037
0.5802
4,4221
3.6340
21,1563
[S4]
0.13
0.2507
0,4539
14.7489
0.6961
0.6769
3.4689
4.7567
21,9784
InvSR [6]]
1
0.17
0,1424
0.2709
0.4297
13,2950
0.6886
0.6845
3.7446
4.3290
21. 21.6265
OSDIlmman [13]
1
0.11
0.1356
0.2384
0,4680
14.4121
0.7312
0.6908
3,8278
4.7512
22.3524
HAODift (ours)
0.20
0.1023
0.2046
0.5161
8.3623
0.7737
0.7097
2.8298
4.8485
18.5986
Table 1: Quantitative results on PERSONA- Val and inference time comparison. The top two scores are highlighted in red and cyan for all methods. C-IQA stands for CLIPIQA, and M-IQA stands for MANIQA. Inference is performed on images of 512x512 resolution on NVIDIA RTX A6000.
Experiments
4.1 Experimental Settings
Training and Testing Datasets. Our model is trained the PERSONA [11]. with 20k images sampled from both LSDIR [27] and FFHQ [18]. We randomly crop LSDIR to 512x512, and pre-donnsample FFHQ to the same size. We generate synthetic HQ-LQ image pairs using our degradation pipeline. For testing, we use PERSONA- Val and PERSONA- from OSDHuman [11]. Additionally, we select images from the MPII Human Pose dataset [1] using the data selection pipeline from OSDHuman, excluding the quality filtering stage. To accommodate the bounding boxes used in this process, we extend the outermost annotated key points outward by certain margin to form enclosing rectangles, which are used as bounding boxes. This process yields MPII-Test, which consists of 5,427 real-world images with diverse human motion blur (HMB). Using our degradation pipeline, we fine-tune YOLOI1 [17] to detect HMB, finding 1,765 instances in 1,326 images.
Evaluation Metrics. For PERSONA-Val, we use both full-reference and no-reference metrics. For full-reference perceptual quality assessment, we use DISTS [9], LPIPS [68], and TOPIQ [5]. Additionally, we calculate FID [14] to measure the distribution distance between the restored images and ground truth. For no-reference metrics, we use CLIPIQA [47], NIQE [66], MANIQA-pipll [58], LIQE [69], and IL-NIQE [67]. These no-reference metrics are also applied to PERSONA-TES MPII-Test. Additionally, for MPII-Test, we use the fine-tuned YOLO model to detect HMB instances and calculate the ratio of detected instances in restored images to those in original images, denoted as HMB-R. The fine-tuned YOLO detector is evaluated on a dedicated hold-out set containing 4,216 images, achieving an mAP@0.5 of 0.6223 and a precision of 0.6738.
and
Implementation Details. In stage 1, the DPG training process balances the magnitude of the LI loss and Dice loss by setting the in Eq. (4) to 2x 10-2, We use the Adam optimizer [20] with learning rate 2x 10 and batch size 16. The STLs in Hg have 6 heads, while those in HAs use 3 heads. For HBR segmentation, the third branch outputs single channel and utilizes the sigmoid activation function, while the other two output three channels without the activation function. The training is conducted for 20k iterations on 4 NVIDIA RTX A6000 GPUs. In stage 2, we set /3 in Eq. (10) to 1x and use the pretrained SDXL [38] UNet as the discriminator following D* [26]. The Aeig in Eq. (8) is set to 3.5. The Adam optimizer [34] used in stage has learning rate x 10-5 and batch size 2. The base model is 1-ase- [43] and LoRA [16] is used to train the UNet with LoRA rank 16. The training is conducted for 120k iterations on 2 NVIDIA RTX A6000 GPUs
Compared State-of-the-Art (SOTA) Methods. We compare HAODiff with multi-step diffusion models, including DiffBIR [29], SeeSR [55]. SUPIR [60], PASD [59] and ResShift [62]; and one-step diffusion models, including SinSR [52], OSEDiff [54], InvSR [61], and OSDHuman [11].
7

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LQ
SUPIR
SeeSR
PASD
ResShift
OSEDiff
InvSR
OSDHuman
HAODiff
Figure 6: Visual comparison of the real-world PERSONA- and MPII-Test datasets in challenging
and representative cases. Please zoom for a better view.
PERSONA-Ted
MPII-Test
Methods
C-IQAT
M-IQA
NIQEL
LIQET
IL-NIQEL
C-IQAT
M-IQA
NIQEL
LIQET
IL-NIQEL
HMMB RJ
SUPIR [60]
0.7106
0.6798
4.0734
4,0501
22.6182
0.6702
0.6256
4.4231
3,4295
26.0135
0.2776
DiABIR [29]
0.7287
0.6812
4,9820
3,8870
25,4183
0.6531
0.6405
5.1638
3.1343
27.8964
0.5705
SeeSR [S5]
0.6968
0.6759
4.1126
4,0800
23.4376
0.6478
0.6636
4.6152
3,5709
26.5874
0,2612
PASD [59]
0.5765
0.6703
3.8728
3,8901
24.9226
0,4023
0.5220
6.0325
2.3903
38.3461
0.9518
ResShin [62]
0.5544
0.6101
4.8438
3.4981
25.0764
0.5128
6.4687
2,4915
32.7579
SinSR [52]
0.5882
0.6010
4.7510
3,5339
23.3862
0.4816
0.5162
5.9015
2,4504
29.0120
0,9394
OSEDif [54]
0.6734
0.6919
4.4600
4,4296
0.6385
0.6580
4.8383
4,1664
26.8298
InvSR [61]
0.6837
0,7122
4.1694
4,2582
22.8932
0.6166
0.6573
5,4187
3,5793
26.8856
0,2771
OSDHuman [1]]
0,7155
0.6977
4.1287
4,3202
24.8712
0.6537
0.6471
4,5960
3.7991
26.8940
0.3433
HAODiff (ours)
0.7210
0,7057
3.8269
4.2375
21,9784
0.6923
0.6787
3,9450
4.1777
23.6714
0.0929
Table 2: Quantitative comparison on real-world benchmarks, with top two results respectively highlighted in red and cyan. HMB-R indicates the ratio of human motion blur instances compared with restored and original images. C-IQA stands for CLIPIQA, and M-IQA stands for MANIQA.
4.2 Main Results
Quantitative Comparisons. Table presents quantitative results the synthesized PERSONA-VA Despite HAODiff's 's overwhelming inference speed advantage over multi-step diffusion models and comparable performance some one-step diffusion (OSD) models, it achieves top scores across all full-reference metrics. In no-reference evaluation, it ranks first in NIQE and IL-NIQE, indicating natural image synthesis, and leads in MANIQA and LIQE, reflecting alignment with human aesthetics. Among OSD models, HAODiff exhibits the best performance in CLIPIQA. showcasing high-quality restoration. Table compares HAODiff on PERSONA- and MPII-Test. On PERSONA-Test, HAODiff maintains superior performance, leading on most metrics. For the human motion blur (HMB)-rich Mest-Test, HAODiff tops every metric. Meanwhile, our model achieved the lowest HMB-R, -, demonstrating strong HBR performance and effective restoration of HMB content.
Qualitative Comparisons. Visual comparisons with SOTA methods are shown in Figs. 5 and 6. HAODiff effectively restores generic degradations in low-quality (LQQ) images, producing visually clear results. On the LQ human body dataset PERSONA-Test our model achieves notably better reconstruction both body and facial regions. Specifically, it is capable of restoring the applied HMB. On PERSONA- Val and MPII-Test. HAODiff shows clear advantages in handling human images with prominent HMB artifacts, delivering effective restoration. In regions affected by severe motion blur, the model produces results with sharp contours and realistic visual quality. Compared both one-step and multi-step diffusion models, HAODiff consistently generates more detailed and natural results, avoids the excessive smoothness observed in PASD [59], ResShift [62], and OSEDiff [54], as well as the ever-sharpened and distorted textures found in SUPIR [60] and InvSR [61]. In addition, HAODiff produces more realistic clothing details, accurately reflecting fabric textures and folds under natural lighting conditions. Moreover, HAODiff specifically restores blurred human regions without altering the depth of field, conforming to natural optical factors, thereby preventing unrealistic texture synthesis in the background. Further visual comparisons are provided in the supplementary material.
8

---

4.3 Ablation Study
HMB-aware Branch of DPG. To assess the impact of the human motion blur (aMare branch (the third branch) in addressing HMB, we conducted a targeted ablation study. Specifically, we train HAODiff using version of DPG that excludes the HMB-aware branch while retaining the other two branches. The training performed for the same number of epochs, with identical settings and CFG strategy, and the model is further evaluated on the HMB-rich MPI--Test. The results are shown in Fig. 7. Without the guidance of the HMB-awan branch, the model shows a clear decline in its ability to recover the HMB regions. Specifically, the arms of the subject in the LQ image suffer from severe motion blur; without the branch, the model removes only non-motion-blur noise, whereas with the branch the arms are sharply restored. Quantitative comparisons between the two configurations are also provided, with LIQE and HMB-R R scores annotated below each strategy. These results indicate that incorporating the HMB-awe aware branch not only preserves the overall restoration quality but also significantly improves the model's ability to handle HMB-specific degradation.
1.0 LIQEt / HHMB-Rj 4,1015 10.2816 4.1777 / /0.0929 Figure 7: Comparison of whether to leverage the third (HMB-aware) branch.
wio 3rd brunch
w/ 3nd branch
The Effectiveness of DPG. In order to validate the effectiveness of DPG,
Prompt Methods
DISTSL
MS-SWD1
PIDL
CLIPIQA!
NIQEL
Text Pair [63]
0,1056
0.3815
8,5215
0,7648
2,9692
we compare it against several prompt-
DAPE [54]
0.1047
8.6143
0.7693
3.0779
based guidance strategies, including: the fixed positive-negative prompt pair with CFG following S3Diff [63]. the
HFIE [1]]
0,1060
0.4066
9.2570
0,7681
3.2063
DPG (ours)
0.1023
0.2829
8.3623
0.7737
2.8298
Table 3: Comparison of different prompt methods. Text Pair
represents using a fixed pos-neg prompt pair with CFG. (DAPE) from OSEDifT [54], and the high-fidelity image embedder (HFIE) from OSDHuman [1]]. Table 3 reports quantitative results on PERSONA-Val Val, where DPG consistently delivers superior restoration quality across multiple metries. Moreover, we observe that other methods tend to produce more pronounced color shifts compared to DPG gPGdguided outputs. These color shifts significantly affect human perception of image fidelity. And once they occur, it is challenging to eliminate them through post-processing methods (e.g., wavelet-based correction [35]). To quantify this effect and assess the impact on model stability, we employ MS-SWD [13]. metric for measuring color distribution differences between the processed results and reference images. With HQ images as reference, the findings clearly show that DPG yields the smallest color discrepancies. DPG's superior performance in this respect sterss from its dual-prompp design: the negative path captures residual noise, implicitly including color-shift degradation. During training, DPG more accurately anchors the original color distribution and detects color drift in the noise. This capability is then explicitly reinforced through classifier-fns free guidance (CFG), thereby resulting in higher fidelity and robustness.
degradation aware prompt extractor
Limitation and Conclusion
Limitation. Images with heavy global degradation and strong local motion blur, particularly in regions with fast-moving limbs, remain challenging for all methods, including ours. A few samples of this type appear in the Mest-Test. Although HAODiff outperforms all base- lines (many of them fail completely, as shown in Fig. 8 and supplementary material), our results remain imper- Figure 8: Challenge task from MPII-Test. fect. Limb poses may appear unnatural, and overlapping objects may be misrestored. These limitations prompt us to reconsider the boundary between pure restoration and content generation. Moreover, similar to common CFG strategies in image restoration, our method employs UNet to compute the latent vector twice. These two passes thus incur a modest efficiency penalty compared to OSEDiff [54] and OSDHuman [1]] (see Tab. 1). In future work, will develop more efficient CFG strategies and explore approaches to handle such extreme real-world cases, recover finer details.
LQ
SUPIR [60] HAODifI (ours)
We propose HAODiff, human- one-step diffusion model. It perceives and addresses degradations around humans (e.g., human motion blur). The restored images are perceptu- ally aligned with human vision, minimizing color shifts while maintaining high quality. Our model achieves these results by leveraging a degradation pipeline with human motion blur and a triple- branch dual-prompt guidance (DPG). The pipeline simulates realistic and diverse degradations, while DPG generates positive-negative prompt pairs that enhance the diffusion's 's CFG capability. Extensive experiments demonstrate the effectiveness of HAODiff and the contribution of these modules.
Conclusion.

---

Acknowledgmentts
This work supported by Shanghai Municipal Science and Technology Major Project (201ISHZDZX0122) the Fundamental Research Funds for the Central Universities, the Special Project on Technological Innovation Application for the 15th National Games and the National Paralympic Games under Grant 2025B0I
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Reviews:
[{"review_id": "3u3ltelD1x", "reviewer": "Reviewer_LbqC", "summary": "The paper proposes a solid solution pipeline for image restoration tasks under human body scene, which consists of a degraded samples generation pipeline, an image restoring pipeline and a human body restoration metrics dataset. The degradation pipeline generate degraded images from high quality images through three paths, which cover the usual image degradation scenes (blur, erosion, dilation, noise, down-sampling, compression), and specially HMB scenes (Human Motion Blur). The restoration pipeline adopts a novel dual-prompt-guidance strategy that split and convert features into positive and negative prompt of CFG. And it outperforms previous methods numerically and visually. Also, the methods is relative light-weight and less performance-consuming, which makes it possible for application on light-weights devices like phones, tables and drones.", "questions": "For the sample generation pipeline:\n\nIt seems that all the samples finally pass through the compression process (Figure 2). But under the real scene, devices (phones and cameras) usually store uncompressed images. Does the pipeline takes this into consideration and generates samples without compression?\n\nUsually, different parts of human bodies can simultaneously have motion blur in different directions. Does the pipeline takes this into consideration?\n\nDoes the pipeline disentangles the motion blur of camera and motion blur of human body, which could happens at same time?\n\nFor the restoration pipeline:\n\nHow much is the performance gain from adversarial training?", "limitations": "The limitations have been thoroughly and clearly discussed in the paper.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThe paper is well organized and the experimental results (metrics and examples) are sufficient to support its claims.\n\nThe method achieves SOTA and outperforms previous method from both numerical metrics and visual quality.\n\nThe method adopts light-weight modules (e.g. swin-transformer-blocks and performer) and one-step diffusion schema, which makes it fast and less performance-consuming. So, it has the potential availability for inferencing on light-weight devices like phones (which is under the most common scene that users need human blur restoration), tablets and drones. \n\nThe proposed strategy dual-prompt guidance is interesting, which logically split and convert additional features into negative and positive prompt of classifier free guidance, has the potential usage for other tasks.\n\nThe ablation study is detailed and convincing.\n\nWeakness:\n\nThe method is very complex, including training and fine-tuning of multiple models and multiple stages, and also generative adversarial training.\n\nOther: See Questions.", "was_revised": true, "final_justification": "The authors' rebuttal have addressed with my problems. This is a solid, novel and practical work proved with heavy experiments. Also, the method is reasonable, specific-designed while remains the possibility for solving other down-stream tasks. I will keep my rating.", "rebuttal": "`Q1-1:` It seems that all the samples finally pass through the compression process (Figure 2). But under the real scene, devices (phones and cameras) usually store uncompressed images. Does the pipeline take this into consideration and generate samples without compression?\n\n`A1-1:`\nThank you for the question. In our degradation pipeline, the compression process specifically refers to **JPEG compression**. While high-end applications may store uncompressed RAW images, JPEG remains the dominant format used by most consumer devices, such as smartphones and cameras, due to its balance between image quality and storage efficiency. JPEG is a lossy compression format by design, and compression artifacts exist at all quality levels, including at a quality factor (QF) of 100. Although these artifacts may be imperceptible to the human eye at high quality levels, they are still present.\n\nIn our setup, we vary the JPEG quality factor within a range of 30 to 95. A QF of 95 produces images that are nearly indistinguishable from uncompressed versions to the human eye and reflects the common default setting used in many modern devices. A QF of 30 corresponds to scenarios such as extreme compression on social media platforms. This range allows our model to learn robustness across realistic compression levels commonly encountered in real-world scenarios.\n\n---\n`Q1-2:` Usually, different parts of human bodies can simultaneously have motion blur in different directions. Does the pipeline take this into consideration?\n\n`A1-2:`\nAppreciate the insightful question. In our current degradation pipeline, we do not explicitly simulate distinct motion directions for different human body parts. Instead, we apply a single localized motion trajectory to each selected region. While this design simplifies the motion modeling process, it still produces realistic motion blur patterns that support effective training and generalize well to real-world scenarios.\n\nImportantly, our pipeline is modular and can be readily extended to incorporate part-specific motion blur. For example, during degradation, multiple segmentation masks corresponding to different body parts or subjects can be pre-selected. Distinct motion blur kernels can then be applied to each region individually, followed by boundary smoothing and spatial composition into a coherent degraded image. Although our current implementation does not explicitly simulate fine-grained motion variations across body parts, we recognize its potential to further enhance realism. We consider this a promising direction and plan to incorporate it into our future work.\n\n---\n`Q1-3:` Does the pipeline disentangle the motion blur of camera and motion blur of human body, which could happen at the same time?\n\n`A1-3:` \nThank you for raising this important and insightful point. Our current degradation pipeline is primarily designed to simulate localized motion blur caused by human body movement, and does not explicitly disentangle it from camera-induced motion blur, although both types of blur may co-occur in real-world scenarios. We believe that these two types of motion blur can be jointly modeled within a unified framework.\n\nSpecifically, motion blur can be interpreted as the temporal integration of pixel intensities over the exposure duration, where each pixel follows a motion trajectory driven by instantaneous velocity. For **camera motion**, a global velocity field is applied uniformly to all pixels. For **human motion**, localized velocity fields are applied only within human regions such as limbs. When both types of motion are present, the per-pixel motion can be approximated as the vector sum of the camera and human velocities, which is then integrated over time to produce the final motion blur kernel.\n\nAs a result, different regions in the image can be convolved with kernels derived from different sources: background regions use kernels from camera motion alone, while human regions use combined kernels incorporating both human and camera motion. The final blurry image is generated by fusing the outputs from each region accordingly.\n\nAlthough our current implementation only simulates human motion blur, this modeling strategy provides a theoretical basis for jointly generating images that contain both types of blur. In future work, we aim to address both concerns raised in `Q1-2` and `Q1-3` within a single pipeline. Specifically, we plan to segment the image into different categories and disconnected regions, and estimate localized motion kernels for each region independently. These region-specific kernels will then be combined with a global camera motion field to simulate the coexistence of camera motion blur and multi-body-part motion blur in a principled and physically consistent manner.\n\n\n---\n`Q1-4:` How much is the performance gain from adversarial training?\n\n`A1-4:`\nThank you for the question. The differences between real and generated images are often subtle and difficult to capture using conventional RGB-space loss functions. Prior research [1] shows that these differences are more clearly reflected in the distribution of a pre-trained UNet's output, which provides a promising basis for improving the realism of restored images. Based on this observation, we use an adversarial loss in the latent space, where a discriminator uses the output of the fixed UNet to guide the restored latent vectors toward better alignment with those of real images.\n\nTo quantify the contribution of adversarial training, we conduct an ablation study on PERSONA-Val by retraining the model without the GAN loss. As shown in the table below, removing adversarial training results in noticeable performance degradation across both perceptual metrics (DISTS, LPIPS, and TOPIQ) and no-reference IQA scores (CLIPIQA, MANIQA, and NIQE). This confirms that adversarial training significantly enhances the perceptual quality and realism of the restored images.\n|Method|DISTS↓|LPIPS↓|TOPIQ↑|CLIPIQA↑|MANIQA↑|NIQE↓\n|-|-|-|-|-|-|-\n|w/o GAN loss|0.1046|0.2091|0.5106|0.7630|0.7057|3.1745\n|w/ GAN loss|**0.1023**|**0.2046**|**0.5161**|**0.7737**|**0.7097**|**2.8298**\n\n[1] Li, Jianze et al., Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator, arXiv, 2025."}, {"review_id": "2sFM3DXzyz", "reviewer": "Reviewer_maZ3", "summary": "The paper tackles the challenging problem of human body restoration, with a specific focus on handling the co-occurrence of generic image degradation and human motion blur (HMB), a scenario often overlooked by existing methods. The authors propose a comprehensive solution with three main components: A new degradation pipeline that synthetically generates realistic training pairs by first simulating HMB on specific body parts and then applying a sequence of generic degradations. A novel prompt generation module, named Dual-Prompt Guidance (DPG), which is a triple-branch network trained to predict the high-quality (HQ) image, the residual noise (LQ - HQ), and an HMB segmentation mask. These outputs are then used to form adaptive positive and negative prompts for the diffusion model. A one-step diffusion model, HAODiff, that leverages these adaptive prompts via classifier-free guidance (CFG) to achieve efficient and high-fidelity restoration. The authors also introduce MPII-Test, a new benchmark dataset curated for evaluating HBR performance on real-world images with significant motion blur. Extensive experiments show that HAODiff surpasses existing state-of-the-art methods quantitatively and qualitatively.", "questions": "Regarding the HMB-R metric, the paper mentions it is calculated using a fine-tuned YOLO model (Sec 4.1), with details in the supplementary material. Given that this is a key metric for validating the central claim of the paper, could the authors provide a brief summary of the detector's performance (e.g., mAP on a hold-out set) in the main paper? The reliability of this metric is crucial for interpreting the results in Table 2.\n\nThe DPG architecture (Sec 3.2) features dedicated branches for residual noise and HMB masks. How would this framework generalize to other types of structured degradation, such as heavy rain, snow, or haze? Would it require adding a new, specialized branch for each new degradation type, potentially making the model cumbersome? What are the authors' thoughts on the scalability of the DPG's multi-branch design?\n\nSome other concerns are listed in Weaknesses", "limitations": "The authors have provided a good discussion of limitations in Section 5, acknowledging failure cases in scenarios with extreme degradation and the modest efficiency penalty from the dual-pass UNet required for CFG. To build on this, I would add:\n\nThe paper does not discuss the limitation of its two-stage training pipeline (training DPG, then training OSD), which can be more complex and resource-intensive to manage and tune compared to end-to-end approaches.\n\nAs raised in the questions, the current design of DPG with specialized branches for specific degradations might not scale well to a wider variety of real-world distortions, which could be considered a limitation of the current framework's generalizability", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\nThe paper correctly identifies a significant gap in existing blind image restoration (BIR) and HBR research: the lack of explicit handling for human motion blur, which is a very common artifact. The proposed degradation pipeline (Sec 3.1) is a logical and direct way to address this, and its design, which places HMB in the first-order degradation stage, is physically plausible.\n\nThe paper is very well-written and easy to follow. The methodology is explained clearly, and the figures, are illustrative and aid in understanding the proposed framework.\n\nWeaknesses\n\n1. While the guidance mechanism (DPG) is novel, the second stage of the model—the one-step diffusion (OSD) process described in Section 3.3, is largely built upon existing frameworks like OSDHuman and other one-step models. The use of a standard UNet with LoRA, a VAE encoder/decoder, and the standard CFG formulation means the contribution is more about how to guide an existing diffusion framework rather than advancing the diffusion process itself. **This is not a major flaw but slightly reduces the fundamental nature of the contribution.**\n\n2. The proposed solution involves a two-stage training process: first training the DPG module, and then training the OSD model using the frozen DPG as a prompt generator. This introduces **significant complexity** in terms of training procedure and pipeline management compared to end-to-end trainable models.\n\n3. In Section 3.2, the DPG is built with a backbone of two RSTBs (with six Swin Transformer Layers) and three reconstruction branches of another two RSTBs (with three STLs). While this is inspired by SwinIR, it is a rather heavy architecture for a guidance module. It is unclear if this level of complexity is necessary. A lighter backbone might have achieved similar guidance quality while improving overall efficiency.", "was_revised": true, "final_justification": "The authors' detailed rebuttal has successfully addressed my initial concerns, particularly regarding the two-stage training pipeline and the complexity of the DPG module. I now consider the proposed HAODiff model to be a general and practical approach, and I have raised my final rating accordingly", "rebuttal": "`Q2-1:` Since HMB-R is central to the paper, can the authors briefly report the YOLO detector’s performance (e.g., mAP on a hold-out set) in the main paper to support its reliability?\n\n`A2-1:`\nThank you for raising this important concern. Since HMB-R is central to evaluating motion blur removal, we agree that clarifying the reliability of the YOLO detector is important. We evaluated our fine-tuned YOLO detector on a dedicated hold-out set of 4,216 images. The evaluation results on this set are presented below to demonstrate its detection quality.\n\n| mAP\\@0.5 | Precision | Recall |\n|---------|-----------|--------|\n| 0.6223  | 0.6738    | 0.6110 |\n\nThe results show a reasonable level of agreement with the annotated blur regions, suggesting that the detector provides a sufficiently reliable basis for computing the HMB-R metric. In the revised version of the paper, we will provide a summary of these results in the main text to enhance transparency and support the use of HMB-R as a key evaluation metric.\n\n---\n`Q2-2:` Since DPG uses separate branches for noise and HMB masks, would handling degradations like rain, snow, or haze require adding new branches? Does this affect the scalability and generalizability of the design?\n\n`A2-2:`\nThank you for the insightful question. The third branch in our DPG architecture was originally designed to predict HMB masks. More broadly, it serves as a general mechanism for providing explicit spatial guidance on localized degradations. Although it is trained on human motion blur in our setting, the underlying principle is applicable to other types of structured degradations such as heavy rain, snow, or haze. These artifacts also exhibit spatial patterns that can be explicitly modeled. **For instance, rain or snow regions can be represented by binary masks, and haze can be described by transmission maps.**\n\nRather than adding a new branch for each degradation type, the existing mask branch can be extended to **predict multi-channel outputs**, with each channel corresponding to a specific degradation type. For example, one channel may predict the HMB mask, while others predict rain masks or haze transmission maps. This channel-wise design allows the model to handle multiple degradation types simultaneously, without significantly increasing architectural complexity. Therefore, we believe that the current DPG design is both flexible and scalable. The third branch provides a unified and generalizable structure for guiding the restoration process under various types of localized degradation.\n\n---\n`Q2-3:` Although DPG is novel, the second-stage OSD mainly builds on existing methods, focusing more on guiding a standard diffusion framework rather than advancing it, which slightly weakens the fundamental contribution.\n\n`A2-3:`\nThank you for the thoughtful comment. In our approach, we choose to retain the existing one-step diffusion (OSD) architecture so as to fully utilize the strong priors in pre-trained diffusion models such as Stable Diffusion. Instead of modifying the model architecture, we focus on developing effective guidance strategies that better adapt these models to realistic and challenging restoration tasks. We believe that enhancing guidance mechanisms can also provide a more scalable and practical path to achieving stronger restoration outcomes.\n\nFirst, we introduce a new degradation pipeline that explicitly incorporates human motion blur (HMB), enabling the model to learn from more realistic motion patterns and thereby improving generalization to real-world scenarios.\n\nSecond, our proposed model, HAODiff, enhances the use of classifier-free guidance (CFG) in one-step diffusion. Previous multi-step methods often use fixed prompts, especially for negative guidance, limiting their effectiveness. Most one-step methods either omit the CFG entirely or set the scale close to 1, making the influence of the negative prompt negligible. In contrast, we adopt adaptive negative prompts in conjunction with a larger CFG scale. This combination effectively pushes the model away from low-quality signals and guides it toward high-quality features, improving both robustness and restoration fidelity.\n\nIn addition, we design a dual-prompt guidance (DPG) module that generates both structural features and spatial degradation maps. This allows the model to incorporate not only high-level content information but also explicit localization of degradations such as HMB. Our ablation study demonstrates that injecting spatial information as part of the prompt significantly improves performance, offering a promising new direction for handling localized degradations in diffusion-based restoration models.\n\nThe strong performance of our method on both public datasets and our newly introduced benchmark demonstrates the effectiveness of the overall framework, including our degradation simulation strategy, adaptive prompt formulation, and spatial guidance mechanism.\n\n---\n`Q2-4:` The DPG design is quite heavy for a guidance module. Is such complexity necessary, or could a lighter backbone offer similar performance with better efficiency?\n\n`A2-4:`\nWhile our method is indeed inspired by SwinIR and adopts Residual Swin Transformer Blocks (RSTBs), we have already introduced **significant architectural simplifications** (e.g., reduced depth and channel size) to reduce the model's size and complexity. Furthermore, compared to other guidance strategies such as SUPIR (which relies on LLaVA for prompt generation and takes several seconds per image), our approach achieves better performance with lower latency. We believe this trade-off is justified by the substantial improvements in guidance effectiveness.\n\nThat said, we acknowledge that DPG remains relatively heavy compared to the prompt modules used in other one-step diffusion methods. The table below provides a quantitative comparison between DPG and the full HAODiff model. While DPG contributes minimally to the overall parameter count, it accounts for a relatively high portion of the total MACs. To explore the potential for further simplifying the current architecture, we are currently training a lighter DPG variant to serve as a more efficient guidance module.\n\n|Module|Parameters (M)|MACs (G)\n|-|-|-\n|DPG|38|479\n|Overall|1,459|2,600\n\n---\n`Q2-5:` The paper does not discuss the limitation of its **two-stage** training pipeline (training DPG, then training OSD), which can be more complex and resource-intensive to manage and tune compared to **end-to-end** approaches.\n\n`A2-5:`\nThank you for the insightful question. We are fully aware that a two-stage training pipeline introduces additional complexity into the overall framework, as it requires separate optimization of the DPG and OSD components. We did experiment with end-to-end training in the early stages of our work, but the results were suboptimal. We believe this is mainly due to two reasons.\n\nFirst, jointly training the DPG and OSD introduces multiple competing loss terms, making it difficult to achieve stable convergence. Second, unlike the UNet backbone in Stable Diffusion that benefits from pre-trained priors, the DPG module is trained from scratch and lacks meaningful guidance signals in the early training stages. As a result, it struggles to generate effective prompts for the diffusion model when optimized jointly from the beginning.\n\nTo mitigate these issues, we adopt a two-stage training strategy. In the first stage, we train the DPG module separately using the training set as supervision to incorporate prior knowledge into the module. Once trained, the DPG can provide stronger and more consistent guidance during the second-stage training of the diffusion model. While this approach adds some training overhead, we find that it significantly improves both performance and training stability. In the future, we aim to explore unified architectures that enable stable end-to-end training while maintaining or even surpassing the performance of our current HAODiff framework."}, {"review_id": "y9L1XhT3xp", "reviewer": "Reviewer_8mh7", "summary": "The paper proposes a novel one-step diffusion model, HAODiff, which addresses the challenges of human motion blur and generic degradation in human-centric image restoration through a dual-prompt guidance mechanism. The model leverages a degradation pipeline that simulates realistic degradation scenarios and employs adaptive positive-negative prompts to enhance restoration quality. Experimental results demonstrate its superiority over existing methods.", "questions": "see weakness", "limitations": "The authors present failure case but lack detailed interpretation and potential solutions.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. The design of the triple-branch DPG mechanism is innovative. It leverages multiple training targets to generate adaptive positive-negative prompts, which enhances the effectiveness of CFG.\n2. The design of the degradation pipeline and the dual-prompt guidance mechanism is well-founded and addresses a gap in existing research related to human motion blur. The integration of classifier-free guidance through adaptive positive-negative prompts enhances the model's robustness against diverse degradations.\n3. The paper provides extensive experimental results on both synthetic and real-world datasets, including the newly introduced MPII-Test benchmark. The results demonstrate that HAODiff outperforms existing state-of-the-art methods in terms of quantitative metrics and visual quality.\n4. The paper is well-organized, with a logical flow from problem statement to methodology, experiments, and discussion.\n\nWeakness:\n1. The paper lacks evaluations on classic metric like PSNR, SSIM or MAE, which affects reliability of the model's performance.\n2. Fig 8 shows limb poses appear unnatural, and overlapping objects may be misrestored. Can the authors provides the reason and potential solutions?", "was_revised": true, "final_justification": "I incline to accept this paper. The authors address my concerns well in the rebuttal phase.", "rebuttal": "`Q3-1:` The paper lacks evaluations on classic metrics like PSNR, SSIM, or MAE, which affects the reliability of the model's performance.\n\n`A3-1:` \nThank you for your valuable suggestion. We agree that classic metrics such as PSNR, SSIM, and MAE provide important references for evaluating restoration quality. In our **supplementary Tab. 5**, we report the PSNR and SSIM results for our method and all compared methods, and we additionally include MAE in the table below.\n\n| Metric       | DiffBIR | SeeSR  | SUPIR  | PASD   | ResShift | SinSR  | OSEDiff | InvSR  | OSDHuman | HAODiff |\n|--------------|--------:|-------:|-------:|-------:|---------:|-------:|--------:|-------:|---------:|--------:|\n| **PSNR↑**   | 20.32   | 20.28  | 19.67  | 21.19  | **21.31** | 20.85  | 20.39   | 19.67  | 21.06    | 20.59   |\n| **SSIM↑**   | 0.5592  | 0.5902 | 0.5451 | 0.6248 | **0.6283** | 0.6007 | 0.6049  | 0.5844 | 0.6180   | 0.6035  |\n| **MAE↓**    | 0.0720 | 0.0622 | 0.0654 | **0.0544** | 0.0546 | 0.0570 | 0.0627 | 0.0655 | 0.0566  | 0.0580 |\n| **DISTS↓**  | 0.1402  | 0.1295 | 0.1415 | 0.1469 | 0.1638    | 0.1579 | 0.1340  | 0.1424 | 0.1356   | **0.1023** |\n| **LPIPS↓**  | 0.2797  | 0.2555 | 0.2929 | 0.2910 | 0.2848    | 0.2840 | 0.2507  | 0.2709 | 0.2384   | **0.2046** |\n\nAs discussed in **supplementary Sec. C.1 Full-Reference (FR) Metrics**, pixel-level metrics like PSNR often fail to capture perceptual quality in blind restoration. This is because blurry results with oversmoothed textures can still achieve relatively high PSNR and SSIM scores, despite being perceptually inferior. In contrast, perceptual metrics such as DISTS and LPIPS align more closely with human visual preferences. For instance, as shown in **supplementary Fig. 3**, the competing methods produce blurry output that nevertheless receives higher PSNR and SSIM. However, our method achieves significantly sharper restoration and better content fidelity, as reflected in significantly better DISTS and LPIPS scores.\n\nGiven the limitations of pixel-level metrics in blind restoration, we focus our discussion in the main paper on perceptual metrics while still providing comprehensive PSNR and SSIM results in the supplementary material to ensure completeness of evaluation.\n\n---\n`Q3-2:` Fig. 8 shows limb poses appear unnatural, and overlapping objects may be misrestored. Can the authors provide the reason and potential solutions?\n\n`A3-2:` \nThank you for your insightful comment. In Fig. 8, we believe this failure case arises due to excessive blur in the degraded region and **lacks essential structural cues**. This makes it ambiguous whether the region belongs to a limb or an object. At present, our model relies primarily on visual structure without incorporating explicit structural priors, which limits its ability to restore severely degraded areas.\n\nTo address this issue, incorporating structural guidance, such as **pose estimation maps**, could help the model better identify ambiguous regions and generate more anatomically plausible results. Although this strategy is not yet included in our current framework, we believe it is a promising direction for improving restoration performance in future work."}, {"review_id": "facvvwB05z", "reviewer": "Reviewer_o2JZ", "summary": "This paper proposes a two-stage framework for human body restoration with a focus on addressing human motion blur. The first stage constructs a realistic degradation pipeline that simulates spatially-aware motion blur using part-level segmentation and morphological operations, enhancing data diversity. The second stage introduces a Dual-Prompt Guidance (DPG) module to extract semantic priors from degraded inputs, which are used to guide a one-step diffusion model for high-fidelity restoration. The methodology is technically solid and well-motivated, combining detailed degradation modeling with efficient generative restoration. However, more analysis on the effectiveness of individual degradation components and a deeper understanding of the prompt embeddings would further strengthen the work.", "questions": "- How well does the HMB pipeline generalize to more complex human motion scenarios, such as fast multi-limb movements in sports or dancing with occlusions? Please provide results or analysis on such challenging cases.\n- Are there any training stability issues with the three-branch Dual-Prompt setup? Clarify training details and convergence.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The restoration task is well-structured into two clear stages: Dual-Prompt guidance for semantic extraction followed by One-Step Diffusion for efficient detail restoration, effectively addressing the challenge of generating high-quality images from low-quality inputs.\n- The use of diffusion inversion with classifier-free guidance, latent VAE sampling, and Performer-based pooling significantly improves sampling efficiency by reducing the need for multiple diffusion steps, resulting in a focused and technically sound approach.\n\nWeaknesses:\n- The method’s heavy reliance on precise body part segmentation via Sapiens limits practical applicability, with no discussion on obtaining accurate masks in real-world or unsupervised settings.\n\n- Human motion blur modeling remains overly simplistic, ignoring complexities like varying limb velocities, camera exposure, and depth-of-field effects, which may hinder real-world generalization.\n\n- The Dual-Prompt architecture lacks clear motivation and detailed explanation on why joint training of image, residual, and mask predictions is necessary.\n\n- The approach primarily assembles existing modules (SwinIR, Stable Diffusion, Performer, CFG), limiting novelty to engineering integration.", "was_revised": true, "final_justification": "The author's response has resolved my doubts, and I will maintain my score. However, I pay little attention to the latest progress in this field. Please also refer to the comments of other reviewers.", "rebuttal": "`Q4-1:` The method’s heavy reliance on precise body part segmentation via Sapiens limits practical applicability, with no discussion on obtaining accurate masks in real-world or unsupervised settings.\n\n`A4-1:` Thank you for the insightful comment. We would like to clarify that body part segmentation from Sapiens is **used only during the training phase** of the degradation pipeline and the DPG. It is not used during inference or under unsupervised/real-world settings. During supervised training, the DPG module learns to utilize motion-aware priors derived from segmentation masks. Therefore, actual segmentation results are not required during inference.\n\nMoreover, we do not rely on high-precision segmentation from Sapiens, as motion blur inherently introduces ambiguity in limb boundaries. To better approximate real-world motion blur, we apply morphological operations and Gaussian blurring to the segmentation masks, which helps produce smoother boundary transitions. This enhances the realism of the simulated degradation and helps reduce the domain gap between synthetic and real-world data.\n\n---\n`Q4-2:` Human motion blur modeling remains overly simplistic, ignoring complexities like varying limb velocities, camera exposure, and depth-of-field effects, which may hinder real-world generalization.\n\n`A4-2:`\nThank you for highlighting this important aspect. In our degradation pipeline, we adopt the trajectory generation strategy from DeblurGAN [1], which combines inertial motion, Gaussian perturbations (to simulate physiological tremor), and occasional large jitters (to simulate abrupt movements). This results in **diverse and realistic motion trajectories under plausible physical assumptions**.\n\nWhile we do not explicitly model exposure time or depth-of-field effects, our training data is collected under **diverse real-world conditions that inherently include variations in exposure, lighting, and focus**. We think this enables the model to implicitly learn such effects and remain robust during inference. Moreover, these factors are often entangled in practice and difficult to isolate individually. Thus, we adopt a data-driven approach to better capture their joint impact.\n\nFinally, our proposed MPII-Test dataset, constructed from real-world blurry images, contains diverse and complex motion blur scenarios. Our method achieves state-of-the-art performance on this benchmark, further validating its strong generalization to real-world conditions.\n\n[1] Kupyn et al., DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks, CVPR, 2018.\n\n`Q4-3:` The Dual-Prompt architecture lacks clear motivation and detailed explanation on why joint training of image, residual, and mask predictions is necessary.\n\n`A4-3:`\nThank you for the question. Our motivation for designing the dual-prompt architecture stems from the observation that current CFG-based image restoration methods typically focus only on the positive prompt, while the negative prompt is fixed or generic. This limits the model’s ability to capture the specific type and region of degradation in the input image. Moreover, vision-language models often struggle to generate precise textual descriptions of degradations, especially in complex or subtle cases. To address these issues, we propose to **jointly learn both positive and negative image-based prompts directly from the degraded input**, enabling more informative and task-specific conditioning. \n\nInstead of using LQ features as the negative prompt, which may retain useful structure, we argue that the residual noise (LQ - HQ) represents more pure degradation signals and is better suited for negative guidance. Conversely, the HQ prediction branch provides high-quality content that aligns with the intended restoration target, serving as a positive prompt. Additionally, we observe that real-world degraded human images often contain localized motion artifacts. To tackle this, we introduce an auxiliary mask prediction branch that captures these local distortions to assist the model in negative prompt modeling.\n\nThe overall three-branch structure of the DPG module is inspired by multi-task learning paradigms, where a shared backbone is used to solve multiple related tasks. In our case, the backbone features are passed to three task-specific branches: one for HQ prediction, one for residual noise prediction, and one for HMB mask prediction. These outputs are structurally correlated and jointly optimized, enabling the model to learn richer and more targeted guidance signals while remaining parameter-efficient.\n\n\n`Q4-4:` The approach primarily assembles existing modules (SwinIR, Stable Diffusion, Performer, CFG), limiting novelty to engineering integration.\n\n`A4-4:`\nThank you for the comment. As noted in our response to `Q2-3`, our work builds upon a one-step diffusion backbone and leverages components such as SwinIR and Stable Diffusion. However, the novelty lies in how these modules are integrated and guided for the specific task of restoring human images degraded by motion blur.\n\nWe introduce a new degradation pipeline specifically tailored for HMB, enabling the model to generalize to complex, real-world motion patterns. Moreover, we propose the dual-prompt guidance (DPG) module, which injects both structural and spatial degradation-aware signals into the diffusion process. This enhances the use of classifier-free guidance (CFG) by introducing adaptive negative prompts and localized spatial maps, which are rarely explored in existing frameworks.\n\nWhile our approach employs existing architectures, it introduces new mechanisms for degradation-aware conditioning and guidance. The consistent improvements across synthetic and real-world benchmarks, including our challenging MPII-Test dataset, demonstrate the practical effectiveness and generalization capability of our framework.\n\n---\n`Q4-5:` How well does the HMB pipeline generalize to more complex human motion scenarios, such as fast multi-limb movements in sports or dancing with occlusions? Please provide results or analysis on such challenging cases.\n\n`A4-5:`\nThank you for the valuable question. Our current degradation pipeline is primarily designed for typical scenarios, such as single-person motion or multi-person scenes with limited movement. Although the current pipeline does not explicitly simulate highly dynamic cases, our method has demonstrated good generalization to a wide range of real-world conditions, including moderate motion complexity and partial occlusions.\n\nThis question shares a common concern with `Q1-2`, namely that different body parts can undergo motion blur in different directions. As discussed in `A1-2`, our pipeline currently applies a single localized motion trajectory per region, without explicitly modeling joint-level motion differences. Nevertheless, the modular design allows for easy extension to incorporate part-specific motion blur by applying distinct motion kernels to segmented regions, followed by boundary smoothing and image composition. This flexibility provides a practical foundation for handling more complex human motion scenarios.\n\nExtremely dynamic movements involving high-speed multi-limb motion are relatively rare in daily life but can appear more frequently in sports scenes, such as the soccer example shown in Fig. 8. Such cases remain particularly challenging for existing restoration models. We believe that incorporating additional human priors, such as body pose or skeletal information, may be necessary for achieving faithful restoration in these challenging scenarios. We consider it valuable to integrate these complex cases into the degradation process and to investigate targeted restoration strategies.\n\n---\n`Q4-6:` Are there any training stability issues with the three-branch Dual-Prompt setup? Clarify training details and convergence.\n\n`A4-6:`\nThank you for the question. Although the DPG adopts a three-branch structure, we did not observe noticeable instability during training. The overall architecture is modular and well-structured, and training proceeds smoothly.\n\nAs shown in Fig. 3 Stage 1 and described in Sec. 3.2, the DPG first uses a convolutional head ($H_D$) to downsample the input LQ image by a factor of 4 and increase the channel dimension to match the input format of the Swin Transformer. The shared backbone ($H_E$) then extracts features using two residual Swin Transformer blocks (RSTBs), each with six Swin Transformer layers (STLs). The features are then passed to three branches ($H_{Ri}, i \\in [1, 3]$), each with two RSTBs and three STLs, followed by a convolutional upsampling module ($H_{Ui}$). The three branches are used to predict the HQ image, the residual noise, and the HMB segmentation mask.\n\nTraining details are described in Sec. 4.1. We employ a joint loss comprising an L1 loss for the HQ image, an L1 loss for the residual noise, and a Dice loss for the segmentation mask. To balance the losses, we set the Dice loss weight to 0.02. We use the Adam optimizer with a learning rate of 2$\\times$10$^{-3}$ and a batch size of 16. The model is trained for 20k iterations on four NVIDIA RTX A6000 GPUs. The segmentation branch outputs a single channel with sigmoid activation, while the other two branches output three channels without activation.\n\nUnder this setup, the DPG shows stable training in stage 1. We do not observe mode collapse or oscillation. All three loss terms decrease smoothly and converge before the end of training. The loss values at different iterations are shown in the table below.\n\n|Iteration|Total Loss|HQ Loss|Residual Loss|Mask Loss\n|-|-|-|-|-\n|2000|0.1321|0.0600|0.0545|0.0176\n|4000|0.1292|0.0576|0.0539|0.0177\n|6000|0.1260|0.0555|0.0527|0.0178\n|8000|0.1234|0.0540|0.0517|0.0177\n|10000|0.1204|0.0525|0.0505|0.0174\n|12000|0.1192|0.0516|0.0500|0.0176\n|14000|0.1181|0.0512|0.0498|0.0171\n|16000|0.1165|0.0505|0.0492|0.0168\n|18000|0.1161|0.0504|0.0490|0.0166\n|20000|0.1160|0.0506|0.0491|0.0163"}]

Author rebuttal:

Meta-review: This paper proposes HAODiff, a one-step diffusion framework for human-centric image restoration, explicitly addressing human motion blur (HMB) alongside generic degradations. The contributions include a realistic degradation pipeline, a Dual-Prompt Guidance mechanism that generates adaptive positive and negative prompts, and a new benchmark for evaluating HMB restoration. The method demonstrates strong performance across synthetic and real-world datasets.

Reviewers appreciated the novelty of handling HMB directly, the innovative DPG design, and the solid experimental validation. Concerns were raised about the complexity of the training pipeline, scalability of the DPG design, reliance on existing diffusion backbones, and missing classical metrics. The rebuttal convincingly addressed these points with additional analysis on JPEG compression, part-specific blur, adversarial training gains, YOLO detector reliability for HMB-R, scalability of the mask branch, lighter DPG variants, and supplementary PSNR/SSIM/MAE results. Reviewers acknowledged these clarifications, with several explicitly raising their scores or confirming acceptance.

Overall, the work is technically solid, novel, and practically relevant, with clear motivation and strong empirical results. I recommend acceptance.
Decision: Accept (poster)