LiveMoments: Reselected Key Photo Restoration in Live Photos via Reference-guided Diffusion
Authors: Clara Xue
Zizheng Yan
Zhenning Shi
Yuhang Yu
Jingyu Zhuang
Qi Zhang
Jinwei Chen
Qingnan Fan
Venue: ICLR 2026 Poster (2026)
Keywords: Live Photo
Reference-based Image Restoration
Conditional Image Generation
Motion Alignment
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We are the first to restore reselected key photos in Live Photos, achieving perceptual fidelity beyond existing solutions in real-world scenes.

Abstract: Live Photo captures both a high-quality key photo and a short video clip to preserve the precious dynamics around the captured moment. 
While users may choose alternative frames as the key photo to capture better expressions or timing, these frames often exhibit noticeable quality degradation, as the photo capture ISP pipeline delivers significantly higher image quality than the video pipeline. This quality gap highlights the need for dedicated restoration techniques to enhance the reselected key photo. To this end, we propose LiveMoments, a reference-guided image restoration framework tailored for the reselected key photo in Live Photos. Our method employs a two-branch neural network: a reference branch that extracts structural and textural information from the original high-quality key photo, and a main branch that restores the reselected frame using the guidance provided by the reference branch. Furthermore, we introduce a unified Motion Alignment module that incorporates motion guidance for spatial alignment at both the latent and image levels. Experiments on real and synthetic Live Photos demonstrate that LiveMoments significantly improves perceptual quality and fidelity over existing solutions, especially in scenes with fast motion or complex structures.

Published as a conference paper at ICLR 2026
LIVEMOMENTS: RESELECTED KEY PHOTO RESTORATION IN LIVE PHOTOS VIA REFERENCE-GUIDE DIFFUSION
Clara Xue-* Zizheng Yan-*
Zhenning Shi
Yuhang Yu²
Jingyu Zhuang*
Qi Zhang' Jinwei Chen' Qingnan Fan vvvo Bluelmage Lab
"ooleeee of Computer Science, Nankai University
{clara. xC277, fqncciiaa (gmail com
zizhengyang Sinkk wuh. cuhk, edu. cn
(a)
image
LQ target image
reselected LQ
iPhonet5 Pro (1-3)
vive X200 Pro (3)
LiveMoments
aligned Ref
guide
restoration
original photo
reseleeted LQ frame
guide
restoration
select
Key Photo Reselection in Live Photos
Visual Comparison
Figure I: Illustration Reselected Key Photo Restoration in Live Photos and visual comparison. While RefISR adopts external reference image with only semantic similarity, setting leverages both the reference and target images from the same Live Photo sequence, ensuring a shared temporal context. The proposed LiveMoments significantly outperforms the premium smartphones.
ABSTRACT
Live Photo captures both a high-quality key photo and a short video clip to preserve the precious dynamics around the captured moment. While users may choose alternative frames as the key photo to capture better expressions or timing, these frames often exhibit noticeable quality degradation, pipeline delivers significantly higher image quality than the video pipeline. This quality gap highlights the need for dedicated restoration techniques to enhance the reselected key photo. To this end, we propose Live:Moments, a reference-guien guided image restoration framework tailored for the reselected key photo in Live Photos. Our method employs a two-branch neural network: reference branch that extracts structural and textural information from the original high-quality key photo, and main branch that restores the reselected frame using the guidance provided by the reference branch. Furthermore, we introduce a unified Motion Alignment module that incorporates motion guidance for spatial alignment both the latent and image levels. Experiments on real and synthetic Live Photos demonstrate that LiveMoments significantly improves perceptual quality and fidelity over existing solutions, especially in scenes with fast motion or complex structures.
the photo capture ISP
Equull contribution. 'Corresponding author.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Unlike traditional photographs that capture a static frame, a Live Photo preserves the fleeting moments around shutter click. To achieve this, each Live Photo consists of two components: (a) a high-quality (HQ) key photd taken at the capture moment and (b) low-quality (LQ) video clip approximately 3 seconds, spanning moments around the key photo. This format not only enables dynamic visual recording, but also offers the flexibility for users to reselect preferred frame as the new key photo. However, these reselected frames often exhibit significantly reduced quality. While the original key photo is processed through the complete ISP pipeline with advanced enhancements, the alternative frames extracted from a compressed, low-latency preview stream and are further degraded by motion blur and sensor noise. To ensure optimal user experience, it is essential to restore the quality of the reselected frame to match that of the original key photo, while preserving content fidelity.
To this end, we introduce a new task: Reselected Key Photo Restoration in Live Photos, where the original key photo serves reference to guide the restoration of the reselected frame. We position this task as a sub-category of Reference--asee Super- Resolution (RefSR), with a unique setting that restores a single LQ frame guided by single reference from the same Live Photo sequence. Unlike conventional Reference- an HQ reference from external databases, our task leverages an in-sequence reference that ensures Video Super-Resolutiin
Image Super- Resol---soottion (RefISR), which restores the LQ image with
content consistency. Meanwhile, Reference-basee video sequences but struggles with high-resolution inputs (i.e. 4K), and methods are typically built on triple-camera smartphone datasets, with videos from different fields of view recorded simultaneously. In contrast, our task focuses on real Live Photos, where a single 4K frame is restored with guidance from temporally offset reference. This setting naturally involves more dynamic real-world scenes, making it novel and practical while remaining efficient in both time and memory. Furthermore, our task introduces unique challenges, including significant quality gap and motion misalignment between the reselected frame and the original key photo, due to subject movement or camera shake.
(RefVSR) enhances full
These challenges impose limitations existing RefSR methods for the task of reselected key photo restoration. Traditional RefTSR methods, typically based on CNNs or transformers (Zhang et al., 2019 Jiang al. 202T), D, adopt various feature matching strategies but are constrained by relatively small model size and the lack of strong pre-trained priors, making them insufficient for handling the diverse degradations and motion misalignments in Live Photos. Although diffusion-based RetISR methods demonstrate stronger generative capabilities. only two such methods have been explored to date and often produce unnatural textures. ReFIR (Guo et al.] (024)) relies on fxed-coefficient gating mechanism with limited robustness, while CoSeR (Sun et 20224) generates HQ references from CLIP-based embeddings that neglect local detail alignment and fuses features by prioritizing LR content, making it less suitable for reference-driven tasks. Beyond image-based settings, RefVSR methods remain confined to traditional designs and exploit full sequence (Lee et al., 2022) or single middle-frans frame (Kim et al. 2023) references with temporal propagation. Such designs are utilized to handle small misalignments but fail under the larger temporal offsets and quality gaps in Live Photos. In addition, single image SR (SISR) methods (Wang al. 2024 Wu et al.] 2024b) overlook the reference and often fail to preserve accurate structure and details in the presence of motion.
Therefore, we propose LiveMoments, a diffusion-based framework tailored for reselected key photo restoration in Live Photos. It leverages diffusion priors for fine-grained feature extraction and employs attention-baut based fusion to guide the RestorationNet in selectively incorporating well-aligned reference features within a shared feature space, enabling precise and targeted reference-driven conditioning. To address motion misalignment between the two inputs, we further introduce a unified Motion Alignment module that operates at both the latent and image levels. At the latent level, we propose a motion- guriidd-guidddd attention that injects spatially aligned guidance into the latent space for more coherent feature fusion. At the image level, we design patch correspondence retrieval strategy that captures patch-wise motion to locate the corresponding reference patches for consistent restoration in ultra- high resolution frames. To facilitate fair evaluation, we introduce comprehensive benchmark for this task, including a synthetic dataset, SynLive260, and two real-world Live Photo datasets, vivoLive 144 and iPhoneLive90, captured by consumer smartphones across diverse scenes. Furthermore, we adapt the no-reference metrics originally used in SISR and image generation to better suit our setting, where an HQ reference is available for evaluating result quality.
From Apple's official documentation:
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Our contributions can be summarized as follows:
To the best of our knowledge, we are the first to address the problem of reselected key photo restoration in Live Photos. We propose LiveMoments, inffusion-based framework tailored for this task, which leverages a dual-branch neural network with advanced feature fusion.
To mitigate spatial misalignment in Live Photos, a unified Motion Alignment module is introduced to inject the motion guidance at the latent level while ensuring image-level content consistency.
We establish a comprehensive benchmark consisting of three datasets and task-specific metrics. Extensive experiments show that LiveMoments significantly outperforms ofate-of-the-ard RefSR and SISR methods in both quantitative and visual results, even under challenging wolld scenarios.
2
RELATED WORK
2.1 DIFFUSION BARUSSIO--BASEDD SINGLE IMAGE REPEERRRSOOUIION (SISR)
Diffusion-based SISR restores the HQ images from a single LQ input via diffusion models, with recent research focusing on complex and unknown degradations. Building the remarkable generative capabilities
diffusion models demonstrated in text-to-image (T2I) tasks (Rombach
et al. 2022, Podell et al. 2023), recent works (Wang al. 2024 Lin et al. 2024 Wu et al. 2024b] et et al. 2024) adapt these pretrained backbones to SISR by leveraging control mechanisms (e.g., ControlNet (Zhang al.) (023b)). StableSR (Wang al.) 2024) fine-tunes time-aware encoder with the controllable feature warping module, while SeeSR (Wu et al. 2024b) uses degradation-aware text prompts. SUPIR (Yu et al. 2024) scales up generation by integrating large diffusion backbones with high-capacity adapters and datasets. CoSeR (Sun et al., 2024) utilizes pre-trained T2I model to generate HQ references from LR embeddings for guided restoration. More recently, one-step distillation techniques are adopted to significantly reduce the number of diffusion steps by directly initializing from the LQ input. OSEDiff (Wu al. (2024a) utilizes VSD loss (Wang et al., (023b) as regularization term, while TSD-SR (Dong et al. 2024) distills a multi-step SD3 model (Esser et al., 2024) into a one-step SISR solution via target score distillation. Despite these advances, most of the existing methods rely solely on generative priors and may produce visually plausible yet inaccurate content that sacrifice fidelity for perceptual richness. Such methods often fail to produce accurate results in real-world scenarios, where effectively preserving the original visual content is essential.
2.2 REFERENCE-BASED SUPER-RESOLUTION (REFSR)
RefSR aims to enhance LR inputs by leveraging external HR references. Image-based RefSR (RefISR) addresses single-image restoration and primarily focus on establishing accurate correspondence between the L.R input and the reference image, enabling effective texture transfer and detail refinement.
Farly methods explore various matching strategies, including feature warping (Zheng et al.) 2018). patch-level matching (Zhang et al. 2019:] Yang et al., 2020). and multi-reference fusion Zhhag et al. 2023a). In addition, SSEN (Shim et al. 2020) employs deformable convolution (Dai et al. 2017) for adaptive feature alignment. Building on this, C2-Matching (Jiang et al., 2021) introduces a contrastive correspondence network with teacher-student correlation distillation. while DATSR (Cao ct al. 2022) integrates deformable convolution with the Swin Transformer for enhanced performance. Recently, ReFIR (Guo al., 2024) proposes retrieval-enhanced architecture built diffusion-based SISR models, achieving reference-guided texture propagation without additional training. Video- based RefSR (RefVSR) extends reference-basse based techniques video super-resolution on smartphone datasets, where triple-camera systems simultancously record videos from different fields of view. By leveraging consecutive frames, these methods integrate multi-frame information with reference to enhance restoration, with only minimal time gaps between frames and the reference. Methods such as RefVSR (Lee et al. 2022) and RefVSR++ (Zou et al. 2025) employ the entire HQ video sequence reference and exploit bidirectional propagation to align multi-frame information, while ERVSR (Kim et al. 2023) improves efficiency by leveraging only single reference frame. While our setting can be regarded dataset construction and task formulation. To address the unique challenges posed by Live Photos, construct dedicated datasets and design task lask-specific architectures.
sub-category of RefSR, it departs from conventional paradigms in both
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Trainable Modules
Frozen Modules
Location IHe
Location of Is
Dual Branch Architechture
Degradation Process
Patch Retrieval Inference
Concat
Add
Motion Alignment Medule
Eitender
ReferenceNeet
Attention
original HQ key photo THo
Kerd
RAFT
Motion
Ptante
estimated optical Limmfa
Decoder
VAE
Restoration
key photo It
Live Photo
Figure 2: Overall architecture of LiveMoments. After the fixed VAE encoder, the original key photo and the reselected LQ frame are fed into the ReferenceNet and RestorationNet, respectively, and fused via cross cross-attention For latent-level motion alignment, the optical flow estimated with fixed RAFT model and encoded with Motion Encoder, which is further injected into the cross-attention as an additive bias. For image-level alignment, Patch Correspondence Retrieval (PCR) strategy is adopted during the inference ensure spatial consistency when using the tiling strategy.
METHOD
3.1 PRELIMINARY
Given a Live Photo, we denote the reselected IQ frame as TL. E Hxx and the original HQ key photo as Ho E Hx The goal of our task reconstruct a high-quality version of the reselected frame, denoted as T.., that matches the visual quality SH while preserving the content fidelity of L... Thus, the task formulated learning restoration model Go parameterized by which takes (L Ho) as input and predicts IH.-
Since real-world Live Photos lack HQ ground-truth counterparts for reselected frames, we construct the training dataset using HQ video sequences. For example, the ground-trute truth I of the reselected frame and the original key photo I are extracted with clear temporal gap. I is degraded to produce the LQ reselected frame I..- (see Section 4.1 for more details). We employ flow match- ing (Albergo & Vanden- Vanden-Eijnden, 2022; Esser et al. 20244) to train the diffusion model. Flow matching aims to transform Gaussian noise into a target distribution by learning an appropriate velocity field. In particular, Rectified Flow defines the forward process as Fe a(t)ao + ((t)e. where axt) 1
and ((() The training objective is Ex,,, [xx dr. dd://dl] where f denotes the neural network that parameterizes the velocity field in the Rectified Flow. To adapt flow matching to image-to-ines to-imaee translation tasks, several approaches condition the generation process on source image. aiming to synthesize the target image from Gaussian noise. However, these methods often suffer from issues such hallucinated textures. Inspired by bridge matching (Chadebec et al.) 2025 Liu al. 2023 Shi et al. 2023) that directly learns the velocity field between the source and target distributions, we leverage an objective that focuses on learning the velocity field between It, and IH.- Specifically, we define the forward process
2 = a + +()(t)zL + o(t)e
where 2Hs and 21s are the latent representations obtained by VAE encoder in the setting of latent diffusion. The objective is to learn the velocity field:
dzc
La Br,,, (Go
dt
The details of (()),(()) (1) can be found in the supplementary material.
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Published as a conference paper at ICLR 2026
3.2 OVERVIEW OF LIVEMOMENTS
Our setting is characterized by temporal dependency and reference-target correlation. Specifically, the reference and degraded frames are sampled from the same Live Photo sequence with a clear temporal offset, yet they retain scene-level coherence compared to static or externally sourced references. Consequently, effective restoration of the reselected key photo requires leveraging fine-grained details from the reference frame. To achieve this, the proposed LiveMoments consists of three key components, as shown in Fig.21 a RestorationNet performs conditional denoising on the noisy latent of Lss ReferenceNet encodes the original key photo HH to provide high-quality guidance; unified Motion Alignment module achieves fine-grained alignment at both the latent and image levels.
3.3 DUAL-BRANCH ARCHITECTURE
Inspired by the success of reference-guidee generation in video tasks (Hu 2024 Xu al., 2024h. we adopt a similar dual-brand design for reference- restoration, leveraging both the diffusion priors and the attention mechanisms of the pre-trained T2I diffusion models. Unlike CLIP-based structure (Radford et al. (021)), which focuses on global semantics for low-resolution inputs, the ReferenceNet preserves high-resolution features and fine- ane-grained details that are essential for high- fidelity restoration. Structurally, it mirrors the denoising backbone, enabling weight initialization from pre-trained checkpoints and better feature alignment with the RestorationNet. In our implementation, both branches are built on the Stable Diffusion 3 (SD3) architecture (Esser et al., 2024), which provides a powerful generation backbone. The original key photo HH is first encoded into the latent space by frozen VAE encoder, then processed by DiT-based ReferenceNet to obtain detailed features. These features are integrated into the main branch through cross-attentie where the key and value from both branches concatenated as follows:
QQK, Krer] Vd
Cross attn Softmax
[V, Viet),
where Q is the query from the attention layer of the RestorationNer, Ker and Vie are the key and value from the ReferenceNet, while K and V are those from the RestorationNet. [. denotes the concatenation operation, and d is the channel dimension. This interaction enables the model to adaptively select and transfer textures and structural information from the reference image rather than relying solely on coarse semantic alignment.
3.4
MOTION ALIGNMENT MODULE
3.4.1 LATENT SPACE MOTION ALIGNMENT
While the dual-branch structure enables implicit feature matching and detailed information transfer, it is often insufficient for the task of reselected key photo restoration Live Photos. The reselected frame Lis typically suffers from motion blur and subject displacement, making it difficult to establish accurate correspondences and align with the original key photo. Moreover, the significant quality gap between the HQ key photo and the degraded reselected frame further hinders effective feature alignment and leads to unreliable fusion.
To address these challenges, we design a pettion-guied attention that introduces explicit motion guidance into the latent space. In Live Photos, where temporal dependency naturally exists, optical flow serves as an intuitive mechanism for establishing spatial correspondence. Distinct from previous flow-based restoration methods, we transform the estimated flow into motion embeddings and incorporate them into the cross-attention, thereby providing alignment priors to guide attention toward relevant regions. Specifically, we utilize a pre-trained RAFT model (Teed & Deng 2020) to estimate the optical flow OLo-LLs. which serves as a dense pixel displacement field between the degraded original key photo Ito and the reselected LQ frame 1s.. During training, L.o is synthesized by applying the same degradation parameters those used for the reselected frame It.. At inference time, since IL. suffers from real-world degradation in Live Photos, we simulate the corresponding degradation on Iн to narrow the quality gap and obtain more reliable motion estimation. To encode the estimated motion, we introduce a lightweight Motion Encoder with convolutional layers and SiLU activations that transforms the raw flow field into motion embeddings. These embeddings
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Published as a conference paper at ICLR 2026
(K,, top-left curner Pia (R,,9)): top-left P
(dx, averuge displacement
reselected patch
vector within Pi
(Ax,
averine
(Ax,AA))
reselected LQQ frame
original key photo
unaliigned patch aliened Ref patch
Figure 3: Illustration of the proposed Patch Correspondence Retrieval (PCR) strategy. The average displacement within a patch (the dense displacement field contained in Pi.)) used to shift the top-left corner (zi, y:) to (f,, fi), then we crop the aligned reference patch Pio from Tho- On the right, we compare the reselected patch PL. unaligned Ref patch Pio and the aligned Ref patch Ho
then incorporated into the cross-attention mechanism an additive bias to the reference key features:
QIK, Knt Kref + -io-stal") VV, Vier]:
Cross_atth Softmax
Vd
where Lo denotes the motion embeddings derived from the dense pixel displacement field Oo The encoded relative motion acts as a spatial bias that facilitates the query to attend to aligned regions, thereby improving restoration under misaligned scenarios.
3.4.2 IMAGE SPACE MOTION ALIGNMENT
Live Photos typically have ultra-high resolutions (i.e., 3072 x 4096 for the original key photo), which require patch-wise inference through a tiling strategy due to GPU memory limits. However, subject motion often causes pixel-level misalignment between patches of LL and ИHo, leading to content inconsistency that undermines reference-guice guidedd restoration. To mitigate this issue, and as complement to motion-guided guided attention, we propose Patch Correspondence Retrieval (PCR) strategy to stabilize local matching and improve content alignment between the reselected patches from Lt. and the reference patches from IH during inference. Integrated into the tiling process and applied before the VAE encoder, uses the estimated displacement field to locate the reference patch, ensuring that latent-space processing operates spatially aligned inputs. Unlike pixel- wise warping, the proposed alignment is performed at the patch level rather than on individual pixels, which is consistent with the tiling-based inference pipeline and naturally preserves spatial consistency. As shown in Fig. 3 we first estimate the optical flow OLs-~Lo from Lts to L For patch PÊ. pxxxxx cropped from LL with the standard tiling strategy, compute the average displacement vector within the patch as:
pxp
pXp
1
1
(Az;, Anh)
1
where (12,, (2.) represents the x- and y-axis components of the dense displacement field at pixel j. obtained from OLL-LOo The top-left corner (& ii) the aligned reference patch Pi cropped from Ho is then computed by shifting the top-left corner (x,, yi) of Pl.:
(o v.) (x; + Ar, Vi Ay:).
(6)
The aligned reference patch Pr is then cropped from HH with the patch size As shown in Fig. pi achieves content consistency with P Pi., compared the unaligned reference patch that directly cropped from the same spatial location as Pi patch is cropped the same location as pr. to ensure spatial consistency of the network inputs.
3
L.. With the tiling strategy, the corresponding optical flow

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Published as a conference paper at ICLR 2026
4 EXPERIMENT
4.1 EXPERIMENTAL SETTINGS
Training Datasets. Since there are no existing datasets specifically for reselected key photo restora- tion in Live Photos, we construct the training set based on high-quality video data. For each sample, we select the HQ ground truth Js of the reselected frame and the original key photo IHo with a clear temporal gap. In particular, we extract frames from the first 3,000 videos of the 2K-resolution DL3DV dataset (Ling et al., 2024), large and multi-view video dataset covering over 10,000 scenes. The frame interval is set to 5, resulting in 50,400 image pairs at resolution of 1024 1024. To further enrich the dataset with various motion patterns, we additionally collect 4K videos from the internet, obtaining 25,000 image pairs at the same resolution. To simulate degradations in Live Photo videos, we utilize the Real-ESRGAN (Wang et al.] 202I) pipeline to obtain the LQ reselected frame L with parameters specifically adjusted to better align the real-world degradations in mobile photography.
Test Datasets. For model evaluation, we construct a synthesized dataset, SynLive260, along with two real-world Live Photo datasets, vivoLive144 and iPhoneLive90, collected from high-end consumer smartphones. VivoLive 44 and iPhoneLive900 consist of 144 and 90 Live Photos captured with vivo X200 Pro and iPhone 15 Pro, respectively. In both datasets, the LQQ reselected frames are manually extracted from the associated Live Photo video clips. These two datasets cover diverse dynamic scenes, including indoor and outdoor environments, street views, portraits, pets, and objects with motions from camera movement and subject dynamics. SynL.ve2600 is built from 182 internet videos, covering various scenarios. After frame extraction, we obtain 260 image pairs of the HQ reselected frame I Hs and the original key photo Ho- The HQ reselected frame is then degraded using the Real-ESRGAN pipeline with a x2 downsampling factor to generate the corresponding LQ reselected frame Ls. simulating degradations in Live Photo videos.
Evaluation Metrics. On both the synthesized dataset and real-world Live Photo dataset, we adopt no- reference metries for evaluation. We apply four metries originally designed for SISR: NIQE (Zhang ct al. 2013), MUSIQ (Ke et al. (22TT) CLIPIQA (Wang et al. 2023a), and MANIQA (Yang et al. 2022 However, unlike traditional SK settings, our task provides access to the original high-quality
key photo IH as a reference. This allows us to extend these metrics into a relative no-reference form
by computing the normalized deviation between the restored reselected frame THe and Lho:
metric (T THo) metric([n.) - metric((Ho)
metric(THo)
where metric(-) denotes one of the above no-reference metrics. This formulation quantifies the quality gaps between the restored image TH and a known HQ reference. To further assess perceptual alignment with HHo we introduce two relative-referencce metrics, CLIP-Q and DINO-Q, derived from CLIP-I and DINO from DreamBooth (Ruiz et al. 2023 Following CLIPIQA, we remove the positional embeddings from both models to support hig high-resolution inputs and eliminate resizing- induced distortions, which is crucial for faithful quality assessment. We also calculate full-reference metrics on the synthesized dataset: PSNR, SSIM (Wang et al., 2004). LPIPS (Zhang et al.) 2018 and DISTS (Ding et al. (220)), where PSNR and SSIM are computed on y-channel in the YCbCr space. FID is also eval on SynL 260. Since it requires downsampling 4K images to a low resolution, we compute patch-wise FID by cropping images into 512 x 512 patches, which ensures that the evaluation resolution is consistent with recent restoration works (Dong et al. 2024).
Implementation Details. Both the ReferenceNet and the RestorationNet are initialized from the pre- trained weights of SD3-medium. We only train the MM-DiT backbones of both networks along with the Motion Encoder, while keeping the VAE encoder and decoder frozen. Training starts with an initial learning rate of 5e and takes roughly 48h with 2 NVIDIA H20 GPUs and a batch size of 8. Inference performed using a tiling strategy with a patch size of 1024 x 1024, which is kept identical to the training patch size for stable and fair evaluation.
4.2 COMPARISON WITH EXISTING METHODS
We compare LiveMoments with three categories of state-of-the the-art methods: (a) RefISR methods, including TTSR (Yang al. 2020), C2-Matching (Tiang al. 202T), DATSR (Cao al., 2022).
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Table Quantitative comparison with RefSR and SISR methods on real-world Live Photo datasets.
The best results are highlighted in bold. Here, NI,e denotes NIQE,er MU,e denotes MUSIQ CAre denotes CLIPIQA and MAre denotes MANIQA
ivvo
iPhonel Livel)
Method
N1.
MU,c
CAre
MA
C
DINO-QT
NIx
MU
CA.e 1 MA.,
CLIP-QT
DINO-QT
TTSR
0,2050
0.3545
0.1928
0.1400
0.9626
0.8304
0.3069
0.2858
0.2593
0.1974
0,9505
0,8689
C2-Matching
0.2047
0.3512
0.1929
0.1390
0.9623
0.8298
0.3147
0.2800
0.2791
0.1978
0.9505
0.8685
DATSR
0.2212
0.3573
0.1951
0.1386
0.9621
0.8261
0.3324
0.2859
0.2853
0.1963
0,9500
0.8657
MRefSR
0,2276
0.3537
0.1916
0.1371
0.9616
0.8216
0.3408
0.2900
0.1978
0.9499
0.8652
CoSeR
0.1953
0.1865
0.2752
0.1080
0,9658
0,9197
0.1774
0.2492
0.1564
0.0784
0,9608
0,8618
ReFIR (SeeSR)
0.3258
0.3042
0.8606
0.2731
0.9582
0.8156
0,2895
0.3605
0.5111
0.1429
0.9467
0,7319
ReFIR (SUPIR)
0.4665
0.2318
0.3571
0.1559
0.9201
0. 8783
0,4336
0.2778
0.2224
0.0876
0.9190
0,8105
RerVSk
0.3798
'0.31S"
0,2142'
'6.167
"0,9609
0.3385
0..2226
0.2332
"6.3116"
0.2810
0.9412
0.8431
ERVSR
0.3314
0,4012
0.3457
0.1459
0.9597
0.8137
0.3635
0.3188
0.4569
0.2030
0.9448
0.8607
StableSR
0.2833
0.7447
0.2250
0.9458
0.3837
0,9466
DMMBIR
0.4228
0.3213
0.9023
0.3477
0.5397
0.1705
0.7727
SecSR
0.2767
0.2916
0.8168
0.2331
0.9606
0. 0.8269
0.2957
0.3511
0.5174
0.1408
0,9445
0.7253
SUPIR
0.2703
0,2545
0.8275
0.1115
0,9407
0.8559
0.1805
0,3429
0,4924
0.0738
0,9422
0.7908
OSEDiT
0.2694
0.3191
0,8206
0.2444
0.9541
0.8536
0.2750
0,3832
0.4698
0.12600
0.9444
0.7525
TSD-SR
0.3359
0.3133
0.8519
0.2205
0.9476
0. 0,8636
0.2963
0,3956
0,5142
0.1210
0.9477
0.7917
LiveMoments
0.0990
0.0893
0.0809
0.0556
0.9805
0.9629
0.0801
0.1230
0.1361
0.0543
0.9842
0.9466
reselected LQ frame
cropped LQ frame
TSDSR
DATSR
RefVSR
original
ReFIR (SUPIR)
LiveMoments
aligned original key photo
reselected LQ frame
cropped
TSDSR
DATSR
RefySR
original key photo
CoSeR
ReFIR (SUPIR)
LiveMoments
aliigned original key photo
Figure 4: Visual comparison on the two real-world Live Photo datasets: vivoLive 144 (top) and WhoneL.ive90 (bottom). The aligned original key photo is cropped manually for better comparison.
and MReISR (Zhang et al. 2023a as well as diffusion-based methods CoSeR (Sun et al. 2024 and ReFIR YYu et 2024) (based on SISR methods SeeSR and SUPIR). Designed for SISR, replace the synthesized reference in CoSeR with the actual one. (b) RefVSR methods, including RefVSR (Lee) al. 2022) and ERVSR (Kim et al., 2023), for which we additionally supply full video inputs to match their model design. (c) Recent diffusion-based SISR methods, including multi-step methods
(StableSR (Wang et al. 2024), DifTBIR (Lin et al. 2024) SeeSR (Wu et al. 2024b), SUPIR (Yu
al. 2024) and single-step methods (OSEDiff (Wu ct al. 2024a), TSD-SR (Dong et al. 2024
Real- World Live Photo Datasets. The quantitative results on the two Live Photo datasets are shown in Tab. [ which can be found that our method achieves SOTA in both two datasets among all metrics. We note that commonly used no-reference metrics may be suboptimal for evaluating reference reference-guided restoration tasks, since they ignore the reference and often favor visually rich but inaccurate results. As illustrated in Fig. 5,, a restored image with high quality scores but large deviation from the reference often contains visually implausible artifacts, whereas smaller deviations yield perceptually closer
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Published as a conference paper at ICLR 2026
Table 2: Quantitative comparison with RefSR and SISR methods on the synthetic dataset SynLive260.
The best results are highlighted in bold. Here, Nl,e denotes NIQE,er MU,e denotes MUSIQ CA,e denotes CLIPIQA and MAre denotes MANIQA
Method
PSNRT
SSIMI
LPIPSI
DISTS!
FID
NI. 4
MU,e
CA,e 4
MA. 4
CLIP-Q1
DINO-QT
TTSR
31.74
0.8778
0,2416
0.1259
18.53
0.4254
0.3885
0.1996
0.2209
0.9621
0.8373
C2-Matching
31.85
0.8782
0.2419
0.1250
18.51
0.4354
0,3873
0.2051
0.2199
0.9619
0.8391
DATSR
31.84
0,8783
0.2417
0.1251
18.50
0,4380
0.3876
0.2034
0.2186
0,9618
0.8388
MRefSR
31.84
0.8782
0.2420
0.1252
18.73
0.4342
0.3886
0.2058
0.2195
0,9612
0.8374
CoScR
27.60
0.8136
0.2436
0.1135
13.92
0.2603
0.2548
0.6380
0.1028
0.9699
0.8924
ReFIR (SeeSR)
27.44
0.7942
0.2873
0,1530
28.26
0.3378
0.4028
1.2814
0.1837
0.9558
0.7934
ReFIR (SUPIR)
24.20
0.7024
0.3992
0.1779
33.95
0.4968
0.3273
0.7808
0.1491
0.9238
0.8295
RefVSR
26.28
0.8637
'022937
0.1183
15.10
6.3913 0,3003
0.1964
"6.2783"
6.9631
0.8609
ERVSR
26.26
0,8016
0,3335
0,1306
20.22
0,4035
0,4021
0.2819
0,2129
0.9576
0,8327
"ttaleSR""
26.49
6.76199
'032388
0.1491
23.77'
0.3912
0.3951
1.1322
"6.1573"
6,9464
688386
DitBIR
25.98
0.6927
0,4421
0.1958
36.39
0.3771
0.4199
1.3705
0.2260
0.9167
0.7824
SeeSR
27.25
0.7865
0.2981
0.1571
28.89
0.3602
0.3929
1,2805
0.1853
0.9538
0.7940
SUPIR
25.96
0.7309
0.3660
0.1762
31.53
0.2875
0.3741
1.3061
0.1083
0.9335
0.7869
OSEDiff'
26.81
0.7882
0.2915
0.1412
27.17
0,3325
0.4132
1.1931
0,1669
0.9566
0,8220
TSD-SR
25.34
0.7379
0,3090
0,1625
24.03
0,4081
0,4595
1.3084
0,1641
0.9439
0.8208
LiveMoments
31.65
0,8990
0,0828
0.0365
4.00
0.0911
0.0720
0.1155
0.0495
6,9950
0.9740
Table 3: Ablation study of the network design vivoLive144, All warp operations are applied on
the original key photo, denoted as the reference image (Ref). The best results are highlighted in bold.
Method
NIQE,, 1 CLIPIQA,, 1 MANIQA,.
CLIP-QT
DINO-QT
RestorationNet
0.1677
0.2348
0,1105
0,9690
0.9081
Restoration ReferenceNet
0.1097
0.0823
0.0631
0.9792
0,9539
RestorationNet ReferenceNet + warp Reflmage
0.1034
0.0873
0.0573
0.9774
0,9480
RestorationNet ReferenceNer + warp RefT atent
0.1
0.0850
0.0622
0.9774
0.9437
RestorationNet ReferenceNet + warp RefKV
0.1183
0.0853
0.0657
0,9776
0.9456
LivéMoments (full modefi)
6.0990
0.08009
0,0556
'0,9805
"0.9229
results. This reveals the limitation of no-reference metrics and highlights the effectiveness of the proposed relative no-reference metric. The visual results are provided in Fig. where we compare representative methods with strong quantitative results. Our method effectively restores the reselected frame with details comparable to the original key photo without introducing artifacts, even when the input suffers from motion misalignment. More results are provided in the supplementary material.
Synthetic Dataset. The quantitative results on SynLive260 are shown in Tab. 2 can be found that our method achieves the best performance among nearly all metrics. Although it shows lower PSNR, this mainly reflects the limitation of full-reference metrics in real-world restoration tasks, as discussed in (Yu et a 2024). In contrast, the proposed task-specifi metrics and qualitative comparisons (in the supplementary material) better capture the perceptual fidelity of the results.
4.3 ABLATION STUDY
Effectiveness of the dual-brande architecture. To validate the effectiveness of our dual-branch design, we first evaluate baseline that uses only the RestorationNet without any reference guidance. As shown in the 1-st row of Tab. 3 performance drops across all metrics, highlighting the importance of introducing the ReferenceNet and the original key photo to enhance restoration quality.
Effectiveness of the motion-guided attention. We evaluate the effectiveness of our motion- motion-guided attention by exploring different strategies for incorporating dense correspondence for motion align- ment into the network. Specifically, we evaluate a model without latent-level alignment and several variants that inject motion information at different stages of the ReferenceNet, as shown in rows 2-5 in Tab. 3 All models are evaluated under our patch correspondence retrieval strategy to ensure spatial consistency during tiled inference on our vivoLive 144 dataset. Among them, the proposed notion-guided attention outperforms other variants. This demonstrates the advantage of injecting aligmment to guide the feature matching within the cross-atts antss-attention mechanism, rather than relying on explicit warping. We provide the results on SynLive260 in the supplementary material.
Effectiveness of the Patch Correspondence Retrieval (PCR). We present challenging case with large motion and complex textures that hinder patch-level alignment, to validate the effectiveness of

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Published as a conference paper at ICLR 2026
X
preferred mo-reference metrics
preferred by
human
relative-refrrence metrics
LiveMomentss
aligned key photo
reselected patch
aligned Ref patch
NIQEL: 2,2079
NIQEL: 5.4055
NIQR 0,50800
NIQEs 0:110
MANIQAT: 0.6223
MANIQAT: 0,3708
MANIQA, 0,4408
MANIQA 0,1611
original key photo
LiveMoments PCR (b) w and w/o PCR comparison
LiveMoments PCR
(1) metrics comparison
Figure 5: (a) Comparison between no-reference metrics, the proposed relative no-reference metries, and human visual preference in evaluating the proposed task. The aligned key photo is cropped manually for better comparison. (b) Comparison between LiveMoments with (w) and without (w/o) the proposed Patch Correspondence Retrieval (PCR) strategy when processing 4K images. Patches are cropped for clearer visualization.
PCR. As shown in Fig. 5 our method establishes accurate correspondence between the two inputs, leading more consistent and visually coherent restoration. These results highlight the robustness of our strategy under real-world scenes. We provide more visual results in the supplementary material.
CONCLUSION
We propose LiveMoments, diffusion-based network designed for reselected key photo restoration in Live Photos. LiveMoments employs a dual-branch neural network with cross-attention between the branches transfer detailed structural and textural information from the original high-quality key photo to the reselected low-quality frame. To address the challenge of large motion misalignment, we introduce a unified Motion Alignment module that facilitates alignment between the reselected and original key photos in both the latent and image spaces. Furthermore, we present a dedicated benchmark comprising both real and synthetic Live Photos, along with task-specific evaluation metrics for comprehensive comparison. Extensive experiments three datasets demonstrate that LiveMoments significantly outperforms existing methods across both quantitative metrics and visual quality, particularly in challenging scenarios.
ETHICS STATEMENT
Our work focuses on restoring reselected key photos Live Photos for improving mobile photography. The synthetic dataset SynLive260 is built from publicly available video sources with no personal information. The real-world datasets (vivoLive 144 and iPhoneLive90) were collected with the consent of participants. Before public release, all identifiable faces will be removed anonymized to minimize privacy concerns. Our method is not intended for identity recognition or generation, and the released resources will be restricted to academic research in image restoration. Potential misuse minimal given the planned anonymization and the academic scope of this work.
REPRODUCIBILITY STATEMENT
We provide implementation details in Sec. 4.1 and Appendix covering both training and inference. Details dataset preparation and evaluation metrics are also included in Sec. [4.]] We plan to release code and datasets to enable reproducibility and encourage further research.
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A THE USE OF LARGE LANGUAGE MODELS (LLMS)
We used large language models (LLMs) only to polish the writing and check grammar. They were used generate ideas, design methods, influence experimental results.
B MORE IMPLEMENTATION DETAILS
B. LOSS DETAILS
We define the forward process of the flow matching as,
21 = a(t) + B(t)2 + o(t)e,
(8)
and the objective:
dzg dl. 112
La = Er,,, (Go
(9)
Specifically, we define the coefficients as,
at) = (1 (0.2 -)
t)t
B(t) =
(10)
0.2
0.2
(t) t,
with te [0, 0.2].
(11)
can be observed that att), ((t) and (t) are differentiable and sum for every t. Moreover, when t = 0, 2 2H.,, and when t = 0.2, 24 = 0.82,., + 0.2c. This formulation adheres to the flow- matching framework, with the only deviation from the classical setup being the initial distribution: instead of pure Gaussian noise, we use biased mixture defined as 2mir = 0.821, + 0.2c. The learning objective is to estimate a velocity field that transports Emiz + ZH, within a narrow time window of length 0.2.
The core intuition is that low-quality (i.e., zI.,) and high-quality (i.e., =H,) images share most of their low-frequency information. In other words, the primary difference between them lies in the high-frequency details. As a result, applying the full computational cost of denoising a completely random image-as as is done in classical diffusion flow-matching approaches- is largely inefficient.
Therefore, we apply only a 20% Gaussian noise perturbation, which preserves low-frequency infor- mation while introducing stochasticity maintain well-posed learning problem, e.g., preventing the collapse of the generative diffusion prior. Furthermore, the ODE is integrated over a shortened time interval, L [0, 0.2). significantly reducing the sampling path length. As result, inference process requires only 6 sampling steps, making it substantially faster than most diffusion-based image generation models. More importantly, due the reduced stochasticity, the predictions become more deterministic- an advantageous property in image restoration scenarios where consistency is critical.
B.2 TRAINING DETAILS
Following prior work, we perform aross-attention across all DiTs Stable Diffusion Medium for full feature fusion, involving both image and text embeddings. Since the reference image provides rich structural and textural cues, we use an empty text prompt during training and inference. We fine-tune the image branches in both the DiT-based ReferenceNet and Restoration while keeping the text branch fixed to fully exploit the HQ guidance.
B.3 COMPARISONS ON COMPUTATIONAL COSTS
LiveMoments processes a 1024x1024 patch 1.89 seconds under FP16 inference on an H20 GPU (14.6 GB memory, 98.91 TFLOPs). For 4K Live Photo (i.e., 3072 x 4096), the total processing time is about 40 seconds. To further clarify the computational cost, we report the parameter count, as well as the parameter-only VRAM and the inference-time peak VRAM for all components of LiveMoments in Tab. measured when processing a 4K Live Photo.
We also provide detailed comparison of parameter count, FLOPs, peak memory usage, and inference time between our method and the iiffusion-based RefISR and SISR methods. All measurements conducted single NVIDIA H20 GPU and run in mixed precision under resolution of
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Table Comparisons branch-wise parameter counts and VRAM, measured when processing
4K Live Photo.
Component
Purameter Counts (M)
Parametcr--nly VRAM (MB)
Inference-time Peak VRAM (MB)
VAE
83.82
159.87
12368.33
ReferenceNet
2028.33
3868.73
10090.78
RestorationNet
2084,95
3976.73
10091
RAFT
5.26
10.03
13180.38
Motion Encoder
66.09
126.06
9940.00
Table Comparisons with RefISR and SISR methods on parameter counts, FLOPs, peak memory usage and inference time resolution of 1024 x 1024 on single NVIDIA H20 GPU.
Type
Method
Parameter Counts (M)
TFLOPs
Peak Memory (GB)
Inference Time (8)
OSEDiff
1294.38 470.93 (DAPE)
20.53
8.66
0.43
Time-Step Distilled Methods
TSD-SR
2207.33
21.91
8.60
0.24
CoSeR
2655.52
32.74
48.89 83
ReFIR (SeeSR)
2039.83 470.93 (DAPE)
1560.70
17.83
ReFiR (SUPIR)
4801.18
2672.70
31.67
StableSR
1554.64
2236.31
334.53
164.67
Non-Distilled Methods
DifiBIR
1683.45
691.43
26.02
SceSR
2039.83 470.93 (DAPE)
741.42
12.07
14.54
SUPIR
4801.18
1200.65
54.25
16.98
LiveMoments
4268.45
98.91
14.53
1024 x 1024, except for CoSeR and DifBBIR, which do not support mixed precision inference at that resolution. As shown Tab. [ our LiveMoments achieves the lowest TFLOPs and the shortest inference time among the multi-step methods, while maintaining competitive model size and peak memory usage to others. Although one-step diffusion methods such as OSEDiff and TSDSR benefit from distillation-basee training and therefore exhibit faster inference and lower computational cost, LiveMoments achieves a trade-off between computational cost and restoration quality.
C
USER STUDY
We compare LiveMoments with three representative RefSR methods: a traditional method (DATSR), diffusion- based approach (ReFIR), and the strongest quantitative baseline (CoSeR). A total of partici- pants took part in the vote. They best matches the visual quality of the reference while preserving the content of the reselected LQ frame. As shown in Fig. 6 our method receives 69.62% approval rates, indicating its effectiveness.
asked to select the result that
Figure 6: User study results.
D MORE ANALYSIS OF THE PROPOSED RELATIVE NO-REFERENCE METRICS
D.1 CORRELATION BETWEEN RELATIVE NO-REFERENCE METRICS AND HUMAN PREFERENCES
Using the same three baselines in the main user study (DATSR, ReFIR. and CoSeR), we further conducted a maaking-based experiment to analyze the correlation between the relative no-reference metries and human preferences. We randomly sampled 15 images from vivoLive 44 and 10 images from iPhonel.veePe A total of 15 participants are invited to rank the images based on perceptual similarity to the reference.
In Tab. 6 the Spearman and Pearson correlations show that all four metrics maintain positive, moderate alignment with human perception across devices and ISPs. These values are comparable to those reported in generic NR-IQA methods without task-specific training such as CLIPIQA (typically 0.36-0.74), despite our much smaller real-world dataset. This indicates that the proposed relative no-reference metrics remain meaningfully correlated with human perception.
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Table 6: Spearman and Pearson correlations between relative no-reference metrics and human perception on real-world Live Photo datasets.
vivoLivee44
iPhoneLivets
Metric
Spearmant
Pearson"
Spearman"
Pearsonf
NIQE,,
0.493
0.518
0.604
0.560
MUSIQ
0,585
0,543
0,414
0,440
CLIPIQA,
0.558
0.542
0.486
0.495
MANIQA,,
0.535
0.548
0.370
0.413
X
preferved
preferred ue-refereace metrics
preferred
no-reference metrics
relative-reeerence metrics
relative-relerenn metrics LiveMoments
CoSeR
LiveMoments
aligned key photo
TSDSR
aligned photo
CLIPIQAT: 0,5867
CLIPIQAT: 0.4266
NIQEL: 2.0735
NIQEL: 2.7610
CLIPIQA,, 0.2224
CLIPIQA 0.111
NIQE,, 4: 0.4873
NIQE, 4: 0.3174
MANIQAT: 0.5033
MANIQAT: 0.4235
CLIPIOAT: 0.7990
CLIPIQAT: 0.6824
MANIQAre 0.1184
MANIQA,, 0.0588
CLIPIQA, 0,190
CLIPIQA, Ar 0.0165
SeeSR
LiveMoments
aligned key photo
ReFIR (SeeSR)
LiveMoments
aligned key photo
MUSIQT:
MUSIQU:
MUSIQT:
MUSIQI: 40.34
40.34
MUSIQ,, 1; 0.0308
MUSIQ,, 0.0126
MUSIQ, 0.3663
MUSIQ,e 4: 0.0081
CLIPIQAT: 0.6246
CLIPIQAT: 0.4153
MANIQAT: 0,5203
MANIQAT: 0,3902
CLIPIQA, 0,519
CLIPIQA, 0.0114
MANIQA,, 0,3054
MANIQA,, 0.0210
SUPIR
LiveMoments
aligned key photo
ReFTR (SUPTR)
LiveMoments
aligned key photo
NIQEL: 3,7756
NIQET: 4.2253
CLIPIQAT: 0.3558
CLIPIQAT: 0.1968
NIQE, 4: 0,3111
NIQE, 4: 0.2291
CLIPIQA,E 4: 0.6378
CLIPIQA, 0.0941
MUSIQU: 55.61
MUSIQI: 50.70
MANIQAT: 0.4587
MANIQAT: 0.3883
MUSIQ,, 0.1288
MUSIQ,e 0.0783
MANIQA,, 4:0.1764
MANIQA,, 4: 0.0042
Figure 7: Comparison between no-reference metrics, the proposed relative no-reference metries, and human visual preference in evaluating the key photo reselection task. The aligned key photo is cropped manually for better comparison.
D.2 COMPARISON OF NO-REFERENCE METRICS WITH THE PROPOSED RELATIVE NO-REFERENCE METRICS
We present additional experiments in Fig. demonstrate that the widely-used no-reference metries may have limitations in assessing image quality under the task of key photo reselection. It can be observed that these metrics prefer rich texture but far from real appearance results.
E ADDITIONAL. ABLATION RESULTS
E. ABLATION STUDY ON SYNLIVE260 DATASET
We additionally provide ablation results on SynLive260 with ground-truth in Table While our method does not achieve the absolute best scores in distortion-orientedd metrics such PSNR and
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Table 7: Ablation study of the network design on SynLive260. All warp operations are applied on the
original key photo, denoted as the reference image (Ref). The best results are highlighted in bold. Here, CAye denotes CLIPIQA and MAre denotes MANIQA
Method
PSNRf SSIMT LPIPS, DISTS
CA..
4 MA.. 4 CLIP-QT
DINO-Qf
RestorationNet
30.42 0,8610 0.1685
0.0793
0.1721
0.0927
0.9841
0.9195
RestorationNei ReferenieNei
31.47
0,8980 0.0837
'60338
0.1246
"6.0586
"09949
00.9433"
RestorationNet ReferenceNet + warp RefImage
31.49
0.8927
0.0968
0.0475
0.1569
0.0546
0.9934
0.9556
RestorationNet ReferenceNet + warp Refl atent
atent
31.84 0.8986
0.0924
0.0441
0.1305
0.0618
0.9936
0.9566
RestorationNet ReferenceNet warp RefKV
31.82
0,8991
0,0895
0.0435
0.1306
0.0645
0.9940
0.9580
LiveMonients (full inodé1)
31.65
0.8990
0.0828
0.0365
0.1155
0.0495
0.9950
0.9740
Table Ablation study of different training degradation settings on vivoL.ve 144.
"Customized Live
Photo degradation" is the setting used in the main experiments. Best results are highlighted in bold.
Degradation Type
NIQE,, + MUSIQ.. + CLIPIQA, MANIQA,, + CLIP-Q7 DINO-QT
Moderate-SSableSR
0.0925
0.1086
0.0883
0.0673
0.9786
0.9550
StableSR
0.0933
0.1165
0.1056
0.0734
0.9791
0.9581
SeeSR
0.0904
0.1030
0.0812
0.0647
0.9788
0.9505
Customized Live Photo Dégradatión
0.0990
0.0893
0.0809
6,0556
0.98005
0.9629
FRIEHOS
minin
reselected potch
aligned patch
rescletted patch
aligned Ref patch
LINE ENOD
BES
original key photo
I InrMoments w PCR
UveMaaest wio PCR
original Key photo
Tvveomenns w PUR
LiveMaaments wia PCC
Figure 8: Comparison between LiveMoments with (w) and without (w/o) the proposed Patch Correspondence Retrieval (PCR) strategy when processing 4K images. Patches are cropped for clearer visualization. Please zoom in for better view.
SSIM, it consistently surpasses all baselines across perceptual and reference- quality metrics (LPIPS, DISTS, CLIP-Q, DINO-Q, etc.). These results indicate that LiveMoments is more effective at recovering fine-grained structures and perceptual fidelity, aligning better with human perception. The consistent performance on both synthetic and real data further confirms the robustness and effectiveness of our design.
E.2 MORE VISUAL EXAMPLES FOR PATCH CORRESPONDENCE RETRIEVAL (PCR)
To further validate the effectiveness of our Patch Correspondence Retrieval (PCR) strategy, we provide additional challenging cases in Fig. Even under large motion and complex textures, PCR corrects spatial offsets between degraded and reference patches, enabling accurate transfer of fine details such as text edges and leaf veins. These results confirm that PCR enhances both restoration fidelity and fine-grained detail recovery in high-resolution scenarios.
E.3 ANALYSIS ON DEGRADATION SETTINGS
We conducted analyses explore how different synthetic degradation settings affect real-world performance. Specifically, we trained LiveMoments under three representative degradation settings: (1) SeeSR degradation, (2) StableSR degradation, and (3) a lighter SaderrateSStaleSR variant constructed by reducing the blur and noise levels. The quantitative results on vvoLivee44 are reported Tab. g Among the three settings, our customized Live Photo degradation achieves the best results across most metrics, indicating that it better reflects real-world degradation characteristics. At the same time, LiveMoments maintains stable performance under all three degradation variants, demonstrating its robustness to different synthetic degradation.
We also provides the visualizations degraded images in Fig. 9 For each setting, the synthetic degradation is applied to the original key photo from our real-world Live Photo dataset and compared
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Table 9: Ablation study of robustness analysis under flow noise injection and flow estimator replace- ment on vivoLive 144. Best results are highlighted in bold.
Method
NIQE,, +
MUSIQ... +
CLIPIQA,,.
MANIQA,,
CLIP-QT
DINO-QI
LiveMoments + 10% noise
0.0999
0.0921
0.0808
0.0561
0,9802
0,9622
LiveMoments + 20% noise
0.1035
0.0935
0,0802
0.0551
0.9799
0,9613
LiveMoments + 40% noise
0,1082
0.0993
0.0811
0.0546
0.9792
0,9591
LiveMoments 80% noise
0,1108
0,1072
0.0845
0.0548
0.9785
0,9555
LiveMoments + 100% noise
0.1116
0.1099
0.0862
0.0566
0.9782
0.9540
LiveMoments + 200% noise
0.1159
0.1222
0.0915
0.0578
0.9771
0,9497
LiveMosents (SPyNet)
0,1066
0.00907
0,0809
0.0561
0.9804
0.9629
LiveMoments (LitcFlowNet)
0.1031
0.0946
0.0808
0.0544
0.9794
0.9595
LiveMomentss
0.0950
0.0893
0.0809
0.0556
0.9805
0.9629
original phote
Seathtt degradation
SanseR degradation
mederan-Nenassee degradisiio
our Liee Phate degradistin
afgood L. frame
original ker phate
Seatit degradation
dogr adatiee
mnsferate degradaatt
easr Live degradation
algged frume
Figure 9: Comparison between different degradation settings. The aligned LQ frame is cropped manually for better comparison. Please zoom in for better view.
with the corresponding reselected LQ frame, allowing us assess how well the synthetic degradations replicate real degradation patterns. The results show that existing settings tend to apply stronger degradations than those observed in real Live Photos, while our customized Live Photo degradations more faithfully reflect real-world characteristics.
E.4 ROBUSTNESS ANALYSIS OF INACCURATE. MOTTON ALIGNMENT
We analyze the behavior and robustness of LiveMoments when the estimated optical flow is inaccurate and motion alignment becomes unreliable. To quantify its sensitivity to flow errors, we conduct two analyses on vivoLive 144: (1) perturbing the RAFT flow with Gaussian noise, (2) replacing RAFT with alternative flow estimators. The results is provided in Tab. g
Noise Injection. We add Gaussian noise with different magnitudes (10%-200% of the original flow magnitude) the RAFT outputs. Although the perturbed flows become increasingly distorted, the reconstruction quality of LiveMoments degrades only slightly under moderate perturbations, indicating low sensitivity flow inaccuracies.
Flow Estimator Replacement. We substitute RAFT with SPyNet and LiteFlowNet, and the results remain close the original RAFT-based LiveMoments, suggesting that the model does rely specific flow estimator and generalizes across different motion estimation methods.
In addition, we provide visualizations to further analyze the impact of flow errors, including the reselected LQ frame, the original key photo with optical flow, and the warped original key photo, together with cropped regions that illustrate local refinement quality.
Robust cases. As shown in Fig. TO LiveMoments remains stable even when RAFT produces low- confidence or divergent flow vectors due to large motion or occlusion. Despite inaccurate motion guidance, the model does not introduce hallucinated textures and still produces visually coherent results, demonstrating its robustness to moderate flow errors.
Failure cases. In Fig. IT] provide failure-case case visualizations, including examples with large motion, severe occlusion, and low-texture regions that lead to inaccurate flow. These examples reveal the boundary conditions where motion alignment errors exceed the model's 's ability compensate.
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onginal photo optizal
Key photo
reidecked paks
LiveMoments
frame
original photo optical
warped original key photo
Masnunity Allgeed Ref porch
wirped Kef patch
reselected LQ frame
original key photo
Figure 10: Robust cases with inaccurate motion alignment. The aligned original key photo cropped manually for better comparison. Please zoom in for better view.
F MORE VISUAL RESULTS
In Figs. 12
13 14 and 16 present additional visual comparisons the real world Live Photo
dataset vivo Livelive14, including cases under high-resolution scenes. We compare visual results on vivoLive 144 from other RefISR, RefVSR and SISR baselines in Fig. 15 In Figs. 17 and [8]
present additional visual comparisons and high-resolution results on iPhoneLive90. The visual results of iPvoneLive90 from other RefISR, RefVSR and SISR baselines are provided in Fig. 19 In Figs. 20 and 21 we present additional visual comparisons and high-resolution results on SynLive260.
These examples demonstrate the robustness of LiveMoments in addressing the key photo reselection task across scenarios.
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resetectd patch
63
reselected Feaie
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original
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Liveootec
TarN Bottle
Kinstu
redicted L.Q fraimiv
origiall Rey phole wirhoppllellllee tlane
warped eriginid key phuto
warped Ru
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Warped or original keephheoo
manns all alechul Rei palich
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LhreMeencats
optical original MiDennRe parch
Failure with inaccurate motion alignment. The aligned original key photo is cropped manually for better comparison. Please zoom in for better view.
Figure 11:
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reselected LQ frame
cropped LO frame
TSDSR
DATSR
ReIVSR
original key photo
CoSeR
ReFIR
LiveMoments
aligned original key photo
DECITHLON
@ DECITHLON
@ DECATHLON
@ DECITHLON
Tewerrme egeea DECA
forsaaetale aggs transe Deck
freeam DECA
Towasorris Orenam DECA
Kana Fab
Soot Kones
Fabn
Keraa
intimmer Pagam
Emshingh Pagami
Untimapr Pagam
-irey Uneimmer Pagan
and
und daad
031
reselected LO frame
cropped LQ frume
TSDSR
DATSR
RefVSR
@ DECITKLOON
S DECXTRSON
@
DECITHLON
e DECATHLON
Towarennia
[laeo) Latt hn Scom Kma Fatz
cosp
Rog Widking Ba
Hadby Nobur Con
FANEE Mode South Kone Fabri
Sommantt agas 200/T4
Giceg Earvit antimae Pagnt
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Ganey mee Pagam
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made Dil-iizaa unon sa Auu
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8: 221 F
4009-103-100 B2 33L
4002-00:
10X
4309-109-1 10110 QUL
4039 23
SM
BENN: 23
originul photo
ReFIR
aligned original key photo
Figure 12: More visual comparisons of RefSR, ReIVSR and SISR methods on vivoLive 144 dataset.
The aligned original key photo is cropped manually for comparison. Please zoom in for better view.
LimeMosents
ABC
eropped I.Q frame
CoSeR
ReFIR (SUPIR)
LiveMoments
original key, photo
aligned kev photo
Figure 13: nigh-resolution visual comparisons of diffusion frafusioo-bbsedd RefISR methods on vivoLive144 dataset. The aligned original key photo is cropped manually for better comparison.
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WeMennenss
ehopped 1.Q frame
CoRRR
ReFIR (SUFIR)
Livenomenis
original key photo
Vlligndd ke Achoo
Figure 14: High-resolution visual comparisons of diffusion-based RefISR methods on vivoLive144
dataset. The aligned original key photo cropped manually for better comparison.
CCRYLIC STAN
CRYLIC STAN
CRRYII STAN
CRYLIC STAN
LINE FRIEMDS minini
UNNESDICNOS minini
LINE FRIENDS
LINE FRIENDS minini
minini
reselected LQ frame
cropped LQQ frame
OSEDIf
C2-Marching
ERVSR
LINE FRIENDS minini
STAN
FRIENDS MInIni
CRYLIC STAN
LINE FRIENDS minini
CCYLIC STAN
LINE LINEFRIENT FRIENDS DS minini
ACRYLIC STAR
CCRYLIC
original key photo
SceSR
ReFIR (SeeSR)
LiveMoments
aligned original key photo
reselected LQQ frame
cropped LQ frame
OSEDiff
C2-Matching
ERVSR
original key photo
SeeSR
ReFIR (SeeSR)
LiveMoments
aligned original key photo
Figure 15: More visual comparisons of other RefISR, RefVSR and SISR methods vivoLive144 dataset. The aligned original key photo is cropped manually for better comparison. Please zoom in for better view.
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Live Moments
cropped LQ frame
CoSeR
ReFIR (SUPIR)
Momentsens
Figure 16: Bigh-resolution visual comparisons of original Key photo
a aligned key photo
RefISR methods on vivoLive 144 dataset. The aligned original key photo is cropped manually for better comparison.
reselected LQ frame
cropped frame
TSDSR
DATSR
ReVV5R
original photo
aligned original key photo
reselected frame
cropped
TSDSR
DATSR
RefYSR
aligned original key photo Figure 17: More visual comparisons of RefISR, RefVSR and SISR methods on iPhoneLive90 dataset. The aligned original key photo is cropped manually for comparison. Please in for better view.
original key photo
CoSeR
ReFIR (SUPIR)
LiveMoments
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LiveMoments
STREET
eropped 1.Q frame
BEUTT
MEAR
WATS!
SHIRTS
Rare
ESPERTIS
SHIEET
PINTS
(REIIRR
SHORT
STREET
WEAR
LiveMoments
oral
AFINEIT DUALTY
STTEREES STREET WEAR
original key photo
aignee PHOTO
Figure 18: High-resolution visual comparisons of diffusion based RefISR methods on iPhoneLive90 dataset. The aligned original key photo is cropped manually for better comparison.
reselected LQ Irame
cropped LQ frame
OSEDiIT
C2-Matching
ERVSR
original key photo
SecSR
ReFIR (SeeSR)
LiveMoments
aligned original key photo
reselected LQ frame
cropped LQ frame
OSEDIf
C2-Matching
ERVSR
original key photo
SeeSR
ReFIR (SeeSR)
LiveMoments
aligned original key photo
Figure 19: More visual comparisons of other RefISR, RefVSR and SISR methods on iPhoneLive90 dataset. The aligned original key photo is cropped manually for better comparison. Please zoom in for better view.
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LiveMoments
cropped LQ frame
CoSeR
ReFIR (SUPIR)
LiveMoments
original key photo
ground truth
Figure 20: High-resolution visual comparisons diffusion-based RefISR methods on SynLive260 dataset.
reselected LO frame
cropped
TSDSR
DATSR
RefvSR
original photo
CoSeR
ReFIR (SUPIR)
LiveMoments
ground fruth
reselected cropped
TSDSR
RefySR
original key photo Figure 21: More visual comparisons of RefISR, RefVSR and SISR methods on SynLive260 dataset. Please zoom for a better view.
CoSeR
ReFIR (SUPIR)
LiveMoments
ground truth
25


Reviews:
[{"review_id": "LEq7GBrfwn", "reviewer": "Reviewer_NWQb", "summary": "This paper introduces the task of Reselected Key Photo Restoration for Live Photos, \nwhere a user-selected frame from the short video is restored using the original high-quality key photo as reference. \nThe paper formulates this as a reference-guided diffusion problem and proposes a dual-branch architecture \ncombining a RestorationNet for the degraded frame and a ReferenceNet for the original photo, fused via cross-attention. \nA unified Motion Alignment module enables alignment both in the latent space through motion-guided attention \nand in the image space via a Patch Correspondence Retrieval (PCR) strategy.\nExperiments demonstrate significant quantitative and visual gains over baselines.", "questions": "1. Flow failure handling.\nIn cases where RAFT yields low-confidence or divergent motion vectors (e.g., due to occlusion), \ndoes the model include any fallback or gating mechanism?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel problem definition.\nThe paper clearly motivates the reselected key photo restoration task, which occupies a unique middle ground between RefSR and RefVSR. \nIt targets a realistic and underexplored use case in mobile photography, differentiating itself from existing triple-camera or multi-view SR tasks.\n\n2. Strong methodological design.\nThe proposed dual-branch diffusion architecture (Fig. 2) is technically sound. \nThe ReferenceNet and RestorationNet fusion through cross-attention is a natural yet effective extension of SD3, \nwhile the Motion Alignment module appropriately handles temporal and spatial discrepancies \nthrough latent-space bias injection and patch-wise retrieval.\n\n3. Comprehensive evaluation.\nLiveMoments achieves superior quantitative results across all datasets and metrics (Tables 1-2) \nand includes clear ablation studies (Tables 3-4) isolating the effect of each module. \nThe analysis of dual-branch fusion and motion-guided attention is particularly systematic.", "weaknesses": "1. Limited exploration of motion estimation reliability.\nThe approach depends on RAFT-based optical flow estimated from a synthetically degraded reference. \nWhile this improves alignment, the paper lacks quantitative analysis of flow robustness under large motion or occlusion. \nFor instance, failure cases or confidence-weighted alternatives could clarify robustness limits.\n\n2. Computational efficiency and scalability.\nAppendix B reports 40 seconds for a 4K Live Photo inference, which, though acceptable for offline processing, may constrain deployment. \nReporting model size and comparing computational cost against strong baselines (e.g., CoSeR, SUPIR) would make efficiency claims more transparent.\n\n3. Ablation studies on training strategy.\nWhile Table 3 examines architectural components, \nthere is no analysis of the synthetic degradation's impact on real transfer. \nThe reliance on Real-ESRGAN for training may bias results toward its degradation prior. \nAn additional sensitivity or adaptation study could strengthen the generalization argument.\n\n4. Positioning relative to RefVSR extensions.\nAlthough the paper distinguishes LiveMoments from multi-camera RefVSR datasets, \na more explicit discussion on whether the method generalizes to multi-frame or multi-reference inputs would help clarify scalability \nto broader camera setups.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1ujaAv5W14", "reviewer": "Reviewer_kHRT", "summary": "I think the paper introduces a practical task: restoring a reselected low-quality Live Photo frame using the original high-quality (HQ) key photo as a reference. The method, LiveMoments, uses a dual-branch diffusion transformer (ReferenceNet + RestorationNet) with cross-attention fusion and a unified motion-alignment module: (i) latent-level motion embeddings from RAFT flow injected as attention bias; (ii) image-level Patch Correspondence Retrieval (PCR) for tile-wise inference at 4K. Datasets include SynLive260 (synthetic) and real vivoLive144 / iPhoneLive90, plus a relative no-reference metric that normalizes to the HQ reference. Results show consistent perceptual gains on real data.", "questions": "- How sensitive is performance to RAFT errors under heavy compression/blur? Any comparisons to alternative matching/flow or ablations with noise injected into flow?  \n\n- Do the relative NR metrics correlate with human MOS across different devices/ISPs (Spearman/Pearson)? Any user study?  \n\n- What are branch-wise params/VRAM? Could KV caching or half-precision in the reference branch speed up 4K inference?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) I think the task setting is new and useful: both the degraded target and the reference come from the same Live Photo (temporal offset), ensuring content coherence while reflecting real ISP quality gaps.   \n\n(2) Method-wise, a dual-branch SD3-based design with reference KV concatenation for cross-attention, plus flow-guided attention bias and PCR for high-res tiling, is a targeted solution to alignment at 4K.  \n\n(3) The relative no-reference evaluation tied to an HQ reference matches the task goal better than generic NR metrics. (Details are described in the experiments.)", "weaknesses": "(1) Reference dependence and mismatch boundary. I think the approach relies on scene-level consistency between the HQ key photo and the reselected frame; large non-rigid motion or occlusions may cause the model to transfer incorrect textures. A failure-case analysis (large pose/occlusion) or a gating mechanism would help.    \n\n(2) Flow robustness under strong degradations. The latent alignment hinges on RAFT performance on degraded inputs; the paper simulates matched degradation for flow, but I’d like sensitivity tests to flow errors/alternatives.  \n\n(3) More metrics are needed in this paper. The relative NR metric is task-aligned but may be reference-pipeline specific; broader validation (more phone ISPs, user studies with correlation stats) would strengthen it.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "q7t5PLY0Y2", "reviewer": "Reviewer_cYvV", "summary": "The paper introduces LiveMoments for reselected key photo restoration in Live Photos. It adopts a dual branch diffusion architecture with a ReferenceNet and a RestorationNet, and adds a unified Motion Alignment module that injects flow guided priors at latent and image levels. The authors build three benchmarks and propose relative no reference metrics tailored to the task. Experiments on synthetic and real Live Photo datasets demonstrate consistent gains over RefISR, RefVSR, and diffusion based SISR baselines.", "questions": "1. How sensitive is the method to errors in optical flow  can the authors include a noise injection or alternative flow estimator study and show qualitative failure cases where misalignment occurs  \n2. Can the authors provide a runtime and memory comparison to representative RefISR and diffusion restoration baselines under the same hardware and resolution", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- Clearly defined and practical new task specific to Live Photos with well motivated formulation  \n- Elegant dual branch diffusion design with cross attention that effectively transfers fine details from the original key photo  \n- Unified motion alignment at latent and image levels addresses large temporal offsets in real captures  \n- Benchmarks and task specific metrics are thoughtfully constructed and results are strong and consistent", "weaknesses": "- Efficiency comparison is limited  runtime and memory are reported for the proposed method only  a fair cross method efficiency table would strengthen the claim", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PmPY4GqdRf", "reviewer": "Reviewer_HHsy", "summary": "The paper presents LiveMoments, a method for selecting and restoring a new low-quality (LQ) key photo from a short clip surrounding some key high-quality (HQ) photo. To this end, the authors build a model based on latent flow models and learnable networks for the HQ key image, the LQ candidate, and the motion between the two frames modeled as optical flow. The authors also propose to perform image space motion alignment based on image patches. The authors train the model using open source high-quality data and introduce three benchmarks for evaluation, a synthetic one and two real-world Live Photo datasets.", "questions": ".", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "* The paper introduces a novel task, Reselected Key Photo Restoration in Live Photos, and builds a model specifically designed for solving this task.\n* The paper introduces novel benchmarks for this task.\n* LiveMoments substantially outperforms competing methods from several related domains on the evaluated metrics.\n* The paper is written clearly, although some parts are not clear, as will be detailed next.", "weaknesses": "* Although I appreciate the effort invested in this paper, I am not sure that, in terms of the scope and potential impact, it fits ICLR. The paper addresses a task that, in my opinion, is very nuanced, and it is challenging to see how to generalize the method to other tasks, setups, and types of image degradations. Could the authors please clarify the possible future directions for extending the proposed method and its other domains of applicability?\n* To me, section 3.4.2 is not clear enough. For instance,\n  - Is the space motion alignment done for each pixel? If so, then how is consistency preserved across pixels? \n  - Is it applied to the output of the model or to the input?\n  - How is the patch size selected? Perhaps an ablation about that can clarify it.\n* Code is missing from the submission to self-evaluate the method quality and better understand the implementation.\n* Minor:\n  - Some information is missing about the vivoLive144 and iPhoneLive90 datasets. For instance, what types of scenes were used to construct these datasets?\n  - Missing explanation for what is $f(x)$ in line 194 and $Q$ in line 232.\n  - Why wasn't FID evaluated on the synthetic dataset?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)