Improved Adversarial Diffusion Compression for Real-World Video Super-Resolution
Authors: Bin Chen
Weiqi Li
Shijie Zhao
Xuanyu Zhang
Junlin Li
Li zhang
Jian Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Real-World Video Super-Resolution
One-Step Diffusion
Improved Adversarial Diffusion Compression
Diffusion Distillation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: While many diffusion models have achieved impressive results in real-world video super-resolution (Real-VSR) by generating rich and realistic details, their reliance on multi-step sampling leads to slow inference. One-step networks like SeedVR2, DOVE, and DLoRAL alleviate this through condensing generation into one single step, yet they remain heavy, with billions of parameters and multi-second latency. Recent adversarial diffusion compression (ADC) offers a promising path via pruning and distilling these models into a compact AdcSR network, but directly applying it to Real-VSR fails to balance spatial details and temporal consistency due to its lack of temporal awareness and the limitations of standard adversarial learning. To address these challenges, we propose an improved **ADC** method for Real-**VSR**. Our approach distills a large diffusion Transformer (DiT) teacher DOVE equipped with 3D spatio-temporal attentions, into a pruned 2D Stable Diffusion (SD)-based AdcSR backbone, augmented with lightweight 1D temporal convolutions, achieving significantly higher efficiency. In addition, we introduce a dual-head adversarial distillation scheme, in which discriminators in both pixel and feature domains explicitly disentangle the discrimination of details and consistency into two heads, enabling both objectives to be effectively optimized without sacrificing one for the other. Experiments demonstrate that the resulting compressed **AdcVSR** model reduces complexity by **95%** in parameters and achieves an **8$\times$** acceleration over its DiT teacher DOVE, while maintaining competitive video quality and efficiency.

IMPROVED ADVERSARIAL DIFFUSION COMPRESSION FOR REAL-WORLD VIDEO SUPER-RESOLUTIC
Bin Chen l2, Weiqi Li! Shijie Zhao Xuanyu Zhang Junlin Li², Li Zhang", J Zhang
School of Electronic and Computer Engineering. Peking University BByteDance Inc. $Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology,
Shenzhen Graduate School, Peking University
ABSTRACT
While many diffusion models have achieved impressive results in real-worle video super-resolution RRea-VVSR, by generating rich and realistic details, their reliance multi-step sampling leads to slow inference. One-step networks like SeedVR2, DOVE, and DLoRAL alleviate this through condensing generation into one single step, yet they remain heavy, with billions of parameters and multi-second latency. Recent adversarial diffusion compression (ADC) offers a promising path via prun- ing and distilling these models into a compact AdcSR network, but directly apply- ing it to Real-VSR fails to balance spatial details and temporal consistency due to its lack of temporal awareness and the limitations of standard adversarial learning. To address these challenges, we propose an improved ADC method for Real-VSR. Our approach distills a large diffusion Transformer (DiT) teacher DOVE equipped with 3D spatio-temporal attentions, into pruned 2D Stable Diffusion (SD)- AdeSR backbone, augmented with lightweight ID temporal convolutions, achiev- ing significantly higher efficiency. In addition, we introduce dual- adversar- ial distillation scheme, in which discriminators in both pixel and feature domains explicitly disentangle the discrimination of details and consistency into heads, enabling both objectives to be effectively optimized without sacrificing one for the other. Experiments demonstrate that the resulting compressed AdR model re- duces complexity by 95 % in parameters and achieves an 8x acceleration over its DiT teacher DOVE, while maintaining competitive video quality and efficiency.
INTRODUCTION
(a) Previous
(b) Proposed
Real-world video reserrresolution (Real-VSR) (Tao al. 2017 Nah al. 2019) is fundamental and long-standing problem in computer vision. It targets at recovering high-resolution (HR) videos XHR from their low-resolution (LR) counterparts XLR degraded by unknown factors in real-world cases. Traditional
Teacher (L.8)
Teachar (11B)
Method
Improved Method
Stadient (0 581
Student [0.88]
Detals
Comist MP
R/F
Dateil
non-generative (Yi et al. 2019. Chan et al. 2022
1'Baa" Tabe")
Covokn A/F
| Chan et al.] 2022b a Wang al. 2019 Liang al.) 2024) and generative adversarial
Yang et al. [201]]
Blocks
20 Blocks
1D Blocks
Disc.
Figure 1: Comparison of methods in com- pressing diffusion networks for Real- VSR. (a) Traditional ADC (Chen et al. (2025a) dis- tills an SD network with 2D spatial attentions into pruned student using a single adversar- ial signal without temporal modeling, suffer- ing from frame flickering. (b) Our improved ADC distills larger DiT-based teacher with heavier 3D spatio-temporal attention into the same 2D student, augmented by ID temporal convolutions, using dual-head discriminators D in pixel and feature domains. Through dis- entangling the discriminations of detail rich- ness and temporal consistency into different heads, it balances the optimization of both.
network (GAN)-based (Chu al. 20200 Lucas et al.) 2019 Xu et al. 2025) approaches have achieved no- table progress, yet most of them struggle under com- plex, mixed degradations, producing over-smoothed results or artifacts. To enhance the detail richness of super-resolution outputs XSR. many Real-V Real-VSR VSR stud-
ies (Zhou et al. 20244) Yang et al. 2024a; He et al.
2024 Wang et al.] 2025b] Xie al. [2255b Kong et al. 2025 Wang et al.] 2025c I. Zhao et al. 2025
Wang et al. 2025e et al. (022)) have already de-
veloped diffusion-based approaches which can gen- erate video frames with richer and more realistic de- tails. However, these methods are hindered by long inference time, as they need multiple sampling steps.
Eigual contribation. Prnject lead. Coneepouding auhhrr zhangjann plu..duu.cc es. This werk was financially suppocted is post National Natural Science Foundation China (62372016). Guanglong Proincial Laburatory of Hlgg Definition Immersive Media Technology (2024B 221200000)) Shenzhen Science and Technology Program (SYSPG204122 (1173440444 and Ourstanding Talencs Training Find in Shenthen.

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Recently, researchers have shifted focus to one-step diffusion Xie et al. 2024 Lin et al. 2025c) for achieving efficient and high-quality Real- VSR. Building on
Waan et al.
2024a Wu et al. 2024
2022), originally developed for image the pretramed Stable Diftusion (SD) models (Rombach et al.
generation, USt (Liu al. 2025) enhances the temporal consistency in XsR by propagating and fusing features along the temporal dimension, while DL (Sun et et. 2025) aligns the structure of the previous frame with the current one using the estimated optical flow between them. Building on video diffusion models. SeedVR2 (Wang et al. 2025a) progressively distills a pretrained 64-step SeedVR (Wang et al. 2025b) into one sampling step and further enhances it through ad adversarial post- training. DOVE (Chen al. 2025b) adapts pretrained Cog CogVideoX networks (Yang et al.) (022b)) to Real-VSR by fine-tuning them on a curated high-quality video dataset. Despite these advances, such approaches still suffer from high complexity due to large-scale parameters and heavy computation.
Meanwhile, two recent methods, AdcSR (Chen et al. 2025a) and TinySR (Dong et al. 2025b). have explored compressing the diffusion networks OSEDiff (Wu al. (024) and TSD-SR (Dong et al.] 2025a) via structural pruning and adversarial distillation to reduce complexity for real-world image super-resolution (Real-ISR), as shown in Fig. (a). However, it is non-trivial to extend them to Real- VSR. One may use them to compress Real-VSS networks such Seed DOVE, and DLoRAL, but two challenges arise: the compressed models are still too large, or video quality is compromised due to the conflicts between optimizing detail richness and temporal consistency (Sun al.) 2025). which are difficult to balance with existing distillation methods. Therefore, it remains underexplored and of great value design more effective compression approaches for diffusion-
To improve effectiveness, in this paper, we propose AdR, a novel Real-VSR network that com- presses the one-step model DOVE using an improved method of Adversarial Diffusion Compression (ADC) (Chen et | (025a). Unlike previous networks that rely on computationally costly 3D spatio- temporal attentions or additional frame alignment modules, we hypothesize that a 2D diffusion back- bone (e.g., 1D2.1))s is sufficient to generate rich details, while temporal consistency can be maintained with few lightweight ID temporal convolutional layers, and that their combination is also effective in removing degradations. Guided by this insight, as Fig. (bb illustrates, ASR adopts the same pruned SD2.1 backbone as AdeSR, augmented with ID temporal convolution layers, achieving sig- nificantly lower complexity than its heavy 3D teacher DOVE. To improve video quality and address the conflict between optimizing details and consistency (Sun et al. 2025). we introduce a new adver- sarial distillation scheme that leverages the strong teacher DOVE together with numerous temporally consistent real videos and detail-rich real images. Specifically, two discriminators are employed for adversarial learning: one discriminating in feature space of variational autoencoder (VAE) decoder and the other in pixel space, each with a "detail" head and a "consistency" head sharing a common backbone, to separately assess the realism of spatial details and temporal consistency. This enables the student to generate super-resolution results which are both detail-rich and temporally consistent. By integrating our "2D + 1D" architectural design with the dual-head. disemiiicrrminttorr adversar- ial distillation scheme, ASR effectively compresses DOVE, yielding substantial efficiency gains while maintaining competitive video quality. Our contributions are summarized as follows:
O (1) We propose a novel improved ADC approach that combines an effective network design with adversarial distillation to compress a heavy Real-VSS model into an efficient diffusion- -GNN hybrid.
D (2) We demonstrate that 2D image diffusion backbone augmented with lightweight 1D temporal convolutions can effectively learn Real-VSR mapping from 3D diffusion Transformer (DiT) teacher.
(3) We introduce new adversarial distillation scheme that decouples the discriminations of details and consistency into two heads sharing a common network backbone, applied in both VAE decoder's feature space and the pixel space. This design enables balanced optimization, avoiding collapse into either over-smoothed outputs (loss of spatial details) or flickering (loss of temporal consistency).
O (4) Extensive experiments show that our VSR model reduces parameters by 95% and achieves an 8x accelcration over its teacher DOVE, while maintaining competitive performance on Real-VSR and striking balance among fidelity, detail richness, temporal consistency, and model efficiency.
2 RELATED WORK
Real-VSR. A main challenge in this field lies in modeling the diverse, complex degradations of LR inputs, which can usually not be well represented by the bicubic downsampling (Mou et al.) 2024
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Hu al. 2023 Wang et al. 2022 Jiang al. 2025 Li et al. 2025b). To address this, two strategies for collecting LR-HR video pairs to train deep Real- VSR networks have been developed: one cap- tures pairs using different camera settings (Yang et al., 2021: Wang et al. 2023b), while the other synthesizes I.R inputs from HR videos by composing degradations including noise, blur, resizing, and image/video coding compression (e.g., JPEG, H.264, and MPEG-4) in random, high-order pro- cesses (Wang et al., 2021]] Zhhng et al. [021] Chan et al., (022b). These approaches enrich the degradation space and can synthesize large amounts of training data. Building upon these, a number of expressive VSR networks have been developed (Shi et al. (002)) Non-generative methods like BSsicVSR (Chan et al. 2021 2022a). EDVR (Wang al. 2019). and RVRT (Liang et al., [024]
2022) excel at distortion removal, but often yield over-smoothed results under severe degradations.
GAN-based methods including TecoGAN (Chu et al. 2020) and VideoGigaGAN (Xu et al. 2025)
enhance visual quality with sharper details, but could introduce visible artifacts. Recently diffusion-
based methods have shown stronger performance by generating more realistic videos. For instance,
Upscalc-A-Vides (Zhou et al. 2024). VEnhancer (He et al. 2024), STAR (Xie et al. 2022b)) Real- VSR (Zhao et al. 2025). Se VSR (Wang et al.] 2025e). and Vivid- VR (Bai et al. (025) integrate transform/control modules into diffusion backbones to exploit the LR inputs
condition. MGLD-
VSR (Yang et al. (2024)) enforces temporal consistency via motion guidance. SeedVR (Wang et al.,
2025b) introduces shifted window-based DiTs to handle varying spatial sizes, LiftVSR (Wang al.
2025cp adopts attentions and memory for modeling short- and long-term dependencies. SimpleGVR (Xie et al. 2025a) performs cascaded latent-domain upscaling, and DiffVSR (Li et al.
2025c) de-
composes kcal-VSR learning in three progressive stages. Although these approaches improve detail richness, their reliance on multi-step sampling makes inference slow and computationally expensive.
One-Step Diffusion. Reducing step number while keeping output quality is widely adopted strat- egy to accelerate inference (Wang al.] (025f). In the context of Real-ISR and Real-VSR, several works have pushed this idea to the extreme by compressing multi-step generation into a single step
(Yue et al. 20255] Gong et al.] 2025 Wang et al. 2025d). For example, SinSR (Wang et al. 2024a) and Seed VR2 (Wang al. (2255a) distill a step ResShift (uue et al. 2024) and 64-step Seed VR
(Wang et al., (2025)) into one step through bidirectional or progressive distillation (Salimans & Ho 2022). OSFDiff (Wu al. 2024), S3Diff (Zhang et al. (2024)). D3SR (Li et al. 2024), and HYPIR (Lin et al. 2025c) adopt variational score distillation (Wang et al. 2024b). degradation-guide guided low- rank adaptations (LoRAs) (Hu et al. 2022). and adversarial tost-training (Lin et al., 2025a b), en-
abling one-step Real-ISR with high image quality. USR (Liu et al. 2025) aggregates temporal
features via recurrent shifts, while P-SA-SR (Sun et al. 2024 and DLoRAL (Sun al. 2025) intro-
duce residual one-step diffusion and dual-LoRA learning, to alternately optimize spatial details and temporal consistency. Leveraging large-scale pretrained DiT-based text-to-imee to-iiage///videe (T2I/T2V) generation models, FluxSR (Li et al. 2025a), DiT4SR (Duan et al., 2025). and DOVE (Chen et al.) 2025b) fine-tune FL.UX (Labs] 20244. SD3 (Esser et al. 2024), and Cog VideoX (Yang et al.) (024bb) under the flow matching framework Lipman et al. 2022) enhance fine structures and text regions.
To further reduce complexity, AdeSR (Chen et al. 2025a), PassionSR (Zhu et al. 2025). and TinySR
DDng et al. 2025b) compress one-step Real-ISR networks using pruning, weight quantization, and distillation. However, these techniques are tailored for Real-ISR and struggle in Real-VSR, as they do not account for consistency. As result, applying them directly would compromise video quality. To mitigate this problem, we propose to learn a diffusion network AdeVSR for Real- VSR, based on our improved ADC method that compresses the large DOVE teacher by distilling into pruned 21) SD2. backbone, augmented with lightweight 1D temporal convolutions. Additionally, we develop a new dual-head, mual-discriminator adversarial distillation scheme with decoupled cetail-consistency discrimination, enabling the student network to achieve competitive video quality and efficiency.
3 METHOD
3.1 PRELIMINARY
Conflict in Optimizing Details and Consistency. Detail enrichment in video outputs requires syn- thesizing fine- ree-grained structures like textures and edges with significant pixel-level variations, while temporal consistency demands constraining these variations across frames ensure visually pleas- ant transitions and suppress flickering. These objectives are empirically found to be in conflict (Chu Li et al. 2025c Xu al. 2025): many generative models emphasizing perceptual qual-
et al. 2020
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(a) Overview
(b) Dual-Head Discriminator Training
SD Decoder
Large DIT Teacher (DOVE)
Adv.
SD VAE Encoder
Distil.
Fiddo
Frase
Ca
Oiscrtennntoor
Training Objjettive
Augmented
Backbone (Conshax/Augg
Inpue
1
Ø
+
Loss
Detail
mented 5' LiNeet
Conoia
Trainable Part
Trainable Blocks
"Real" Prediction
Inserted Trainable
AdcVSR
15
Frezen Part
Frozen Blocks
"Fake" Prediction
Temporal Comn. Blocks
Figure 2: Illustration of the proposed improved ADC method, with application to compressing DOVE (teacher) into Adc model (student). (a) We augment the 2D AdeSR Real-ISR network (Chen al. 2025a), consisting of pruned SI22.1 UNet and VAE decoder, through inserting 1D tem- residual blocks (RBs) after each 2D spatial RB and Transformer block (TB), enabling temporal modeling, while maintaining efficiency for Real-VSR. The resulting VdR network is then fully trained end-to-end via adversarial distillation from DOVE. (b) For adversarial learning, we design dual-head discriminators with pretrained backbones for feature extractions. Each discriminator uses 2D and ID convolutions, followed by two linear projection heads at the tail, to separately evaluate detail richness and temporal consistency. Training is guided by five curated types of video and image data (1-5) with head-specific labels, achieving balanced optimization of details and consistency.
ity tend to prioritize details, leading to visible flickers, while propagation or alignment mechanisms for consistency may over-smooth attenuate details. Recent work (Sun et al. 2025) also highlights that details and consistency are competing objectives, where optimizing one could degrade the other.
AdeSR, and Current Methods Limitations. AdeSR (Chen et al. 2025a) demonstrates that one- step Real-ISR diffusion network (Wu et al. 2024) can be compressed by an ADC method: removing VAE encoder and text components, pruning channels of denoising UNet and VAE decoder, and then applying adversarial distillation to restore outputs' quality, as shown in Fig. (a). However, AdeSR is designed for Real-ISR and does account for temporal modeling. When applied frame by frame to vidcos, it inevitably introduces flickers (Zhou et al. (2024; Rota et al. 2024). One-step Real-VSS VSR networks (Liu al. 2025) Wang et al. [025al] Sun et al.) 2025| Chen al., (2025b) are still heavy, with 21.3 1.3B parameters and 24s latency even for a 25- 512 x 512 video (Fig. 4). An intuitive idea to compress them by combining ADC, but existing learning approaches like dual-LoRA (Sun) et al. 2024 2025), adversarial/score-ban
distillation (Xu et al. 2025 Liu et al., 20255 CCen et al., 2025a) are ineffective under aggressive pruning, failing to resolve the detail-consisenncy conflict.
3.2 NETWORK ARCHITECTURE DESIGN
To compress large Real-VSR diffusion networks while balancing quality and efficiency, we propose an improved ADC approach which combines an effective architecture with an adversarial distillation scheme. Our key intuition is that although 3D spatio-temporal DiT's (Wang et al. 2025a Chen al.) 20256) achieve impressive results, their attention mechanisms aim to capture long-rand range space-time dependencies, which are important for T2V generation (Yang et al. 22224b] Wan et al. 2025). where such global information must be inferred from scratch. In Real-VSR, however, the LR video already provides much of this information (Sun et al. 2025), e.g., structural layout and temporal continuity. Its main objectives are: (1) synthesizing details and (2) ensuring that they are temporally consistent to prevent flickering. In this setting, heavy 3D attentions might introduce redundancy, as much of its capacity is devoted to generating global spatio-temporal structures that are already present in XLR-
Building on this insight, we hypothesize that (1) a 2D diffusion backbone capable of synthesizing details, and (2) consistency can be maintained with several 1D temporal convolutions. The rationale behind the second point is that maintaining consistency is inherently less challenging than synthesiz- ing details: the objective is to constrain pixel-level variations across consecutive frames, rather than generate new fine structures. By enabling adjacent frames to be processed with temporal awareness, these convolutions are hypothesized to be sufficient to suppress flickering and yield temporally con- sistent recoveries. This motivates a principled "2D ID" network design that is expressive enough to learn the powerful Real-V VSR mappings of large 3D DIT's while reducing redundant overhead.
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Specifically, as Fig. 2 (a, bottom) exhibits, we adopt AdeSR (Chen et al. 2025a) as the 2D backbone, composed of channel -rannel-prumed SD2.1 UNet and VAE decoder. To add temporal awareness, we insert residual blocks after each UNet block, each consisting of 1D temporal convolution, ReLU activa- tion, and a second convolution with a skip connection. Unlike flow- and motion- -uidedd approaches (Zhou et al.,, [2224] Yang et al., (2024a)
prignment/rropagation strategies (Liu et. [025] Xu et al.) [ et al. (2025), this simple yet effective design equips the resulting network, AdcVSR, with temporal modeling capacity, while keeping network architecture compact and inference efficient.
3.3 ADVERSARIAL DISTILLATION SCHEME
To achieve competitive video quality, we distill the large pretrained 3D DiT model DOVE (teacher) into our "2D> + 1D" network (student). Specifically, as illustrated in Fig. 2 (a, top). we adopt DOVE's outputs as the learning target and conduct distillation in two domains: the pixel domain and the fea- ture domain of the AdcSR VAE decoder's middle block. In the latter, DOVE's output pixels Xeencher are re-encoded by S VAE encoder and fed into the middle block to obtain aligned features feachher for supervision, using the regression losses |[xstdeent Xyeacter || and |[faudent - fuaaderr 11. where Xstudent and faudent denote the student's 's corresponding outputs. Compared with the original ADC framework
(a,
(Chen et al. 2025a), which distills only in a single feature domain corresponding to fauueent and freacher with the remaining decoder blocks frozen, our method uses richer supervisory signals and fine-tunes the entire network end-to-end, thereby activating its full capacity to learn Real-VSR mappings better.
However, although minimizing pixel and feature errors provides strong supervision, is insufficient because our "2D + ID" AdR is significantly smaller and architecturally different from 3D DiT teacher, making exact fitting impractical while causing optimization difficulties and degraded recon- structions. Unlike the original setup of ADC, where the student was a streamlined variant of teacher with closer capacity and simple error minimization could still be effective, our case far more chal- lenging due to much larger gaps in both architecture design and parameter scale. We therefore retain error- miniiiiiiiiig distillations as foundation, but augment them using adversarial learning to relax the requirement of exact replication, enabling the student to benefit from guidance of teacher DOVE while enjoying the flexibility to generate outputs that are feasible for its capacity and of high quality.
To achieve this, a straightforward approach would introduce standard discriminator that adversar- ially aligns output distributions with real data (e.g., XHR) (Sauer et al.) 2023] 2024; Lin et al.) 2024). However, this couples the objectives of details and consistency into one single adversarial signal. In practice, the discriminator 'D often tends to prioritize one aspect (typically details) at the expense of the other (typically consistency), leading to detail-rich but flickering result XSR- This reveals funda- mental issue: a traditional single-head discriminator entangles these conflicting objectives, yielding gradient that can not balance both. To overcome this, we propose dual-head and dual-discriminator scheme that disentangles the assessments of details and consistency. Concretely, as Fig. 2 (b) shows, one discriminator operates in the pixel domain on Xsudent, while the other in decoder feature domain on Suudent, forming a more comprehensive dual dual-domain supervision than single-don domain approaches. Each discriminator is built upon a separate frozen pretrained backbone (ConvNeXt (Liu et al. 2022
Lin et al. 2025c) for Xandem: and the same augmented SD UNet as our designed AdR for faudeen)) to provide strong representations and stabilize training, followed by three additional alternating 2D and ID convolutional layers to jointly capture spatial and temporal features. Finally, each discrimi- nator branches into two linear heads ("detail" and "consistency") that project the last-layer features into two adversarial signals for detail realism and consistency, respectively. Formally, the adversar- ial distillation loss for the student generator is defined as follows, where we also include a perceptual DISTS (Ding et al. 2020) loss term as in DOVE further strengthen the pixel-domain supervision:
L = > Cpiset + Ateatur Sieature:
Cpixel
Xstudentt Xeencher 1 + DISTS (xsudent: Xwacher) ) + Softplus D pixell (xtudee)) )),
Lieature || | fueucher|1 | + Aun,, Softpluss Dienture (fsustent)
where D Dpixel and Dicature denote pixel- and feature-domain discriminators, Softplus( D Ppixel (Xandent)
and Softplus(- Seftplus(-Diuuuuee Diesature (faudem)) )) implement aatusrrraiinng adversarial losses (Yin et al. 2024 while Apixel. Ateature and Xudv are weights controlling the relative contributions of corresponding loss terms.
To train the dual-head discriminators, we curate five carefully designed data types with head-specific labels that vary detail and consistency independently. First, the student's 's outputs (Xautem and fonden)) are always labeled as "fake" for both heads, ensuring that adversarial feedback persistently pushes
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the generator toward improvement. Second, real videos (Xvideo and fvidzo) are adopted and labeled as "real" for consistency, as they preserve coherent temporal dynamics of the same underlying scene. Third, temporally shuffled versions of these videos (Xi and fadss)) obtained via randomly penmut- ing frame order along the temporal dimension, destroy frame-to-frame continuity, and are therefore labeled as "fake" for consistency. Fourth, we exploit detail-rich real images by repeating each one to construct static pseudo-videos (Ximage and fimage). which enjoy both high-quality details and perfect temporal stability, and are thereby labeled as "real" for both heads. Finally, we randomly sample and crop real images without temporal correspondences (Ximg and a. These sequences are detail- rich but inherently inconsistent across frames, so they are labeled as "real" for details but "fake" for consistency, To be formal, the losses for dual-head discriminators ppicel and Pieature are defined as:
Eaise
Σ
Soltplus(-v((D()))) + Softplus(-v((((s)))
(4)
(S.NUNN))
8= {Xxsuddent -1,
-1); (f.tuueffa -, 1)), (Xvideu, 0, 0)1 (Fridee: 0, 1), (X 0, -1);
(5)
(fritcon 0, 1), (Ximage: 1, 1), (fimage. 1, 1), (Xage 1, - 1), (fingee 1, -1)
Here, [D(s)]a and [D(s)]= denote the outputs of the "detail" and "consistency" heads of discriminator D for imput s, while Yd, Ve E {, 1} encode "fake", "unlabeled", and "real" labels, respectively. The set S enumerates the five curated data types and corresponding labels in both pixel and feature domains. It is worth noting that leave real video details unlabeled, and rely on real images the positive supervision for "detail" head, encouraging the generator to produce more detail-rich frames.
In contrast to standard GAN discriminators (Goodfellow et al. 2014) which provide single binary signal for real versus generated samples, our design restructures adversarial supervision into a multi- attribute form, producing fine-grained and disentangled signals for both details and consistency, with outputs that preserve the same spatial resolution as the inputs. This approach moves beyond existing adversarial distillation methods (Sauer et al., [023 2024, Chen al. 2025a) by explicitly requiring the dual-head discriminators to evaluate two aspects of video realism. As result, neither aspect can be disregarded or down-wo wwwn-weighted, as the two dedicated heads consistently provide supervisions, en- suring that the model receives separate weight gradients for both. This prevents AdR generator from collapsing toward one objective at the expense of the other, instead guiding it to optimize both jointly, and produce reconstructions that are simultaneously detail-rich and temporally consistent.
4 EXPERIMENT
4.1 EXPERIMENTAL SETTING
Implementation Details. We employ AdcSR (Chen al. 2025a) pretrained by compressing PiSA- SR (Sun et al. 2024) as 2D backbone, in which the SD2.1 denoising UNet and VAE decoder (Luo let al.02 are channel-pruned by 25% and 50%, respectively, and augmented with pero-initialized (Zhang et al. 2023) 1D temporal convolutions to form our AdR network. Each 1D convolution has kernel size of 3 and the same channel number as its preceding UNet block. For discriminators, the channel numbers of first convolutions are adjusted to match the dimensions of input images and features, while both channel numbers of last-layer features are set to 256. The "detail" and "consis- tency" heads are implemented by 1 x convolutions with 192 and 64 output channels, respectively.
Similar to Wang et al. (202T), VSR model is trained in two consecutive stages. In the first stage, we perform only error- mirimmiin distillation from the pretrained DOVE teacher without adversarial learning for 200K iterations. In the second stage. VSR (generator) is initialized with the weights from the first stage and fine-tuned for another 200K iterations. During this stage, the pixel-domain discriminator uses the pretrained ConvNeXt backbone from the OpenCI.IP library (kept frozen) (Liu et al. 2022; Lin et al. 2025c). while the feature-domaie discriminator exploits the same pretrained augmented SD UNet from the first stage (also frozen). The initial learning rates for AdcVSR are set to x xx00= in the first stage and 1 x 10 in the second stage, each halved after 100K iterations, while the trainable parts of discriminators (first and tail convolutions, as well as heads) adopt learning rate 1 x 10-7. Loss weights are set to Xpixel 0.1, Aeauure 1.0, and X 1.0, respectively.
In both stages, we fully fine-tune the entire AdR network end-to-end, following Liu al. (2025); Sun al. (2025): Chen et al. (2025b), using randomly sampled and cropped temporally consistent
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Table 1: Quantitative comparison of Real-VSS performance. Inference time is measured on an NVIDIA H20 GPU for generating a 25-frame video at spatial resolution x The best, second- best, and third-best results are labeled in bold red, underlined blue, and italic green, respectively.
Mcthod RealBasicVSR Upscale---Video MGLD-VSR STAR [SedVR2 DOVE DLORAL PISA-SR AdeSR HYPIR AAdeSS (Ours)
Test Dataset:
UDMI0 (Synthetic)
PSNR
24.39
23,03
24.28
22.61
15.92
26.00
22.49
23.21
23.39
22.35
23.36
SSIM
0.7376
0.6189
0.7491
0.6544
0.7674
0.7808
0.7130
0.6799
0.6995
0.7697
LPIPSL
0.3283
0,4218
0,3103
0.5055
0.2653
0.2645
0.3201
0.3658
0,3781
0.3736
0.3065
DISTS
0.2078
0,2360
0.1909
0.2665
0.1532
0.1752
0.2066
0.2125
MANIQA
0.5725
0.5331
0.5558
0.3468
0.5232
0.5133
0,5679
0.6257
0.5696
0.5856
0.5793
CLIPIQAT
0.4422
0.4661
0,4640
0.2346
0.3471
0.5420
0.4667
0.7055
0.6693
0.6006
0.6818
MUSIQ
$7.10
$2.06
58.12
33.93
50.09
60.68
55.54
66.42
61.30
59.85
63.88
Ex
3,36
3:68
3.31
2.37
2.56
2.22
3,51
6.96
6.19
10:68
1.67
DOVERT
0.2610
0.4002
0.3311
0.2717
0.3296
0.4731
0.3637
0,5010
0,4364
0.4857
0,4878
Test Dataset:
VideoL
(Reeal
World
MANIQA
0.5609
0,5366
0.5530
0.4355
0,4388
0.4355
0.5976
0.6.119
0.6017
0.6424
0.6127
CLIPIQAT
0.3444
0,3594
0.3446
0.2497
0.2318
0.5258
0,4211
0.6199
0.6088
0.5937
0.6024
MUSIQ
56.47
55.20
55.90
41.01
40.56
50.03
58.50
67.31
66.14
63.69
64.55
9.27
14.54
8.99
10.08
11.32
8.4]
8.94
12.08
12.47
23.45
6.74
DOVERT
0.2239
0,3278
0,2830
0.3013
0,2027
0.3790
0.3192
0,4131
0,4100
0.4711
0,4319
Effiiinccy
#Steps]
30
50
13
#Param, (B))
0.04
14.44
1.43
2.49
8.24
10.55
1.30
1.30
0.46
1.55
0,57
Time (s1)
0.35
66.39
32.34
96.38
50.61
4.42
6.36
2.94
0.52
2.81
real video and detail-rich real image data from Vpe
Vid M (Nan et al., (0244 and LSDIR (Li al.
2023). We use batch size of 8. with 25 frames per video clip and spatial resolution of 512 x 512.
The RealBasicVSRR degradation pipeline (Chan et al. 2022b) is applied to synthesize LR-HR video
pairs. All experiments are implemented in Py Torch, and trained with the Adam optimizer (Kingmal
2014) on 8 NVIDIA H20 GPUs (96GB each), with the full training process taking about one day.
Test Datasets. Following Liu et al. ) Wang et al. (2025a); (Snn et al. (2025), ) we test VSR and compare it with other methods using the same synthetic and weal-world datasets as DOVE (Chen et al. 2025b). The three synthetic test datasets include UDMI0 eY al. 2019) (10 videos), SPMCS (Tao et 2017) (30 videos), and YouHQ40 (Zhou et al., 2024) (40 videos), which are synthesized with the same degradation pipeline as during training, using scaling factor of 4 for Real-VSR task. The three real-world datasets are RealVSR (Yang et al. (02T) (50 videos), MVSR4x (Wang et al.) 2023b) (15 videos), and VideoLQ (Chan al. (002bb) (50 videos). All videos are pre-processo via clipping the first 25 frames and applying center-cropping fixing the output spatial size to 512 x 512.
Evaluation Metrics. We employ both full-reference and no-reference metrics for performance eval- uations. Full-reference metrics include PSNR and SSIM (Wang et al.) (004) for fidelity, as well as LPIPS (Zhang et al.) (2018) and DISTS (Ding et al. (2020) for perceptual quality. No-reference met- rics include MANIQA (Yang et al.] 2022), CLIPIQA (Wang et al. 2023a), and MUSIQ (Ke et al.) 202T]. In addition, following Yang et al. (2024a); Zhang et al. (2024b); Sun et al. (2025). we report the flow warping error Ex (Lai et al. 2018), scaled by 10-3, as in DOVE (Chen et al. 2025b), and employ DOVER (Wu et al. 2023). 3. to evaluate temporal consistency and video quality, respectively.
4.2 COMPARISON WITH STATE-OF-THE--ATS
Compared Methods. Following Liu et al. (2025);
) Wang et al. (025a) Sun et al. (2025); : Chen et al. (2025)), we compare the proposed ASR model with seven representative Real-VSR approaches: the non-gennrattiv RealBasicVSR (Chan al. (002b); multi-step diffusion-bascd Upscale-A-Video (Zhou et al. 2024). MGLD-VSR (Yang et al. 2024a), and STAR (Xie et al., 2025b); as well as one- step diffusion networks SeedVR2 (Wang al. (025a), DOVE (Chen et al.) 22026), and DLORAL of-te----hh--aa
(Sun et al. 2025). Additionally, we include three of the-hhhrrrt art one-step diffusion- based Real-ISR approaches: PiSA-SR (Sun et al., a (2024), AdeSR (Chen et al. (025a), and HYPIR (Lin at. 2025c), which super-resolve video frames independently, for comprehensive evaluation and comparison.
Video Quality Comparison. Tab. D quantitatively demonstrates that our VSR achieves compet- itive performance across broad range of metrics. First, it ranks within the top three in most cases, surpassing the majority of competing approaches and confirming its effectiveness in restoring high- quality video frames. Second, VdR achieves strong temporal consistency with smallest warping errors, as indicated by its superior Ewap results. In contrast, Real-ISR models perform the worst on this metric because they lack temporal modeling, which leads inconsistent content across frames. Third, when compared with the previous best approaches DOVE (teacher) and PiSA-SR in fidelity, perceptual quality, and warping error, Adc remains highly competitive across all these aspects.
7

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Input
Upseale-A- Video
PISA-SR
AdeYSR (Ours)
T- TGG
4
Impal
RealBusicVSS
MGLD-VSS
SeedVRR yR2
DLoRAL
AdeSR
AdeVSR (Our))
HELP
Figure 3: Qualitative comparison of RSR performance 13th frames of two videos: "028" from RealVSR (top) and "016" from Videol.Q . (bottom). Temporal profiles are provided below each frame, obtained by taking slices along the width-temporal plane at the vertical centers of the frames.
The advantages of Ade VSR also verified by the qualitative comparison in Fig. Our network constructs sharp and realistic details, while RealBasicVSRR Upscale- A-iico, MGLD-VSR, DOVE, and DLORAL yield over-smoothed outputs. STAR and AdcSR bring artifacts to boat's 's windows and facial regions, whereas PiSA-SR and HYPIR produce fewer details on building and water surface, or generate visually unrealistic boat textures. Moreover, they suffer from significant temporal instabil- ity, evidenced by the erratic fluctuations in their temporal profiles, resulting in unpleasant flickers that degrade overall video quality. comparison, our AdcVSR not only reconstructs natural details on buildings, boat structures, water textures, facial components, clothing, hat, and signage with less distortion, but also maintains smooth transitions across consecutive frames with reduced flickering.
It is worth highlighting from Tab. aand Fig. 3 that, despite their poor temporal consistency, Real-ISR diffusion networks PiSA-SR. AdeSR, and HYPIR, with only 2D spatial attentions and convolutions, highly effective at removing degradations in individual video frames and generating rich details. This results in high-quality outputs with strong scores on no-reference perceptual metrics, including MANIQA, CLIPIQA, MUSIQ, and DOVER, which often align better with human perception than traditional metrics like PSNR. This observation is consistent with hypothesis (1) in Sec. 3.2 Build- ing on this insight, our method employs lightweight temporal convolutions together with dual-head adversarial distillation, allowing the preservation of strong per- pea-frame detail quality while improving fidelity and temporal consistency across all frames.
AdcVSR (Ours)
Efficiency Comparison. The final 4 rows of Tab. and the bubble plot in Fig. compare Real-VSR per- formance in temporal consistency (Ewarp). step num- bers, parameter numbers, and inference times across different approaches. Built upon an effective "2D +
SeedVR2
STAR
Consikent
MGLL--SRR
DOVE
OLORAL
Flow Warping
Upscale-A-Video
AdcSR
PISA SR
Parameter Number
ID" architecture, and learned from the large 3D DiT teacher DOVE using our dual-head adversarial dis- tillation scheme, AdR delivers both strong tem- poral consistency with the best Ewar results and ef- ficiency gains. Specifically, it reduces parameters by 96%, 60%, and 77%, and accelerates inference by x,1x, 59x, and 175x over the multi-step diffusion- based methods Upscale-A-Videe Video,, MGLD-VSS DSR, and STAR. Against one-step diffusion- based RSR models SeedVR2 and DLORAL, it achieves param- eter reductions of 93% and 56%, and accelerations of 110x and 308 x, respectively. Compared with its teacher DOVE, Adc achieves 95% % reduction in parameters and an 8x speedup while maintaining very competitive video quality. Overall, Adc is substantially faster and more lightweight than most existing approaches, verifying the effectiveness of our improved ADC method and the high efficiency of the resulting compressed diffusion network.
18
HYPIR
107
107
10f
Time
Figure 4: Performance comparison among diffusion-based Real- VSR methods in tem- poral consistency and complexity (parameter number and inference time) (see Tab. [T). Ad- cVSR attains the lowest warping error Exarp- the second-lowest parameter number, and the second
inference speed. Bubble col- ors represent method types: green for multi- step, blue for one-step, and red for AdR
8

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4.3 ABLATION STUDY
Table 2: Comparison of network designs on UDMI0.
Method
DISTS!
Eaupt
#Param. (B)L
Effect of "2D 1D" Network Design. Tab.
3D (A Pruned DOVE)
0.2098
2.53
8.36
and Fig. [ compare three student architectures:
2D (AdeSR)
0.2418
4.43
0.52
pruned 3D DiT (based DOVE) obtained by
2D + ID (Ours)
0.2112
1.67
0.55
the original ADC approach, a 2D SD backbone (AdeSR), and our Adc The 3D DiT model delivers the best DISTS and strong Emarr but re- mains heavy. Replacing with 2D backbone severely degrades performance, showing that model without temporal modeling cannot effec- tively learn from temporally aware teacher, or maintain both frame quality and inter- frame co- herence. leading to visible flickering. By introducing ID convolutions, our design restores temporal modeling capacity while preserving efficiency: it achieves the lowest Evapp narrows DISTS gap to 3D model to 0.0014 with 7% parameters, and produces sharp textures and smooth temporal profiles.
Input
3D
2D
2D + 1D (Ours)
Figure 5: Comparison of different network designs.
Effect of Dual-Head Discriminators. Tab. compares three VSR variants with differ- ent settings for the discriminators: (1) single-
Table 3: Comparison of discriminators on Youl
Method
CLIPIQAT
Emapl
Single-Head, Dual-Domain
0.6745
6.32
head (only one output without distinguishing details and consistencies), (2) single-domain
Dual-Head, Single-Domain
0.6421
3.59
Dual-Head, Dual-Domain (Ours)
0.6861
2.22
(only one feature-domale discriminator as in the original ADC), and (3) our proposed scheme. The single-head variant preserves frame quality but shows much worse Eiarr indicating that consistency less optimized during its adversarial training. The single-domain variant improves consistency but reduces perceptual quality due to the absence of pixel-domain supervision. In contrast, our method achieves best performance on both metrics, effectively balancing details and temporal consistency.
Effect of Adversarial Distillation. In Tab. 4 we compare VSR variants under different setups of adversarial learning and distillation.
Table Comparison of distillation setups MVSR4x. Method
PSNR
LPIPS
MUSIQU
No Adversarial Loss
23.97
0.3596
54,33
Removing adversarial losses or relying solely on ground truth (GT) supervisions noticeably degrades LPIPS and MUSIQ, indicating that both adversarial training and teacher's guid- ance are essential for perceptual quality. Using SeedVR2 or DLORAL as teachers yields promising but weaker results. By contrast, our adversarial distillation with DOVE as teacher strikes a favorable balance across three metrics, demonstrating that adversarial learning combined with an appropriate teacher important for improving Real- VSR performance in both fidelity and perceptual realism.
No Teacher (HR GT Only)
24.85
0,3641
50.32
ScedVR2 as Teacher
23.24
0.3489
60.74
DLoRAL as Teacher
23.08
0,3554
54.61
DOVE Teacher (Ours)
23.81
0.3337
61.48
Due to page limitations, more experimental results, analyses, and discussions are presented in the Appendix.
5 CONCLUSION
In this work, we proposed an improved Adversarial Diffusion Compression (ADC) method for real- world Video Super-Resolution (Real-VSR). Instead of relying computationally heavy 3D spatio- temporal attentions as in existing diffusion Transformer (DiT)-based approaches, our model adopted compact "2D ID" design: a pruned 2D Stable Diffusion (SD) backbone for synthesizing details, augmented with lightweight 1D temporal convolutions to enforce inter-frame coherence, while their combination also proved effective in removing degradations. To address the conflicts between opti- mizing detail richness and temporal consistency in Real- while leveraging the knowledge of large 3DD DIT teacher DOVE as well as diverse real video and image data, we introduced dual- head- dual-discriminator adversarial distillation scheme that disentangles and jointly optimizes details and consistency via pixel- and feature-domain supervision. Across synthetic and real-world benchmarks, the resulting Ade model achieved competitive video quality while being substantially more effi- cient than its 3D DiT teacher, offering a 95% parameter reduction and an 8x inference acceleration, striking strong balance among fidelity, detail richness, temporal consistency, and model efficiency. Beyond -SRR our work provides a systematic recipe for building efficient video reconstruction systems, delivering practical guidelines for diffusion model compression and real- world application.
9

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APPENDIX
Our main paper outlines the core ideas and techniques of the proposed approach, demonstrating the effectiveness of our main methodological contributions, including (1) the "2D + 1D" network archi- tecture design and (2) dual--ead adversarial distillation scheme, via experimental validation. In this Appendix, we provide additional information, including the training pseudocode of our improved ADC method in Sec. A more ablation studies in Sec. B additional comparison results in Sec. discussion on limitations Sec. D and statement of large language model (LLM) usage Sec.
A TRAINING PSEUDOCODE OF ADCVSR
The training pseudocode of AdcVSR is outlined in Algo. | and consists of two consecutive stages, as explained in Sec. 4.1 In Stage 1, we augment the pretrained AdeSR model by adding ID) resid- ual blocks (RBs), each containing two temporal convolutions, to create AdR We then perform knowledge distillation from the large 3D DiT teacher DOVE without adversarial learning, by mini- mizing the errors between the student's and teacher's outputs in both the pixel and feature domains. In Stage 2, we initialize the student model with the pretrained version from Stage I, and incorporate both the pixel- and feature-domain discriminators. Adversarial losses in both domains further refine the student's ss ability to generate rich, realistic details and maintain temporal consistency. This stage allows the model to effectively learn from DOVE while achieving a balance hetween detail richness and temporal consistency, thus addressing the conflict between optimizing these objectives.
Algorithm 1: Training of Our Proposed AdcVSR
Input: Pretrained 3D DiT DOVE, AdeSR, and SD2.1 models; Weights Xpisel. Meatures and Agdv.
Stage 1: Knowledge Distillation Without Adversarial Learning
Add 1D temporal convolutions to AdeSR as described in Sec. 3.2 to obtain VSR network; for number of training iterations do
Sample batch of LR-HR video pairs (XLR, XHR):
Compute the outputs Xecacher and freacher from DOVE (teacher);
Compute the outputs Xstddet and faudent from Ade (student);
Compute the distillation loss:
DISTS(Xaudent, Xatudent Xieacber )1 Areatue [audent freacher Il Update the model weights of AdR (student) using the Adam optimizer;
Laistil Apixet [|ldent Xeeache 1 + DISTS
Stage 2: Dual-Head, Dual-Diseriminator Adversarial Distillation
Initialize AdR (student, generator) from the pretrained model in Stage 1; Initialize pixel- and feature-domain discriminators Dpicel and Dieature as described in Sec. 4.1 for number of training iterations do
Sample batch of LR-HR video pairs (xLR, XHR)
Compute the outputs Neaacher and freacher from DOVE (teacher); Compute the outputs Xstudeni and faudent from AdcVSR (student);
Compute the distillation loss as in Eq. Tp:
Update the model weights of ASR (student) using the Adam optimizer; Sample three batches of video and image data: Xvideo Ximage, and Ximage'
Construct batch of pseudo-videos x by randomly permuting the frames within each
individual video of Xviden, ensuring that each real video is shuffled independently;
Compute features f f Ci finnges and finuge using Xvidcos x video Ximages and image
Compute losses for discriminators Dpisel and D Deeaure as in Eq. (4);
Update the model weights of discriminators using the Adam optimizer;
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B MORE ABLATION STUDIES
Effeet of "2D + ID" Network Design. Tab. 5
Table 5: Comparison of network designs on SPMCS.
and Fig. [ compare four students: (1) 2D SD
Method
LPIPS. MANIQAT
backbone (AdeSR) without temporal modeling.
2D (AdcSR)
0.3625
0.6120
3.84
same as in Tab. 2 (2) our AdR with one 1D residual block (RB) after each UNet block, (3)
2D + ID Temp. Cony. (Ours)
0.3282
0.6500
1.59
2D + 1D Temp. Conv. Doubled
0.3215
0.6462
1.68
2D ID Temporal Attention
0.3391
0.6384
2.08
variant with doubled ID convolutions, and (4)
variant replacing the ID convolutions with tem-
0.5
poral self-attention modules (Zhou al. 2024). The 2D network suffers from large performance drops: without temporal receptive fields, distil- lation becomes difficult and both temporal con- sistency and frames' quality degrade heavily, as reflected in its poor metrics and higher loss val- ues. Adding ID temporal convolutions notably reduces the loss a lower plateau and improves video quality by providing the network with the ability to capture temporal dependencies. Dou- bling ID convolutions brings no clear gains, in- dicating that the main factor for student performance is the acquisition of temporal receptive fields rather than simply increasing parameter number or depth. Replacing 1D convolutions with temporal attentions does not improve quality or efficiency, and makes optimization more difficult, as shown by higher converged loss. Overall, our design achieves strong LPIPS, MANIQA, and F results with minor overhead, verifying that adding several 1D) temporal convolutions to 2D backbone yields an effective student capable of learning from 3D DiT teacher while remaining compact and efficient.
20 (AdcSR)
0.4
2D Temp. Conv. (Ours)
Loss Loss Value
2D + 1D Temp. Conv. Doubled
0.3
2D 1D Temporal Attention
0.2
0.1
50
100
150
200
Iteration (K)
Figure 6: Comparison of distillation loss in Stage 1.
Effeet of Dual-Head Diseriminators. To ex- amine how allocating channels between the two heads influences outputs' quality, we vary their split under a fixed total of 256 channels across
Table Comparison of dual-head splits on RealVSR.
Head Split (Detail/ Consistency)
MUSIQT
DOVERT
100% 0% (256 Channels)
73.10
8.85
0.4520
75% / 25% (192/ / 64 Ch., Ours)
72.95
3.28
0.4875
five settings, as presented in Tab. o and Fig. 7 At one extreme (100% / 0%)) the setup degener- ates to a single-head detail discriminator, which achieves the best MUSIQ result with sharp de- tails but fails to maintain temporal consistency, yielding the worst Ewap and low DOVER, lead- ing to visible flickers in temporal profiles (e.g., in the upper branches, building facades. stripes, and lower vegetation). At the opposite extreme (0% / 100%), the model suppresses inter-frame Figure 7: Comparison of various head channel splits. variation, slightly reducing Waap but at the significant expense of perceptual fidelity, producing over- smoothed textures and artifacts. Our default split (75% / 25%) strikes a strong balance: it achieves the highest DOVER while maintaining high MUSIQ and low E showing that dedicating mod- est portion of capacity to "consistency" is sufficient when both heads share the same backbone and output layers. Intermediate splits (50%) / 50%, 25% / 75%) do not clearly improve performance, yielding lower MUSIQ and DOVER with few leaf details. Overall, these results confirm that channel allocation between the two heads is critical, and that assigning a minority share (25%) ) consistency delivers balanced optimization of detail richness and temporal consistency, validating our design.
50% / 50% (128 / 128 Channels)
70.80
3.22
0.4802
25% 75% (64 / Channcis)
68.39
3,15
0,4631
0% / 100% (0/ 256 Chunnels)
65.21
3.18
0,4410
100% / 00 75% / 25% 50%
50%
25%
75%
0% / 100%
Additionally, to validate whether sharing backbones and tail convolutions is necessary for two heads in D, we compare our dual-head design (shared backbones and tail convolutions for the two heads) against a variant employing separate single-heat discriminators for both details and consistency (no sharing). In the latter case, the "consistency" branch produces unstable loss values and the train- ing collapses. We conjecture this because shuffling frames (xiidoy) or assembling different images (Xingge) ) as "fake" videos may not provide sufficient supervision for independent consistency discrim- inators. In contrast, forcing two heads to share the same backbone and tail convolutions stabilizes training: the last-layer features are compelled to encode both detail realism and temporal coherence, allowing the consistency head to judge temporal flickering by leveraging detail information. In this way, our design introduces an implicit regularization, making the optimization of consistency stable.
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Table 8: Quantitative comparison of Real-VSR performance on 4 additional benchmark datasets.
Method ReaiBasicVSR] U Upscale-A-VV Video NGLD-VSR STAR SecdVR2 DOVE DLORAL PISA-SR Ad:SR HYPIR AddVSR (Ours)
Test Dataset: SPMCS (Synthetic)
PSNRT
20.69
19.83
20.33
20.32
20.89
20.94
20.35
20.12
20.14
18.13
20.83
SSIMT
0,5056
0.4301
0.5201
0.4897
0.5234
0.5221
0.4955
0.4674
0.4702
0.4284
0.5055
LPIPS.
0.3886
0,4336
0.3583
0.5657
0.3122
0.3165
0.3531
0.3662
0,3695
0.3969
0.3282
DISTSL
0.2334
0.2466
0.2198
0.2957
0.1823
0. 1896
0,2164
0.2286
0.2304
0.2251
0.2036
MANIQAT
0.5977
0,5862
0.5941
0.3654
0.6289
0.5774
0.6066
0.6661
0.6332
0.6961
0,6300
CLIPIOA
0.4563
0.4892
0.4358
0.2663
0.4764
0.6971
0.6716
MUSIQU
64,88
66.30
64.96
34.76
63.89
68.41
63.67
70.61
69.78
70.23
69.94
E Exom
1.73
2.81
1.83
1,10
1.38
2.76
5,30
5.52
13:33
1.59
DOVERT
0,2792
0.3834
0,3326
0.2353
0,3769
0:4759
0.3691
0.4445
0.4638
0.5383
0.4622
Test Dataset: YouHQ40 (Synthetic)
PSNR
21.74
22.38
21.35
22.70
21.53
21.51
SSIMI
0,5491
0.4817
0.5679
0.5462
0,5844
0.6068
0.5322
0.5166
0.5204
0.4782
0. 0,5999
LPIPS
0.4039
0.4137
0.3796
0.5195
0.2839
0.3319
0.3454
0,3632
0,3603
0.3903
0.3539
DISTS]
0.2324
0,2200
0.2199
0.2627
0.1604
D. 1935
0.1955
0.2164
0.2071
0.2163
0.2126
MANIQAT
0.5860
0,6122
0.5860
0.3622
0.6212
0.5058
0.6072
0.6557
0.6330
0.6718
0.6587
CLIPIQA
0.4557
0.4896
0.4343
0.2650
0.4156
0.5111
0.4698
0.6973
0.6921
0.6870
0.6861
MUSIQI
62.14
63,42
61.97
33.48
61.47
59.56
59.68
69.41
67.97
69.12
68.28
Fxup
4.30
6.20
4.41
2.38
5,30
2.63
4.51
7.27
7.39
17.88
DOVER1
0,3507
0,5691
0.4288
0.3767
0.5011
0.5649
0.5015
0.5930
0.5993
0.6393
0 0.6098
Test Datuset:
VSalVSK (Real. World)
FSNRT
21.98
20.47
21.44
18.07
20.75
22.05
18.46
19.87
19.27
16.52
27.53
SSIMI
0.7113
0,6005
0.6450
0.5273
0.66970
0.7234
0.5284
0.6113
0,5634
0.4928
0.6668
LPIPS
0.1899
0,2540
0.2439
0.3180
0.1810
0.1703
0.1937
0.2092
0.2590
0.2975
0.1920
DISTSL
0,1143
0,1489
0.1499
0.1742
0.1080
0.1111
0.1133
0.1511
0,1657
0.2019
0.1364
MANIQAT
0.6755
0.6278
0.6479
0.6431
0.6746
0.6769
0.6837
0.7374
0,0655
0.7671
0.6939
CLIPIQA
0.3418
0,4340
0.3273
0.3289
0.3137
0.5194
0.4331
0.6822
0.6881
0.6704
0,6646
MUSIQI
67.96
67.18
63.80
71,55
70.36
73.83
72.49
73.07
3.60
5.06
3.18
7.16
4,21
3.82
5.30
8.97
13.78
22.82
3,28
DOVERT
0,3482
0.3420
0.3353
D.4063
0,4028
0,5284
0,3789
0.5372
0,4578
0.5711
0.4875
TEst Datavel: MVSR4x (Real. Worldy
PSNRT
21.89
21.30
22,55
21.50
22.18
22,15
21.87
21.95
12.03
20.81
23.81
SSIM
0.7181
0.66622
0.7325
0.7346
0.7597
0.7292
0.7182
0,7115
0.7017
LPIPS
0,3816
0.4161
0.3578
0.4529
0.3419
0.3643
0,3487
0.3727
0,3960
0.4133
0.3337
DISTSL
0.2498
0.2657
0.2446
0.2781
0.2453
0.2500
0.2488
0.2638
0,2756
0.2537
MANIQAT
0,5378
0,5316
0,4930
0.2829
0.3265
0.4973
0.4983
0.5231
0.5367
0.5470
0.5283
CLIPIQA
0.4729
0,4938
0.3221
0.2299
0.2258
0.5453
0.4560
0.6086
0.6527
0.5807
0.5911
MUSIQI
58.35
61.34
$4.07
24.76
29.14
62.22
51.47
61.27
62.46
56.05
61.45
A
1.64
3.10
1,94
0.78
0.98
1.01
1.60
3,02
3.12
4.54
0.79
DOVERT
0,2455
0,3652
0,2428
0.0951
0.0831
0.3811
0,2586
0.3684
0,3824
0.4006
0,3860
Effect of Curated Data for Dual-Head Train- ing. We ablate the five curated data types used to train the dual-head discriminators, and inves- tigate whether they are necessary to disentangle details' richness from temporal consistency. As shown in Tab. 7 first, training only with videos Xvidea and images Ximage (i.e., removing the shuffled videos Xiideo and the assembled image sequences Ximage) fails to penalize flicker: Ewarp increases and LPIPS worsens, indicating that the heads' objec- tives are entangled and optimization deteriorates. Second, if we label real video details as positives for the detail head, temporal consistency remains decent and Eaar strong, but perceptual quality drops. This might stem from that most real videos in Open Vid-IM are not as high-quality or detail- rich the images of LSDIR, causing the model align with lower-quality frames. Third, replacing real videos with teacher outputs Xwacher and feacher as "real" for the consistency head degrades both perceptual quality and consistency, with worse E This may be due to that teacher super-resolwe results lack real, natural temporal dynamics of real-world videos. In contrast, our scheme combines teacher outputs with real images/videos to provide complementary supervision signals. This leads to the lowest LPIPS, the highest CLIPIQA, and the best Eaarp confirming that our curated data for the two heads are essential to avoid collapse and to balance the optimization of details and consistency.
Table 7: Ablation of curated data for 22 on UDMI0.
Training Configuration
LPIPS CLIPIQAT Nag
No xi and No Xingg (Types 3 & 5)
0.3430
0.6521
5.92
Real Videos us Detail-Real (Labe) "1")
0.3152
0.6703
1.98
Teacher Videos Replacing Real Videos
0.3224
0.6652
2.47
Full Scheme (Ours)
0.3065
0.6818
1.67
C MORE COMPARISON RESULTS
Quantitative Comparison. In Tab, g we present a comprehensive comparison of AdcVSR against ten state-of-the-ard approaches (same in the main paper), evaluated with nine metrics across four test datasets: SPMCS, YouHQ40, RealVSR, and MVSR4x. Consistent with the results in Tab. [T Ad- cVSR ranks within the t3p-3 in 24 out of 36 cases two-thirrs overall), a higher proportion than other competing approaches, demonstrating its competitive Real-VSR performance. While advanced one- step diffusion-bar based Real-VSR models (SeedVR2, DOVE, ALORAL) and leading Real-ISR models
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Imput
RealBasicVSR
Upscale-A A-iieo
MGLD-VSR
STAR
SeedV
DOVE
DLoRAL
PISA-SR
AdeSR
HYPIR
AdeVSR (Ours)
Input
RealBasic VSR
Upseale-A- A-Vico
MGLD-VSR
STAR
SecdVR2
DOVE
DIARAL
PISA-SR
AdeSR
HYPIR
AdeVSR (Ours)
Inpul
ReulBasic
Upscale-A A Video
VSR
STAR
SeedV
DOVE
DLoRAL
PR
AdeSR
HYPIR
AdRVSR (Ours)
Figure 8: Qualitative comparison of Real-VSR performance on three real videos: the 4th frame of "029" (top), the 2nd frame of "090" (middle), and the 15th frame of "247" (bottom) from RSR.
(PiSA-SR, AdeSR, HYPIR) perform well, they tend to be biased toward either fidelity or perceptual quality but rarely both. In comparison, ASR not only achieves many top-3 and top-2 results but also several times secures the best (top-1) scores, exhibiting a favorable balance across fidelity, per- ceptual quality, and temporal consistency, delivering both high-quality video frames (PSNR, SSIM, LPIPS, DISTS, MANIQA, CLIPIQA, MUSIQ), and strong temporal consistency (E ), as well as overall video quality (DOVER). Importantly, compared with its teacher networks DOVE and PiSA- SR, ASR attains comparable fidelity and perceptual quality while maintaining competitive tem- poral consistency. Note that it is much more efficient, requiring only 5% and 44% of the parameters, while providing 8x and 5x faster inferences over DOVE and PiSA-SR, respectively. These results
19

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validate that VSR is both effective and efficient, confirming the effectiveness of the "2D ID" architecture design and stal-head adversarial distillation scheme in our improved ADC method.
Qualitative Comparison. Fig. g shows visual comparisons across super-resolved videos produced by different approaches. We observe that RealBasicVSRR .GL.-MGLDDDDD VSR, SeedVR2, DOVE, and DLo- RAL often fail to recover sufficient fine details, resulting in over-smoothed blurry textures, in the orange hull region of the ferry and in the foliage areas. Methods including Upscale-A- Video, STAR, and AdeSR tend to introduce noticeable artifacts, particularly in high-frequency regions like tree leaves and the ship bow, leading to unnatural appearances. PiSA-SR and HYPIR produce rela- tively sharper results but sometimes generate unrealistic or inconsistent details, occasionally accom- panied by shifts in brightness compared with the input. In contrast, our VdR produces sharp and realistic details with a balanced trade-off between fidelity and pereeptual quality, while avoiding the artifacts and distortions observed in other approaches. Furthermore, the temporal consistency of our results remains stable across consecutive frames, as reflected in its smooth temporal profiles without significant flickering. Overall, these results are consistent with quantitative findings, validating the ability of AdR to generate rich and fine visual details while preserving temporal coherence.
D LIMITATIONS
Despite the effectiveness of proposed AdR several limitations remain. First, while the approach produces rich and sharp details, certain challenging structures (e.g., fine textures such as foliage, wa- ter surfaces, or transparent regions such as glass) are still not faithfully reconstructed. In these cases, AdR may generate softened details, or partially hallucinated patterns. Second, the performance of our model is inherently tied to the base SD22. backbone and its teacher networks. Although our improved ADC method effectively compresses them, the capacity gap between student and teacher can still limit final reconstruction fidelity, particularly under complex motion or severe degradations. Third, the reliance on curated video and image datasets together with adversarial learning makes the model sensitive to training data quality and might introduce instability during optimization. Finally, while our design strikes a strong balance between fidelity, perceptual quality, and temporal consis- tency, the trade-off's are not perfect, and minor flickerigg may still occur under extreme degradations.
Future Work. the future, we plan to leverage stronger generative priors from larger networks, in- vestigate more advanced temporal modeling strategies, and refine the adversarial distillation scheme to further enhance reconstruction performance and robustness. These directions may help resolve the above limitations and extend the applicability of our method to more diverse real-world scenarios.
E
LLM USAGE STATEMENT
We used large language model (LLM) only for minor grammar and phrasing polishes. All technical content, including ideas, experiments, analyses, and discussions, was entirely created by the authors.
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Reviews:
[{"review_id": "IRzl3seu62", "reviewer": "Reviewer_GsDk", "summary": "This Paper proposes an improved Adversarial Diffusion Compression method. The core idea it to distill knowledge from a large-scale 3D teacher model into a well-designed and lightweight ‘2D + 1D’ student model. This is achieved through a novel adversarial distillation scheme. The proposed approach effectively addresses the critical challenge of preserving both spatial details and temporal consistency, a common problem in the compression of video super-resolution models.", "questions": "Please see the weaknesses above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThis paper proposing a “2D spatial + 1D temporal” decoupling hypothesis, and introduces a novel lightweight “2D+1D” architecture. This approach drastically cuts down on parameters and computational load, enabling efficient inference.\n2.\tThe paper proposes a novel dual-head adversarial distillation scheme. This scheme effectively balances the richness of spatial details with temporal coherence, which is a critical challenge in the field of video super-resolution.", "weaknesses": "1.\tA core assumption of this paper is that a 2D diffusion model is sufficient for synthesizing fine-grained details. However, this assumption is challenged by the experimental results. Currently, the ablation study comparing the 2D and 3D backbones is based only on the DISTS metric. To provide a more balanced and convincing comparison, the authors should consider including additional metrics that measure perceptual quality (e.g., LPIPS) and/or fidelity (e.g., PSNR, SSIM). Furthermore, the qualitative results in Figures 3 and 5 exhibit clear visual artifacts or \"hallucinations.\" These findings suggest that the 2D diffusion model may be insufficient for generating correct details, and therefore, the validity of this core assumption is questionable. The authors should address this limitation, perhaps by discussing the trade-offs of their approach or analyzing why these artifacts occur.\n2.\tThe paper states that the 'detail head' label for real videos is set to 'unlabeled', with the provided justification being to “encouraging the generator to produce more detail-rich frames”. This is a critical design, yet the underlying mechanism or rationale is not sufficiently explained. It is unclear why the more intuitive approach—labeling the details from real videos as 'real'—was not adopted. The authors should clarify whether this decision is supported by empirical findings (e.g., from an ablation study) or if it is grounded in some theoretical justification.", "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": "FATQPC6TjL", "reviewer": "Reviewer_r2a8", "summary": "This paper introduces AdcVSR, an improved adversarial diffusion compression framework tailored for real-world video super-resolution (Real-VSR).\nThe method builds upon the concept of Adversarial Diffusion Compression (ADC), proposing a “2D + 1D” hybrid architecture that replaces heavy 3D diffusion backbones with a 2D spatial diffusion network (a pruned SD2.1) augmented by lightweight temporal 1D convolutions.\nFurthermore, it introduces a dual-head dual-discriminator adversarial distillation scheme, where two discriminators (in pixel and feature domains) independently supervise “detail” and “temporal consistency.”\nExperimental results on multiple Real-VSR datasets demonstrate that AdcVSR achieves competitive visual quality while reducing parameters by up to 95% and achieving an 8× inference speedup compared to the teacher model DOVE.", "questions": "1. Why does AdcVSR use a video diffusion model as the teacher but an image diffusion model as the backbone? How does a 3D spatio-temporal DiT as the student network compare with the 2D+1D architecture in terms of performance and efficiency? I recommend that the authors include related experiments.\n\n2. Why did the authors choose to use a 2D VAE? I think that employing a 3D VAE could lead to faster inference and better temporal consistency.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed dual-head discriminator effectively disentangles spatial detail enhancement and temporal consistency, addressing a long-standing trade-off in Real-VSR.\n\n2. The results on multiple datasets and metrics (PSNR, LPIPS, MUSIQ, MANIQA, etc.) are convincing and show both efficiency and quality improvements. And the visual quality is also satisfactory.\n\n3. The 2D+1D design combined with adversarial distillation is simple yet efficient, offering clear insights into practical diffusion model compression.", "weaknesses": "The paper lacks formal justification for why the dual-head adversarial loss leads to better convergence or perceptual trade-off control.", "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": "m0uYq0wIXV", "reviewer": "Reviewer_7Ea8", "summary": "This paper proposes an improved adversarial diffusion compression method for real-world video super-resolution tasks. The authors note that while existing diffusion-based VSR methods can generate detail-rich videos, their inference speed is slow; conversely, one-step approaches are faster but still result in bulky models. To address this, the authors introduce AdcVSR, which distills a DiT teacher model with 3D spatiotemporal attention into a lightweight \"2D + 1D\" student network (based on a trimmed Stable Diffusion 2.1 backbone combined with 1D temporal convolutions). Additionally, a dual-head dual-discriminator adversarial distillation strategy is introduced to decouple the discrimination of detail richness and temporal consistency in the pixel and feature domains, respectively. This approach significantly improves efficiency while maintaining video quality. Experiments demonstrate that AdcVSR reduces parameters by 95% and accelerates inference by 8 times, while still achieving visual quality comparable to the teacher model.", "questions": "Is the weight allocation (75%/25%) between the \"detail head\" and \"consistency head\" in the dual-head discriminator universally applicable? Would this ratio remain effective across different datasets or tasks?\n\nWere other teacher models (e.g., SeedVR2, DLoRAL) explored? Was DOVE selected solely because its structure is more suitable for this method?\n\nIt is recommended to include performance on longer video sequences (e.g., >100 frames) to validate the stability of temporal modeling, and providing failure cases or limitations analysis, such as performance under complex motion or extreme degradation, would be beneficial.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "Originality: The novel \"2D + 1D\" architecture design, combined with the dual-head discriminator adversarial distillation strategy, effectively decouples the optimization objectives for detail and consistency, demonstrating strong innovation.\n\nQuality: Comprehensive experimental designs, including extensive validation on multiple synthetic and real-world datasets, support the effectiveness of the method through both quantitative and qualitative results. Ablation studies also thoroughly verify the contribution of each module.\n\nClarity: The paper is well-structured, with detailed method descriptions, and the inclusion of diagrams and pseudocode aids understanding. The writing is generally fluent, and technical details are clearly expressed.\nImpact: The proposed method achieves a notable balance between efficiency and quality, offering significant practical value for deployment and providing a feasible pathway for the compression and application of diffusion models in video tasks.", "weaknesses": "Insufficient Comparative Experiments: Although comparisons are made with several SOTA methods, there is a lack of comparison with recent non-diffusion-based efficient VSR approaches, such as those based on CNNs or lightweight Transformers.\n\nLimited Generalization Validation: All experiments are conducted at a fixed resolution (512×512) and frame length (25 frames), without demonstrating performance on longer videos or higher resolutions.\nWeak Theoretical Support for Dual-Head Discriminator Design: While experiments prove its effectiveness, there is insufficient theoretical or visual analysis explaining why the \"shared backbone + separate heads\" design outperforms independent discriminators.\n\nIncomplete Computational Efficiency Comparison: Only parameter counts and inference time are provided, without more detailed efficiency metrics such as FLOPs or memory usage.", "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": "zKnF1LBe4W", "reviewer": "Reviewer_c8ag", "summary": "This paper proposed an improved adversarial diffusion compression method for real-world Video Super-Resolution. It comprises a 2D SD backbone and several 1D convs to replace the heavy 3D DiT architecture to improves its efficiency. By using a dual-head discriminator, it balances the optimization between detail enhancement and temporal consistency for adversarial distillation scheme. Experiments on synthetic and real-world benchmarks show its potential in compressing Real-VSR model on both model size and inference steps effectively.", "questions": "* Using stronger generative backbone. Using SD2.1 as 2D backbone limits the capacity on complex degradations. Authors should consider utilizing a large-scale backbone.\n* I strongly recommend authors to provide a video demo or video files corresponding to the results presented in the paper. Given the usage of a ``2D+1D'' architecture, verifying the temporal quality of the final results is essential. Displaying only a single frame from the video within the article is not very convincing. I will consider raising my score after evaluating the video results.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* This paper proposes an intuitive explanation to use a ``2D + 1D'' architecture for student model to improve its efficiency.\n* Detailed designs on adversarial distillation scheme (e.g., data type, training loss) help improve its clarity.\n* Thorough experiments on synthetic and real-world benchmarks demonstrate its effectiveness after distillation.", "weaknesses": "* I remain skeptical about the effectiveness of replacing the 3D DiT architecture with a ``2D+1D'' architecture. As demonstrated in the paper, it uses a radical distillation strategy to achieve a 20-fold reduction in parameter count and single-step inference. It is essential to demonstrate that this strategy does not cause significant performance degradation on real-world benchmarks. Ablation study is only conducted in UDM10 (a small synthetic benchmark). Authors should conduct more analysis on large-scale real-world benchmarks.\n* Detailed investigation on model size is missing. This paper provides an appealing results by distilling a 0.6B student model from a 11B teacher model. However, the selection of model size requires further exploration. Authors need to find a boundary between visual quality and model size, and conduct an ablation study about it.\n* Motivation of using dual-domain discriminator. As mentioned in Sec 3.3, it replaces the conventional feat-domain discriminator by two discriminators in both pixel and feat domain. However, the motivation of adding one discrimintor in pixel domain remains unclear. Some related questions remain unresolved, such as why two discriminators are not used in the feature domain, and whether using more discriminators would improve performance. \n* Authors should also conduct experiments about temporal quality. By using 1D convs and a relative small model size, the temporal consistency of AdcVSR remians unclear now. Authors should conduct experiments by showing multiple frames of a single video in real-world benchmark.", "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)