SimpleGVR: A Simple Baseline for Latent-Cascaded Generative Video Super-Resolution
Authors: Liangbin Xie
Yu Li
Shian Du
Menghan Xia
Xintao Wang
Fanghua Yu
Ziyan Chen
Pengfei Wan
Jiantao Zhou
Chao Dong
Venue: ICLR 2026 Poster (2026)
Keywords: High-resolution text-to-video generation; Generative Video Super-Resolution
Primary area: generative models
TL;DR: A Simple Baseline for Latent-Cascaded Generative Video Super-Resolution

Abstract: Cascaded pipelines, which use a base text-to-video (T2V) model for low-resolution content and a video super-resolution (VSR) model for high-resolution details, are a prevailing strategy for efficient video synthesis. However, current works suffer from two key limitations: an inefficient pixel-space interface that introduces non-trivial computational overhead, and mismatched degradation strategies that compromise the visual quality of AIGC content. To address these issues, we introduce SimpleGVR, a lightweight VSR model designed to operate entirely within the latent space. Key to SimpleGVR are a latent upsampler for effective, detail-preserving conditioning of the high-resolution synthesis, and two degradation strategies (flow-based and model-guided) to ensure better alignment with the upstream T2V model. To further enhance the performance and practical applicability of SimpleGVR, we introduce a set of crucial training optimizations: a detail-aware timestep sampler, a suitable noise augmentation range, and an efficient interleaving temporal unit mechanism for long-video handling. Extensive experiments demonstrate the superiority of our framework over existing methods, with ablation studies confirming the efficacy of each design. Our work establishes a simple yet effective baseline for cascaded video super-resolution generation, offering practical insights to guide future advancements in efficient cascaded systems. Video visual comparisons are available at https://simplegvr.github.io/.

Published as a conference paper at ICLR 2026
SIMPLEGVR: A SIMPLE BASELINE FOR LATENT- CASCADED GENERATIVE VIDEO SUPER-RESOLUTTE
Liangbin Xie
Yu Li
Shian Du
Menghan Xia
Xintao Wang
Fanghua Yu²
Ziyan Chen
Pengfei Wan
Jiantao Zhou
Chao Dong2.6
State Key Laboratory Internet Things for Smart City, University of Macau
Shenzhen Institutes of Advanced Technology. Chinese Academy of Sciences
TTinnhua University
*HUST
Kuaishou Technology
"Shenzhen University Advanced Technology
Pab for and mort
(1050p)
T2V (384x672) SimpkGVR
(384x6722)
Video
(e\SimpleGVR
Figure 1: Built upon the low-resolution latent outputs (e.g., 384 672 resolution) from the first-stage Large T2V model, SimpleGVR generates high-quality results that even surpass the 1080p outputs of the Large T2V model. Compared Flash Video, which also adopts cascaded architecture, SimpleGVR produces more realistic and finer details.
ABSTRACT
Cascaded pipelines, which use base text-to-video (T2V) model for low- resolution content and video super-resolution (VSR) model for high-resolution details, prevailing strategy for efficient video synthesis. However, current works suffer from two key limitations: an inefficient pixel-space interface that in- troduces non-trivial computational overhead, and mismatched degradation strate- gies that compromise the visual quality AIGC content. To address these issues, we introduce SimpleGVR, a lightweight VSR model designed operate entirely within the latent space. Key to SimpleGVR are a latent upsampler for effective, preeeiipr conditioning of the high-resolution synthesis, and two degrada- tion strategies (flow-based and model-guided) to ensure better alignment with the upstream T2V model. further enhance the performance and practical appli- cability of SimpleGVR, introduce set of crucial training optimizations: a detail-aware timestep sampler, suitable noise augmentation range, and an effi- cient interleaving temporal unit mechanism for long-video experiments demonstrate the superiority of our framework over cxisting methods, with ablation studies confirming the efficacy of each design. Our work establishes a simple yet effective baseline for cascaded video super-resolution generation, offering practical insights to guide future advancements in efficient cascaded sys- tems. Video visual comparisons are available here
video handling. Extensive
INTRODUCTION
Recent advancements (Chen et al. 2023b
Fridman et al. 2024 Voleti et al. 2022 B lattmann et al.,
2023 Yang et al. 2024b Polyak et al. Ma al. 2025 Kong 2024 Seawead et al. 2025 Wan
et al.
2025) in diffusion- based text- text-to-video (T2V) generation have markedly enhanced the visual
Work done during internship KwaiVGI, Kuaishou Technology. Corresponding authors

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Published as a conference paper at ICLR 2026
quality and coherence of synthesized videos. Leading models, such Hunyuan (Kong et al. 2024) and Wan (Wan et al. 2025), rely on large DiT backbones with full self-attention to fuse spatial, temporal, and textual cues, producing coherent clips with rich detail. However, their computational cost grows quadratically with spatial resolution: directly generating 1080p video in a single stage demands prohibitive computation and incurs long inference times.
To mitigate the substantial computational cost associated with generating high-resolution video, a prevailing and effective strategy is to adopt a cascaded generation pipeline, which first uses pow- erful base T2V model to create a low-resolution resolution capturing the core semantic content and motion, followed by a lightweight video super-resolution (VSR) model to synthesize fine- fine-grained details. Many works Zhhag et al.] [025a; Wang et al., 2025c Zhang et al.) (2025b) implement this strategy. However, we observe that these works treat the base and VSR models as two loosely- coupled components. They are merely a simple combination relying on an inefficient pixel-space interface: the base model's latent output is first decoded into a pixel-space video, upscaled via video-level interpolation, and subsequently re-encoded to serve as the input for the VSR model. These redundant VAE decoding and re-encoding steps introduce non-trivial computational overhead and increase inference time.
Beyond the architectural inefficiency, the performance of VSR models is limited by the mismatched degradation strategies. These models typically rely on either simple downsampling kernels (He et al., 2024) or more advanced two-stage degradation schemes (Chan et al.) 2022b Wang et al.
2025b Zhhnng et al.) 2025b). While the latter approach is effective for general video enhancement, is ill-suited for AIGC content. A model trained with such a scheme tends to generate severe artifacts or compromise depth perception when applied AIGC content from Large T2V models.
To address these two limitations, in this work, we propose SimpleGVR, a lightweight diffusion- based VSR model. supports direct operating on the low-resolution latent representations pro- duced by upstream T2V models, thereby eliminating redundant decoding and re-encoding steps. While channel concatenation a more efficient conditioning strategy than alternatives like Con- trolNet (He et al.] 2024) and token concatenation (Bai et al. 2025), the upsampling of the low- resolution (LR) latent via naive interpolation causes a loss of local detail. We therefore introduce a latent upsampler that preserves local structural integrity by first expanding the latent's channel and temporal dimensions, performing interpolation, and then reducing them. To address the second lim- itation of mismatched degradation strategies, we propose two methods designed to mimic the output characteristics of the upstream T2V model: (1) Flow- based degradation, where optical flow guides motion-aware color blending and adaptive blurring, and (2) Model-guided degradation, where noise is added to low-resolution video frames and partially denoised using the base T2V model. These strategies generate training pairs that better reflect the characteristies of base T2V model output.
Based on the architecture design and degradation strategies, we further optimize the training config- uration of SimpleGVR in three key aspects. First, we propose detail-aware timestep sampler that more effectively reconstructs high-frequency details compared to a uniform sampler. Second, we identify an optimal middle-range noise level (i.e., 0.3 ~ 0.6) for the low-resolution input augmen- tation, which strikes a better balance between detail enhancement and structural correction. Finally, to enable practical application on long videos (e.g., frames) under memory constraints, we intro- duce the interleaving temporal unit mechanism to extend the model from short to long sequences for both training and inference.
In summary, our main contributions are as follows: 1) We present SimpleGVR, lightweight diffusion-based VSR model that directly performs on the latent representations of large T2V mod- els. Compared to using single large T2V model to generate 1080p video end-to-end, the two-stage cascaded pipeline, which integrates SimpleGVR. achieves superior visual quality while reducing computational cost. 2) We investigate different LR latent injection schemes and introduce latent upsampler that effectively integrates information from low-resolution latents. 3) We design two degradation schemes, namely flow-based degradation and model-guided degradation synthesis, to simulate the degradation characteristics of the base model's outputs. This ensures better alignment between the VSR model and its upstream generator. 4) We present set of training configurations, including detail-aware sampler, a noise augmentation range, and the interleaving temporal unit, which improve the generative ability and practical applicability of SimpleGVR.
Here 1080p roughly corresponds a pixel area of about 14402.
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2 PRELIMINARY
Our work builds upon a pre-trained text-to-video foundation model that is composed of a 3D VAE (Kingma al., 2013b), T5 text encoder (Raffel al. 2020), and transformer-based la- tent diffusion module (DiT) CChn et al., 2023a Peebles & Xie 2023). The DiT module processes latent representations using blocks spatial self ouff-attetionn spatiotemporal attention, and text-guided cross-attention layers (conditioned on Cext), and we adopt the Rectified Flow framework (Esser et al., 2024 to define the linear path between clean latent 20 and noisy counterpart zy:
24 (1 - z) + te, e~ 10,
ordinary differential equation (ODE) governs the denoising process, mapping 21 back to 2p:
dz, = ue (zt, L, Crsa)dl
where the velocity field modeled by a neural network with parameters via During training, Conditional Flow Matching (CFM) (Lipman et al., 2022) is used to regress the velocity via the following objective:
[[[(s 20) vo(2e, t, Cress) 112]
CCFM M(,,
3 METHODOLOGY
Our cascaded video generation framework operates within a latent space defined by pre-trained VAE, The framework comprises core components: (i) A computationally intensive base Text-to- Video (T2V) model, which employs a Large DiT architecture to generate low-resolution video latent representations. (ii) diffusion-based VSR model, termed SimpleGVR, which adopts lightweight architecture to efficiently enhance the base model's output into high-resolution video latent repre- sentations and enhance the details. The overall framework structure is illustrated in Fig. As the primary focus of this paper, our method addresses the task of the latter component.
In the following sections, we first present the overview of SimpleGVR (Sec. 3.1 Then, we in- vestigate the low-resolution latent injection mechanism (Sec. 3.2). Subsequently, we describe the degradation simulation that generates training pairs (Sec. 3.3 Finally, we present three training con-
figurations to further improve the performance and practical applicability of SimpleGVR (Sec. 3.4
3.1
OVERVIEW OF SIMPLEGVR
Fig. illustrates the training pipeline of SimpleGVR. To optimize SimpleGVR, we adopt latent diffusion model. The high-resolution (HR) video THR E R N xW, and the corresponding low- resolution (LR) video FLR E R x3x H2 W2 are first encoded into latent representations via a 3D VAE (Kingma al. 2013a),, yielding HR 2q E R,2xx8xH, Wa and LR latents co R.N,x8xH, Here N, denotes the number of frames in clip, while H1 W, and H₂ x W2 are the spatial sizes of the high-resolution and low-resolution vidcos, roughly 14402 and 512?, respectively. We omit the batch dimension for simplicity. The VAE adopt downscales the spatio-temporal dimensions by x x 4, which means H3 = H1/8, HA Hz/8, and N₂ (N1 1)/4 + 1. To enable Vi directly process the low-resolution latent produced by the Large T2V model during inference (shown in Fig. (3). unlike Flash Video, the co is derived from the original LR video rather than upscaled LR video. Then, two independent random noises are then injected into both latents with different magnitudes, yielding noisy representations žN and Ct. že refers to the noisy latent in the diffusion process, while e denotes the noisy LR latent that serves as the conditioning input. To inject the information of C into we explore this in the following section.
3.2
LATENT INJECTION MECHANISM
As lightweight VSR model, SimpleGVR designed to enhance details while preserving the mo- tion, structure, and content provided by the conditioning latent e;- However, since e and the model's
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internal noisy latent 2, have different dimensions, effectively incorporating this conditional guid- ance a non-trivial design challenge. To address this, we explore several strategies for utilizing low-resolution latents and propose low-resolution latent injection mechanism.
Latent Interpolation + Channel Concatenation. As illustrated in Fig. 2(b), since the spatial di- mensions of c; and 2, differ, the most straightforward approach is to do the bilinear interpolation for Cr to match the dimensions of 2,, and then perform channel concatenation:
Tt patchify ([2,, (alinear(c)))chnnnee
(4)
where ERLIDD, L1 (N₂ H3 Ha)/4, D is the channel dimension.
Token Concatenation Self Attention Token Drop. Recent attempts (Tan et al. 2024 Bai al, 2025) incorporate the condition imput through token concatenation, thereby supporting condition inputs of arbitrary resolution. shown in Fig. c). 2n and e are patchified separately to obtain their respective tokens, which are then concatenated and fed into the self-attention module. Afterward, the tokens corresponding to e are dropped. yielding Iz:
2y drop(self.attee ((pttiaattttaattitttttt...
(patchityy 2e): patchff((e()))
(5)
where [patchify(21) )x): (atchify(c+) denotes the concatenation of patchified and e2 tokens.
Latent Upsampler Channel Concatenation (ours). To better preserve the layout and structural information of Ce2 we propose a latent upsampler that enlarges C₁ to the same size as and then injects the upsampled information into 2 via channel concatenation. Specifically, since e, contains compressed features produced by the encoder, we first expand its channel and temporal dimensions using two 3D residual blocks, followed by bilinear interpolation. We then employ another two residual blocks to reduce its temporal and channel dimensions back to match those of 2t.
Tt = patchifyy(2s ([+,, Res3D (es channel-dim)
(6)
where Res3D denotes the 3D residual block.
The architecture design of the latent upsampler is non-trivial. Its key component is the temporal expansion of the latent before spatial interpolation, which ensures that each frame in the expanded latent corresponds to frame in RGB space. This design prevents inter-frame signal aliasing during the spatial upscaling process. To verify the importance of temporal expansion, we conduct another baseline: "3D ResBlocks latent interpolation channel concatenation" for comparison. Fig. 10 illustrates the comparative results.
3D ResBlocks + Latent Interpolation + Channel Concatenation.Simill to the latent upsampler, we apply two 3D ResBlocks to expand the low-resolution latent only along the channel dimension before interpolation. After interpolation, two additional 3D ResBlocks were used reduce the latent dimensions, followed by channel concatenation with the high-resolution latent.
Comparison and Discussion. Our experiments show that incorporating into 2; via the proposed latent upsampler combined with channel concatenation achieves better semantic fidelity and layout consistency the final results, illustrated Fig. IO Compared to latent interpolation, the latent upsampler first projects ç₁ into higher-dimensionall space, enriching spatial and temporal details and enabling subsequent interpolation to more accurately blend structures and motion. In addi- tion, channel concatenation preserves layout information more effectively than single-layer token concatenation strategy.
Once trained, SimpleGVR can be directly applied to T2V generation pipeline, as shown in Fig, Specifically,, given random low-resolution gaussian noise cr, the large T2V model performs mul- tiple denoising steps to produce clean low-resolution latent cg- This latent is then perturbed with a fixed level random noise, and upsampled to yield e with the latent upsampler. Concurrently, a high-resolution gaussian noise sample is randomly initialized. The noisy high-resolution latent 2r and the conditioned latent e are concatenated along the channel dimension and fed into the DiT blocks of SimpleGVR. Notably, the conditioning latent remains fixed throughout the denoising process. After the iterative refinement, the final clean high-resolution latent 2n is decoded to obtain high-quality 1080p video.
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Now-Resolution Latent Injection Strategies
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Figure 2: Overview of SimpleGVR. Left: The training pipeline of SimpleGVR. To eliminate re- dundant decoding and re-encoding steps during inference, the latent G is not generated at the same spatial size as the high-resolution noisy latent 2, from the very beginning. Right: Comparison of different Low-resolution latent utilization strategies. (a) Latent upsampler and channel concatena- tion used in paper; (b) Latent interpolation and channel concatenation; (c) Token concatenation, self-attention and token drop.
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Cascaded high-resolution T2V pipeline. The large T2V model produces a low-resolution latent cp. which is upsampled by the latent upsampler in SimpleGVR and concatenated with ran- domly initialized 2r. The concatenated latent is iteratively denoised by DiT blocks to obtain 20, which is then decoded into the final 1080p video.
Figure 3:
3.3 DEGRADATION MODELING
3.3.1 FLOW-BASED DEGRADATION
Upon inspecting the base T2V outputs (see Fig. 4) we observe that unlike real-world low-quality videos, these video sequences do not exhibit severe degradations such severe blur, noise, or compression. Instead, they primarily exhibit two entangled, motion notion-dependent characteristics: (1) frame-to-frame color blending (where hues from previous frames smear into the current one) and (2) localized motion blur. As conventional degradation models (Chan et al.] 20226) cannot repli- cate these effects, design flow-based degradation strategy simulate these motion-dependent phenomena. The process driven by the motion field, which is estimated between adjacent frames using the DIS optical flow algorithm (Kroeger et al. 2016).
This motion field is then leveraged to synthesize both distortions. To simulate color blending, we identify regions of significant movement and introduce randomized elliptical patterns guide color sampling process. Hues from corresponding locations in the previous frame are then blended into the current frame with a distance-basee weighting, realistically mimicking the observed color smearing. For motion blur, the same motion field is used to generate adaptive, block-wise blur kernels. The parameters of each kernel (e.g., size and orientation) are determined by the local motion vectors. This ensures blur is only applied to moving regions and is aligned with the direction of motion, preserving the sharpness static areas.
3.3.2 MODEL-GUIDED DEGRADATION
The primary objective of SimpleGVR is to learn a mapping from the output domain of large T2V models to high-quality video data. By constructing paired training samples where the low-resolution
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Color blendlingg
Morion blur
Figure 4: Visual artifacts in decoded videos from the Large T2V model. Dynamic regions exhibit noticeable local motion blur and color blending distortions.
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3D VAE Ensoder
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Figure 5: Model-guided degradation synthesis pipeline. The parameter a controls the strength of the added Gaussian noise, which also affects the structural alignment between êy and co-
inputs are directly sourced from the T2V model outputs, SimpleGVR can be better aligned with the distribution and artifacts specific to the T2V model. Inspired by SDEdit (Meng et al.) [02T), as shown in Fig. 5 we begin by downsampling high-quality 1080p video to 512p and encoding it via a 3D VAE to obtain the latent co. This latent is blended with gaussian noise under a predefined ratio a, and the noisy latent is then partially denoised using the large T2V model to generate ép- A higher a pushes čg closer to the T2V distribution but weakens its structural alignment with the original video. To balance realism and fidelity, set [0.3, 0.4], ensuring that éu retains the overall layout of the source video while approximating the output domain of the Large T2V model.
3.4 TRAINING CONFIGURATION
To further enhance SimpleGVR's ability, we optimize the training configuration of SimpleGVR in three key aspects: the timestep sampling scheduling, the noise augmentation applied to the low- resolution (LR) branch, and the efficient training (i.e., interleaving temporal unit).
Table Quantitative comparison between the uniform sampler and the detail-aware sam- pler, demonstrating that the detail-aware sam- pler outperforms the uniform sampler in most metrics. These experiments are conducted on 17-frame inputs for 20K iterations.
DOVER
Sampler
MUSIQ
Technical
Overall
Figure 6: High-frequency variation curve over timesteps during inference.
Uniform
62.04
18.58
98.78
68.94
Detail-aware
62.19
18.92
98.83
69.64
Timestep Sampling Scheduling, Since SimpleGVR focuses on detail synthesis. understanding which timesteps contribute most to enhancing visual details during denoising is crucial. To this end, we analyze high-frequency detail changes the predicted 50 at each denoising step. Specifically, we sample 200 low-resolution 512p test videos and perform 50-steps inference using the SimpleGVR model trained with a uniform sampler. At each denoising timestep t, we obtain the latent 2, and directly predict its corresponding clean signal =p. To quantify the high-frequency content of sp, we apply discrete cosine transform (DCT) and extract its sighfrreuuecc coefficients H((2)). We then compute the pairwise differences of these high- gigh-freuunccy components across timesteps to derive the detail variation curve shown in Fig. 6 The figure shows that detail gains primarily occur in the high and mid-noise regions, while the low-noise region contributes minimally. Based on this

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Train 0.3-66
Train NA- 0.6-0.9
Jos NA 0.0
Test NA: 0.3
Test NA: 0.6
Inpuct IR
Test NA: 0.2
Test NA: 0.5
Test NA: 0.8
Figure 7: Visual results of SimpleGVR trained with different noise augmentation (NA) ranges.
observation, we propose a detail-aware sampler by normalizing this variation curve into a probability distribution. In the training phase, we derive different sampling probabilities for different time steps; in the inference phase, the sampling steps remain uniformly selected (e.g., 1000, 980, 960, .... 0) as in standard diffusion processes. As demonstrated in Table replacing the standard uniform sampler with our detail-aware version during training leads to improved performance.
Noise Augmentation Effect. The level of noise augmentation (NA) applied to the low-resolution latent critical hyperparameter, controlling the trade-off between structural fidelity to the input and the model's capacity for detail enhancement and correction. To identify suitable range for SimpleGVR, we conduct experiments with three intervals: small (0.0 ~ 0.3), middle (0.3 ~ 0.6), and large (0.6 ~ 0.9). As shown in Fig. large noise interval (0.6 ~ 0.9) causes the model to disregard the input's global structure, leading to significant divergence in shape and color (highlight in orange box). Conversely, small noise interval (0.0 0.3) limits the model's generative capacity- When the input contains fine-grained structural errors (shown in the red box), the model becomes faithful to this flawed input and fails to make corrections, preserving the messy details instead. Only the middle interval (0.3 ~ 0.6) strikes an effective balance. It empowers the model to correct localized, fine graiind structural errors while remaining the overall content and global structure of the input video.
Interleaving Temporal Unit. Processing long video sequences (e.g.. 77 frames) with full at- tention is often infeasible due to GPU memory constraints. We address this by first training SimpleGVR on short 17-frame clips and then extending its capabilities using our interleav- ing temporal unit mechanism. As illustrated in Fig. the long latent sequence is divided into smaller, efficient windows along the temporal dimension when fed into the transformer blocks I (where/ denotes the entire sequence of trans- former blocks). In even-numbered blocks 12* and 12k+2, the sequence is partitioned into four non-overlapping windows. In gumbeeeee blocks 12k+1, to enable information exchange across windows, the attention windows are shifted by half of their size along the temporal axis, following a Swin-style Liu al.) (202T) attention mechanism. This alternating scheme allows the model to perform efficient temporal attention while maintaining long-range dependencies.
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Figure 8: Visualization of the interleaving tempo- ral unit mechanism.
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
4.1.1 TRAINING DATASET
We design an automated filtering pipeline and collect approximately 840K high-quality video clips (each contains more than 77 frames) from the Internet construct our training dataset. Specifically, we first discard videos that are overly bright or dark. Then, for each video, we uniformly sample 10 frames and compute two metrics: the average MUSIQ score (Ke et al. (021) and the Laplacian variance, which reflects the level of spatial detail or sharpness. Videos with an average MUSIQ score below 40 or Laplacian variance below 30 are discarded.
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Table 2: Quantitative comparison on AIGCI00 dataset. Bold and underline indicate the best and
second best performance.
Melhol
MUSIQ
CLIPIQA
MANIQA
N(QU1)
Eaery x1P(
Incheccal
Acottctic
Cherall
Eacgeraazd Crecsnery
52aget Consarrryy
Qualits
Quality
SEARCE Streateee
Avctage
Vintancer
10.05
015013
0.31
5.311
3.340
15:36
12'10
03.51
91.94
01:03
59-96
66:33
06:18
$7.05
Upe Vidao
26 15
118784
0.9111
53114
4.314
12.03
108.3
59:01
$7.00
08:01
03:36
65.85
18:94
58.71
4.314
17331
03.14
08.04
SaxdVR (7B)
$6.77
0.517
04323
4.3055
3.200
18.05
07
61.87
54.90
03.50
63.02
00.40
95.51
$4.00
Seatvat2
51-11
1314
12.31
01.88
91.81
09331
0343
09-12
JGGLD
K2.10
015182
3377
12.00
13.03
9966
10145
DIoKAL
38.57
0,5973
0.301
43587
27064
14.33
07.61
55.35
95.84
9400
64.21
00.20
98.63
4.1.2 TESTING DATASET
Based on this output, we collect a dataset, AIGC100, which contains 100 video clips. To ensure the diversity of the test set, this dataset covers wide range of scenarios, including different sub- jects (e.g., humans and animals), various camera motions, and diverse backgrounds. More details of the AIGCI00 dataset can be found in the supplementary material. In addition, make more comprehensive evaluation, we construct VBenchl 10 by randomly selecting 10 prompts from each of VBench's Huang et 2024 11 categories. The of each clip is approximately 512".
4.1.3 METRICS
Since there is no ground-truth reference for the AIGCI00 and VBenchl 10 datasets, we adopt several no-reference metrics to evaluate both frame-level and video-level quality. Specifically, we employ MUSIQ (Ke et al. 2021 MANIQA Yang al. (022). CLIPIQA Wang et al. (2023) for single- frame perceptual quality, DOVER (Wu et al. 2023)) for overall video quality, and a suite of metrics from VBench (Huang et al. 2024) that
various aspects of AIGC videos, including back- ground consistency, subject consistency, aesthetic quality, imaging quality, and motion smoothness. Meanwhile, adopt the flow warping error E Eqaay Lai et. (2018), to assess temporal consistency.
4.1.4 TRAINING DETAILS
SimpleGVR is trained on 16 GPUs with a total batch size of 32. We use the Adam W opti- mizer (Loshchilov & Hutter 2017) with a learning rate of 10-5, and randomly replace the text prompt with null prompt in 10% of cases to enhance robustness. The training pipeline is di- vided into three stages. In the first stage, initialized from a pretrained IB T2V model, SimpleGVR is trained for 20K iterations 17-frame inputs using training pairs constructed via the degradation process in RealBasic (Chan et al. 2022b). In the second stage, we fine-tune the model for an additional 10K iterations the third stage, based on the dataset synthesized in the previous two stages, we continue fine-tuning SimpleGVe VR with 5K iterations and extend the temporal range 77 frames by using the interleaving temporal unit mechanism. During the whole training pipeline, we adopt the proposed detail-aware sampler, and the LR branch is injected with noise sampled from the range [0.3, 0.6).
dataset (30K) generated using the proposed degradation strategies. In
4.2 COMPARISON WITH SOTA METHODS
We compare SimpleGVR with existing state-of-the-art methods, RealBasicVSRR (Chan al. 2022b). Upscale-A- Video (Zhou et al. 2024), VEnhancer (He et al. 2024). MGLD, STAR (Xie al. 2025), SeedVR (Wang et al. 2025b). DiffvSR Li et al. (2025). MGLD Yang al. a024a)). DOVE Chen et al. (2025). DLoRAL Sun et al. (2025), SeedVR2 Wang et al. (2025a). FlashVides (Zhang et al. (025b). For fair comparison, we the inference steps of FiashVideo to 50. As shown in Table 2 SimpleGVR achieves the best performance on MUSIQ, MANIQA, CLIPIQA, and DOVER. Moreover, regarding the comprehensive metrics proposed in VBench, Sim- pleGVR also achieves the highest average score. For the temporal consistency metric Ewarg Sim- pleGVR also achieves competitive performance. Qualitative comparisons are presented in Fig. 9 Compared to other methods, for human faces, SimpleGVR produces finer and more realistic details. In contrast, other methods either struggle to generate sufficient detail or create noticeable artifacts. More visual comparisons can be found in the appendix and here
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RealBasicVSRR
(7B)
Oulpat Large T2V
"A young lady with long, curly brown hair
STAR
FlashVideo
SimpleGVR
Figure 9: Qualitative comparison AIGC100 dataset. Our SimpleGVR is capable of generating more realistic details than our methods. More visual comparisons can be seen the appendix.
(b) Lavent Interpolation Channel Concatenation
Token Concanicaation
(a) Input
(4) I Lteene Upsampler Channel Concstonsrion
(c) ResBlacks Interpolation
Concstemaioon
Figure 10: Ablation on low-resolution latent injection techniques. The proposed "latent upsampler channel concatenation" can better preserve the layout and semantic content of the original input.
4.3
ABLATION STUDY
4.3.1
ABLATION ON LOW-RESOLUTION LATENT INJECTION TECHNIQUES
To verify the effectiveness of our "latent upsampler + channel concatenation". we compare our "latent upsampler + channel concatenation" strategy with three alternatives: "latent interpolation channel concatenation", "token concatenation self-ateention token drop". "3D ResBlocks + latent interpolation channel concatenation" Both qualitative and quantitative comparisons, as presented in Fig. an Tab, g clearly demonstrate that our low-resolution latent injection technique achieves better performance. As shown in Fig. TO (highlighted with red circles), compared with "latent upsampler channel concatenation" approach, the other three alternative methods show less ability preserve the original input layout and may slightly deviate from its semantic content (e.g., an extra ear appears in Fig 10 (b), while unnatural artifacts on the tail in Fig 10 (c, e)). Although the variant "3D ResBlocks latent interpolation + channel concatenation" also employs additional 3D ResBlocks, performing only channel expansion does not sufficiently preserve the layout and semantic content of the latent.
4.3.2 EFFECTIVENESS OF DEGRADATION STRATEGIES
To ensure better alignment between the VSR model and its upstream generator, propose two degradation strategies. As shown in Tab. starting from a first-stage model trained on training pairs constructed with the degradation strategy of RealBasicVSR, we progressively incorporate data synthesized with our proposed degradation strategies into the training set for further training. The experimental results demonstrate the effectiveness of these two degradation strategies.

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Published as a conference paper at ICLR 2026
Table 3: Quantitative comparison between different low-resolution latent injection techniques.
These experiments are conducted under the setting of 17 input frames for comparison.
Festure
MUSIQ
Aeshetic
Consittacy
Quality
Imaging
Socadaass
Score
Teenzl Concirzattin
15.25
97:31
95:45
96.00
72.72
94130
KTT2
Tiken Corcatziatio
60.31
15
07.14
55.65
96.56
96.55
57.27
71.81
98.86
54.27
Uigaappeer Chanee Concatonacion (ours)
63.06
16.35
97.60
61.25
96.64
97.02
61.00
72.79
05:36
85.26
Restliecoo Lateni Inceppation + Charnel Concalenation
61.75
96.43
96.96
60.31
71.90
Table The effectiveness of our proposed degradation strategies. From top to bottom, we progres- sively add the synthesized paired dataset as part of the training set. These experiments are conducted under the setting of 17 input frames for comparison.
DOVER
Degradation Settings
MUSIQ
Technical
Aesthetic
Overail
Rackground Consistancy
Consistency
Subject
Acsthazic
Quality
Inaping
Quality
Smoothness
Meeion
Averag
Score
Degradation Chan al. 20220
62.06
16.25
91.60
61.25
96.64
97.02
61.49
72.79
98.36
85.26
Flow buree
61.89
18.21
97.72
63.41
96.57
97.03
61.70
75.11
98.
85.36
Model-guided Degrndation
18.92
73.37
98.78
4.4 COMPARISON IN T2V: END-TO-END VS. CASCADED
We also compare the performance two different T2V paradigms: a large T2V model that directly generates 1080p videos (i.e., end-to-end), versus a large T2V model that first produces 512p latent representations followed by the SimpleGVR module to generate 1080p outputs (i.e., cascaded). As shown in Tab. 5 the cascaded paradigm achieves better performance end-to-end paradigm. On other metrics that measure diverse aspects of videos (i.e., smoothness and consistency), the results under both paradigms are comparable. The visual comparison is here!
quality metrics than the
To validate that the cascaded approach reduces computational overhead, we measure the generation time for each paradigm. As our proposed low-resolution latent injection mechanism removes the intermediate decoding and re-encoding steps, we focus the comparison on the core DiT processing time. The comparison reveals substantial efficiency gain, with generation times of 950s for the end- to-end paradigm versus 283s for our approach. We note this result is achieved using 50 inference steps, a number with considerable redundancy that we aim to optimize in future work.
Table 5: Quantitative comparison between two different T2V paradigms on AIGC100 dataset.
Method
MUSIQ
Technical
Aesthotic
Overall
Background Consistency
Subject Consistency
Aestbetic Quality
Imaging Quality
Mation Smonthness
Average Score
Inference time (s)
Fad-to-Fad
56.77
18.82
97.27
62.32
96.04
98.16
63.45
67.69
98, 98 74
84.25
950
Cascaded
62.35
20.44
98.88
71.34
95.35
94.32
62.84
71,91
84.63
5 CONCLUSIONS
In this work, we propose SimpleGVR, lightweight video super- rupertresolution model that operates entirely in the latent space to eliminate the redundant decoding and re-encoding steps. To en- able effective conditioning within this latent-space framework, for derail-prsserving injection of low-resolution information. We further align SimpleGVR with its base generator through two AIGC-centric degradation strategies for synthesizing training pairs. Then SimpleGVR is optimized by suite of training configurations, including a detail-aware sam- pler, a suitable noise augmentation range and an efficient long-video mechanism, which enhance both generative quality and practical applicability. Experimental results demonstrate the superiority of SimpleGVR, providing an effective baseline for future research cascaded video synthesis.
introduce latent upsampler
ACKNOWLEDGMENT
This work was supported in part by Macau Science and Technology Development Fund un- der 001/2024/SKL, 0119/2024/RIB2, 11/02/25// IB2, and 0022/2022/A1; in part by Research Committee at University of Macau under MYRG- WWTRG-CRG2225000011 00086-FST; in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515012536; in part by RGC General Research Fund No. 12200725; part by the National Natural Science Foundation of China (Grant No. 62276251, 62502169).
and MYRG-GRG2025-
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Ethics statement. This work includes a study involving human subjects. All participants were informed of the study's 's purpose and provided consent prior to participation. The study design and procedures were conducted in a manner consistent with ethical standards to ensure the protection of participants' rights and privacy. In addition, as with any generative model, our method carries the risk of potential misuse. We emphasize that the system should be applied responsibly and urge caution to avoid malicious or harmful applications.
Reproducibility Statement. ensure the reproducibility of our work, we will ensure the follow- ing points. Code: Our code and model will be made publicly available, including necessary scripts. Data: Detailed descriptions of our data processing are provided in Sec. 4.1 Experimental Setup: We have stated all experimental configurations, including hyperparameters, hardware specifications in the Implementation Details of the main paper. Model Architecture: The architecture details are described in method part.
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A APPENDIX
In this appendix, we include the following:
Related work.
Effectiveness of degradation strategies.
Quantitative comparisons on the VBenchl 10 dataset.
User study.
More visual comparisons.
Details of the AIGCI00 dataset.
Performance real-world low-quality videos.
Performance on the output of other base T2V models (Wan (Wan et al. 2025 Cog VideoX (Yang et al. (2024b).
Limitation.
Discussions.
A.1
RELATED WORK
Cascade Diffusion Models. Cascade architectures have been widely explored in text-to-image, text-to-video and image-to-video generation (Li et al., 2023b Saharia et al. 2022 Zhang et al.) 20256 2023), where multi-stage designs are employed to address the challenge of generating high- resolution outputs. Typically, low-resolution sample is first generated, followed by specialized model to progressively refine details. Among these, Flash (Zhang et al.) 2025b) is most re- lated to our work. It begins second-stage generation from low-quality video inputs rather than pure guassian noise, enabling efficient high-resolution synthesis with only 4 function evaluations. How- ever, SimpleGVR differs in two key aspects. First, treat the low-resolution latent condition rather than directly using it as the input, allowing the model not only to leverage the coarse content contained in the low-resolution latent but also to flexibly correct its structural errors. Second, introduce two degradation strategies that explicitly simulate the characteristic degradations from the first-stage T2V generator.
Degradation Models in Restoration. Degradation modeling important for effective image and video restoration. Traditional models (Dong et al. 2014 2016 Gu et al. 2019) often rely simple assumptions like bicubic downsampling or gaussian blur, which fail to capture complex real-world degradations. Prior works such as BSRGAN (Zhang et al. 2222I) Real-ESRGAR (Wang et al.) 202I), and video-oriented methods like RealBasic VSR (Chan et al.] 2022b) simulate more com- plicated degradations, including blur, noise, and compression, improving robustness real-world low quality images and videos. However, these models are designed for real-world scenarios and do not account for the unique distortions in AIGC- -enerreee videos, such as motion blur and color blending. These AIGC-specific artifacts require specialized degradation modeling. To this end, we propose two degradation strategies: flow-based degradation scheme and model-guided degrada- tion scheme via SDEdit. Together, they enable the generation of training pairs that better mimic the output characteristics of the first-stage T2V generator.
Video Restoration. Early video restoration (VR) methods (Chan et al. 2021 2022a Chen al. 2024 Li et al., 2023a Liang et al.] 2022;) Wang et al. 2019: Youk et al. 2024; Xu et al. 2025 rely on synthetic data, limiting real-world performance. Later works (Chan et al.
a 2022b) Xie al. [2023] Zhang & Yao 2024) shift toward real scenarios but still struggle with texture realism. Diffusion- based approaches (He et al.) 2024, Wang et al., 2025c Li et al., 2025 Wang et al. 2025b Zhang al. 2025b Rota et al. 2024) leverage generative priors to achieve more realistic and coherent video restoration. However, all these methods require decoded RGB frames and cannot operate directly on latent representations, making them less suitable for T2V pipelines. In contrast, SimpleGVR performs upsampling and refinement directly in the latent space of the upstream gener- ator, enabling seamless integration with generative video models.
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22 Sewtrroe
Figure 11: Visualization of three consecutive frames generated by SimpleGVR. "w/o degradation FT" indicates that SimpleGVR trained only with the conventional degradation(Chan et 2022b), without fine-tuning using the proposed degradation strategies.
A.2 EFFECTIVENESS OF DEGRADATION STRATEGIES
In Fig. [ demonstrate the effectiveness of the proposed degradation strategies, As shown in the first and third rows, SimpleGVR trained solely with the conventional degradation(Chan et al. 2022b) exhibits noticeable temporal inconsistencies in motion areas, such as the panda's paw, and suffers from color blending artifacts, particularly evident in the human arm. After fine-tuning with training pairs generated by the two proposed degradation schemes, SimpleGVR effectively mitigates abrupt changes in motion regions across consecutive frames and can eliminate color blending distortions.
A.3 MORE QUANTITATIVE COMPARISONS
As we mentioned in the main paper, to make more comprehensive evaluation, we also construct dataset, VBenchl 10, by randomly selecting 10 prompts from each of VBench's (Huang et al.) 2024) 1l categories. The quantitative comparison of different methods on this dataset is shown Tab, 6 It can be observed that SeedVR achieves the best performance on the MUSIQ metric. For other metrics, such as DOVER and the VBench average score, SimpleGVR attains the best results.
Table 6: Quantitative comparison on VBenchl 10 dataset. Bold and underline indicate the best and second best performance.
DOVER
Vbench
Method
MUSIQ
Technical
Acsthetic
Overall
Background Consistency
Subject Consistency
Assibetic
Quality
Imaging Quality
Motion Smaothness
Average
RealBascVVS
$7.22
11:30
98.41
$8.87
94.33
93.26
61.79
72.71
98.56
83.25
VEnhancer
48.07
14.29
98.35
62.14
95, 14
95.43
63.71
68.19
84.36
Upscale-A-Ve
38.90
10.13
98.16
55.23
96.88
95.65
64.46
66.90
99.28
84.63
STAR
$3.07
15.97
98.51
64.95
96.44
95.54
64.72
70.62
99.17
85. 30
Flashvideo
98.35
96.06
84.45
SesdVR (7B)
61.64
15.14
97.14
58.18
95.57
95.23
66.04
71.18
98.71
Ours
60.20
16.50
98..3
65.71
95.86
93.34
65.05
71.98
99.02
85.45
A.4 USER STUDY
Considering that existing IQA metrics (Ke et al. 2021 Huang et al. 2024 Wu et al. 2023 cannot fully capture perceptual performance. further conduct
study. Specifically, randomly select low-resolution clips from AIGC100 and VBe For each clip, we obtain the re- sults of RealBasicVVR (Chan et al. 2022b), VEnhancer (He al. 2024). STAR (Xie al. 2025
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Flash Video (Zhang et al. 2025b), SeedVR (Wang et al. 2025b) and SimpleGVR, and randomly shuffle the order of these method results. For each set clips, we ask participants to independently select the two best videos. The first one is the best video with the highest visual quality (i.e., Detail Quality), and the second one is the video with the best temporal consistency or lowest temporal flickering (i.e., Temporal Consistency). The result is shown in Fig. [2] We can find that over 50% of participants prefer the details generated by SimpleGVR. Mcanwhile, more than 43% of partic- ipants consider that SimpleGVR provides better temporal consistency. This further suggests that SimpleGVR is superior to other methods.
Detail Quality
Temporal Consistency
RealBasieVSR
RealBusie
Venhancer
Venhancer
8.00%
8.00%
STAR Vidso
STAR
VinshVideo
6.67%
SimpleGVR 43.10%
14.33%
SeedVR
SimpleGVR 50.67%
SimpleGVR
SimpleGVR
SecdVR 25.00%
SeodVR 22.67%
Figure 12:
The results of user studies, comparing the results generated by RealBasieVSR, VEn- hancer, STAR, Video, SeedVR, and SimpleGVR.
To further compare SimpleGVR with more recent representative approache (i.e., MGLD Yang al. (2024a), Dif Li al. (025), DOVE Chen et al. (2025), DLoRAL [Sun al. (2025) and Seed R2 Wang et al. (025a)), conduct an additional user study. This study follows setting similar to the one described above. As shown Fig. 13 the outputs SimpleGVR are consistently
preferred over those of the other methods.
Detail Quality
Temporal Consistency
MGLD
MGLD
DifTVSR
DiffVSR
15.60%
DOVE
9.00%
DOVE
DLoRAL
DLoRAL
SimpleGVR 40.40%
ScodVR2
SImpleGVR 45.00%
9.67%
SeedVR2
12.67%
SimpleGVR
SimpleGVR
11.33%
11.67%
SeadVR2 R2
SecdVR2 18.00%
16.33%
Figure 13: The results of user studies, comparing the results generated by MGLD, DifTVSR, DOVE, DLORAL, SeedVR2, and SimpleGVR.
A.5 MORE VISUAL COMPARISONS
Fig. 14 presents additional visual comparisons between SimpleGVR and other VSR methods. From the first group of results, it can be observed that, compared with other approaches, SimpleGVR exhibits stronger generative capability, successfully restoring the previously distorted guitar string from the upstream T2V model to a perfectly straight state. Besides, for animal fur, SimpleGVR is also able to generate richer and finer details. Please refer here for better visualization.
A.6 DETAILS OF THE AIGC100 DATASET.
AIGC100 consists of 100 low-resolution videos generated by the base T2V model. As shown in Fig. 15 we present the category composition and motion-intensity distribution of the AIGC100
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Published as a conference paper at ICLR 2026
VSa
VEnhancer
ScodVR (7B)
"A giant punda with soft, fluffy far and gentle demeanor is sitting on a wooden dock
STAR
FlashVideo
SimpleGVR
ReaIBasicVSR
VEnhancer
ScodVR (7B)
"A close-up view of a cute little white chick wearing blank-framed glasses perched on the end
FlashVideo
SimpleGVR
RealBasieVSR
VEnhancer
SeedVR (7B)
"Two majestic tigers, their vibrant orange and black stripes glistening in the sunlight, are sitting at
STAR
FlashVideo
SimpleGVR
RealBasicVSR
VEnhancer
SeedVR (7B)
"A stunning 18-yeur-old woman with a delicate melon-seed face..."
STAR
FlashVideo
SimpleGVR
Figure 14: Qualitative comparisons with state-of-the- the- methods. Our SimpleGVR is capable of generating more realistic details than our methods.
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Published as a conference paper at ICLR 2026
dataset. It encompasses wide variety of semantic categories and spans broad spectrum of motion intensities, providing a comprchensive benchmark for evaluating model performance.
48
Nemkes
19
Adkil
Lut Lac.
164
Sanay
Hana Calegyy
Acilacaa
Soit
Dudk Sucie
08.
8-1
1-2
2-3
3-4
Motion Interaly
Figure 15: Statistical analysis of categories and motion in AIGCI00
A.7 PERFORMANCE ON REAL-WORLLD LOW- LO-QUALIITY VIDEOS
The motivation proposing SimpleGVR to do computational decomposition high-resolution T2V tasks. As such, SimpleGVR is lightweight, and its components specifically tailored to chosen T2V model. Evaluating its generalization to other datasets is therefore beyond the primary focus of our work.
We evaluate the performance of SimpleGVR on real-world low-quality videos from VideoLQ L Chan al. 2022b), and the visual results are shown in Fig. [6 It can be observed that SimpleGVR capable of removing certain degradations and recovering some details.
LQ
SimpleGVR
Figure 16: Qualitative results of SimpleGVR on the VideoL. dataset.
A.8 PERFORMANCE ON THE OUTPUT OF OTHER BASE T2V MODEL
Under the computational decoupling concept, SimpleGVR is proposed to play the role of cooperat- ing with specific base T2V model, instead of targeting for general video super-resolution task.
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Published as a conference paper at ICLR 2026
Table 7: Quantitative results of applying the pretrained SimpleGVR low-resolution oulputs gen- erated by the Wan model.
Vitrach Mortic
NIgIU)
Sagjer
Feeiorery
Qualiyy
Quallts
Scom
Waa 14a-145-500 4502
64:20
0.363
4,456
0.555
13:34
15,30
35.00
15.35
15:24
02.33
16:35
58.35
$4.25
Waa 145- 220
68.55
4,320
0411
17:87
92.18
60.3 73
05.04
62.89
08.11
96.57
$3.35
Wis SinpleGVR
Table 8: Quantitative results of applying the pretrained SimpleGVR to low-resolution outputs gen- erated by the Cog VideoX model.
HOVER
Vilech Metiic
Mathed
MUSSK
MANAJA
CLIFIAA
Bacogrosss
STA233
MMoonn 97.01
Anurage
CogMMieoN SB 430
66.3907
0.3350
6.301
03300
9:60
95.00
45.37
05.33
94.95
59.25
62.15
$1.3
111319
27311
The rational lies that we require the tailor-made SimpleGVR to be as efficient as possible, so its model capacity is relatively lightweight, which is impractical expected tackle general low- resolution video pattern. Anyway, the approach of SimpleGVR itself is general to any T2V base model, except that we need to adjust some aaperpparaeetes of flow-based degradation for training data preparation.
As an interesting extended study, we directly apply our trained SimpleGVR to the low-resolution puts (i.e., 480p) generated by Wan-14B (Wan et al.) 2025) and upscale them to 1080p high-resolution videos. The quantitative results are shown in Tab. 7 On most quantitative metrics, Wan- 140-144 SimpleGVR achieves better performance, indicating that SimpleGVR can generalize other base T2V models to some extent. As illustrated in Fig. SimpleGVR is able to enhance details for low-resolution inputs generated by Wan-14BB
We also conduct corresponding experiments on CogVideoX (Yang et al. 2024b). Specifically, we apply SimpleGVR directly the 480x720 videos generated by Cog VidcoX-BBB producing 1080p outputs. The quantitative results are shown in Tab. [ On most evaluation metries, the combination of CCooVVieeX444007777
SimpleGVR" achieves better performance. This observation is consistent with our findings on Wan- Wan-14B. As illustrated in Fig. [8 SimpleG is also capable of enhancing fine details for the low-resolution inputs generated by CogVideXX-55
480p input from Wan-14B
1080p result with SimpleGVR
Figure 17: Qualitative results of SimpleGVR on the low-resolution output from Wan-14p
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Published as a conference paper at ICLR 2026
480x720 imput from Cog VidexX
1080p result SimpleGVR
Figure 18: Qualitative results of SimpleGVR on the low-resolution output from Vigee..
A.9 LIMITATION
As SimpleGVR lightweight and primarily focusing on detail enhancement, it relies on the motion and overall structure of the low-resolution latent. Therefore, if the low-resolution latent misaligned with the prompt or exhibits severe motion degradation, such issues will inevitably propagate to the final high-resolution video.
A.10 DISCUSSIONS
Why local blur and color blending artifacts appear? It is common to motion blur in real- world videos, which as training data, is challenging for T2V model to synthesis, especially when compressed by temporal VAE. So, the high-dynamic regions of generated video tend to suffer from abnormal blur and color blending.
For high-resolution T2V generation target, why is fidelity to the low-resolution latent impor- tant? The aim of our proposed two-stage high-resolution T2V framework is for computation de- coupling. As the modeling of motion and structure is complex and requires large capacity, it is completed by the large base T2V model and inherited by SimpleGVR. Relaxing this fidelity con- straint would challenge the lightweight SimpleGVR model to synthesize reasonable motion, struc- ture, thereby obeying the purpose of decoupling. However, at minor structure/details level, Sim- pleGVR still has certain freedom to generate natural details. This fidelity&geerration balance can be controlled by the noise added to the low-resolution latent.
B THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, we utilized large language models (LLMs) exclusively for the purpose of grammar checking and text polishing. Specifically, LLMs were employed to assist in enhancing the clarity, coherence, and readability of the text. by identifying and correcting grammatical errors, improving sentence structure, and refining language usage. These models were not involved in any aspect of the research ideation, data analysis, experimental design, any other stages of the research process. The content, ideas, and conclusions presented this work are solely the result of the authors' intellectual contributions.
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Reviews:
[{"review_id": "RQ1ufACugz", "reviewer": "Reviewer_tr3b", "summary": "This paper proposed a diffusion-based latent video super resolution model for T2V model outputs. It first inject the low-res latent into VSR model by a proposed latent upsampler and channel concatenation operations. To mitigate the gap between synthetic low-res latent and real T2V model output latents, the author proposed two degradation schemes, i.e., flow-based and model-guided degradation. Finally it also introduce multiple training receipts for improved performance, controllable generative details and long video super resolution.", "questions": "- For the proposed low-res latent injection method, it first increase the channel and spatial/time resolution and reduce them in the end. What is the rationale behind it? And is there any ablation about any simpler designs like directly increasing the channel and spatial/time resolution to match the high-res latents?\n- How is the proposed detail-aware sampler implemented in details? Is it the shifted/warped timestep toward high noise level sampler?\n- How to resolve the memory consumption when dealing with mulitple video chunk for each transformer layers? Are all intermediate results cached while computing so that they can be shifted high window?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- VSR directly performs on latent space, instead of introducing decoding and re-encoding from lossy and compute heavy VAE.\n- There are comprehensive low-res latent injection methods and discussion, which gives a better understanding of the best low-res to high-res latent mapping.\n- It proposed flow-based and model-guided degradation schemes to simulate the degradation characteristics of the T2V base model outputs.\n- The detail-aware timestep sampler, optimal noise augmentation range, and interleaving temporal unit mechanism enhance the model generalization ability and robustness.", "weaknesses": "- The overall model design is specific to the base T2V model used. Specifically, the design of flow-based degradation is used to mimic the artifacts from the specific base model. It would be helpful to better understand the generalization ability of the proposed model on other baseline T2V models.\n- The flow-based degradation though novel, may introduce multiple stages like optical flow estimation, color blending, distance-based weighting, etc. This can bring complexity of the pipeline and is likely prone to error.", "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": "dxWZDCKpIt", "reviewer": "Reviewer_D3ZN", "summary": "The paper aims to address the heavy computational cost and suboptimal visual quality of AIGC videos. It proposes a method named SimpleGVR, and designs explicit degradation pipelines to match the distribution of AIGC videos. Compared to existing VSR methods, it is interesting to consider the VSR problem within the paradigm of video generation.", "questions": "(1)\tA large portion of the paper, including Figure 2, is devoted to describing the combination of LR and GT features. However, the conclusion drawn from this design seems rather intuitive, since the latent upsampler introduces additional 3D ResBlocks compared to latent interpolation. This part feels redundant and could be streamlined for clarity.\n\n(2)\tFigure 8 is hard to understand. The meaning of symbol “l” is not explained and annotations of “2k/2k+1/2k+2” are ambiguous. And also, what does “shift window” mean exactly?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "(1)\tThe observations of “video generation + vsr” pipeline are interesting. There are two key observations, architectural redundancy and degradation misalignment. Both of them are key to high-quality video generation.\n\n(2)\tThe synthesis pipeline of LR videos is quite reasonable, since it considers the distribution of T2V model.", "weaknesses": "(1)\tThe motivation for the two synthetic degradations is unclear. Figure 4, used to justify common degradations in AIGC videos, is unconvincing. For example, the color blending example, which is very similar to natural lighting or shadow variation and seems to have little impact on video quality. Providing clearer, more representative cases where these degradations noticeably degrade perceptual quality would strengthen the rationale for the subsequent degradation design.\n\n(2)\tThe evaluation is limited in both dataset and metrics. The paper adopts AIGC100 as the testing dataset, which aligns with the goal of enhancing AIGC videos but lacks diversity to verify the method’s generalization. Since AIGC100 contains AI-generated videos whose degradations differ from those in most compared models’ training data, it is unclear whether the improvement comes from the model itself or from the matched degradation distribution. Additional results on general datasets (e.g., VideoLQ) are suggested. As for evaluation metrics, besides MUSIQ and DOVER, it would be more convincing to include CLIPIQA, MANIQA, and Warping Error for a more comprehensive quality assessment.\n\n(3)\tThe comparison methods are also limited. It would strengthen the experimental evaluation to include more recent or representative approaches, such as MGLD, DLoRAL, Dove, and SeedVR2, to provide a more comprehensive and convincing performance comparison.", "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": "WR2ZKIhmKn", "reviewer": "Reviewer_ExS4", "summary": "This paper introduces SimpleGVR, a lightweight video super-resolution method designed for cascaded text-to-video generation systems. The approach performs super-resolution directly in the latent space, avoiding redundant encoding and decoding operations in pixel space. A latent upsampler is used to integrate low-resolution latent features, and a multi-step diffusion denoising process generates high-resolution latent representations. The authors further propose two degradation modeling strategies tailored to the characteristics of generative video content, along with several training techniques to enhance detail quality and temporal consistency. Experiments show that SimpleGVR achieves higher efficiency and comparable visual quality to existing multi-stage cascaded methods on their constructed datasets.", "questions": "1.The paper states that injecting noise into the low-resolution branch helps the model correct structural errors and recover fine details, but the underlying mechanism is not well explained. It is recommended that the authors clarify whether the noise distribution is consistent between training and inference, and whether any mismatch could affect model stability. The paper also claims that the model can “correct structural errors,” but the limits of this ability are unclear. Current experiments only show mild distortions; more extreme cases would better demonstrate robustness.\n\n2.In the degradation modeling section, the flow-based degradation uses optical flow and blur to mimic artifacts observed in generated videos, yet it remains unclear whether these synthetic artifacts truly resemble those of real T2V outputs. The authors are encouraged to provide quantitative comparisons, such as spectral statistics or color-blending analysis. In the model-guided degradation strategy, where the upstream generator is involved in synthesizing training data, there is a risk that SimpleGVR might merely learn the biases of the teacher model rather than general restoration capability. A cross-model experiment, such as training on data from model A and testing on model B, would help evaluate generalization.\n\n3.The AIGC100 test set is manually curated by the authors, but the selection criteria and data distribution are not disclosed. It is recommended to report the category composition, motion intensity distribution, or include evaluations on public benchmarks to enhance credibility.\n\n4.The evaluation currently relies solely on no-reference metrics such as MUSIQ, DOVER, and VBench, without subjective assessment or explicit temporal consistency measures. Incorporating human perceptual scores (e.g., MOS) or motion-consistency metrics would provide a more comprehensive evaluation of video quality.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.The overall structure and motivation of the paper are reasonable. Performing video super-resolution in the latent space effectively avoids redundant VAE encoding and decoding, thereby reducing computational overhead. This idea is well justified within the cascaded text-to-video generation framework.\n\n2.The experimental design includes ablation studies and hyperparameter analysis, which systematically demonstrate the effects of different components such as degradation modeling, latent injection strategies, and timestep sampling methods.", "weaknesses": "1.The work exhibits limited originality. The core idea of latent-space video super-resolution closely resembles prior studies such as FlashVideo, LaVie, and SeedVR. The proposed latent upsampler, channel concatenation, noise range setting, and timestep sampling strategy are largely incremental refinements or empirical combinations of existing approaches, lacking substantial algorithmic or theoretical innovation.\n\n2.The experiments are conducted on the authors’ self-constructed AIGC100 and VBench110 datasets, which are not publicly available, making it difficult to assess fairness and reproducibility. The comparison settings are not fully aligned, as details about the base T2V model, training data, and inference steps are not clearly specified.\n\n3.The paper does not compare with stronger diffusion-based VSR methods such as DiffVSR or VideoGigaGAN, nor does it evaluate whether the proposed model generalizes to different upstream T2V generators such as CogVideoX.", "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": "aujFywj1ti", "reviewer": "Reviewer_w3HT", "summary": "The paper proposes an algorithm, SimpleGVR, for Video Super-Resolution (VSR) tailored for AIGC videos. SimpleGVR enhances the resolution of a generated video by operating directly in the latent space. The main contributions of the paper includes: (a) A simple, lightweight yet effective model for VSR for videos generated by latent diffusion models, (b) New degradation strategies to simulate low-resolution synthetic videos, and (c) new sampling scheduler and noise augmentation for improving the low-res latent codes.", "questions": "- Although only trained for AIGC videos, will SimpleGVR work for generic low-res videos by encoding real low-res videos into the latent space?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "### Motivation\n- Studying on efficient high-resolution video generation is critical for efficient video generation. \n\n### Method\n- The paper shows a simple yet efficient pipeline for AIGC video enhancement.\n- The paper shares interesting insights into degradation for AIGC videos and a new framework for upsampling latent codes.\n\n### Experimental results \n- The authors show extensive experimental evaluations, especially their model designs. \n- SimpleGVR outperforms many baselines over common benchmarks. \n- SimpleGVR enables long-clip training/inference (77 frames) under limited GPUs. \n\n### Writing/Presentation\nThe paper is well-written and easy-to-follow.", "weaknesses": "### Motivation\n- Although reasonable and intuitive, the core idea (cascaded pipeline) is not new and exists in many video generation papers, such as Imagen-video from Google. \n\n### Experimental results \n- Temporal consistency is not well evaluated. Vbench metrics shown in the paper cannot capture temporal inconsistency.\n- As many no-reference visual assessment metrics are not convincing, it is highly recommended a user study is conducted for more direct comparison.", "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)