This paper presents a review of the LoViF 2026 Challenge on Weather Removal in Videos. The challenge encourages the development of methods for restoring clean videos from inputs degraded by adverse weather conditions such as rain and snow, with an emphasis on achieving visually plausible and temporally consistent results while preserving scene structure and motion dynamics. To support this task, we introduce a new short-form WRV dataset tailored for video weather removal. It consists of 18 videos 1,216 synthesized frames paired with 1,216 real-world ground-truth frames at a resolution of 832 x 480, and is split into training, validation, and test sets with a ratio of 1:1:1. The goal of this challenge is to advance robust and realistic video restoration under real-world weather conditions, with evaluation protocols that jointly consider fidelity and perceptual quality. The challenge attracted 37 participants and received 5 valid final submissions with corresponding fact sheets, contributing to progress in weather removal for videos. The project is publicly available at https://www.codabench.org/competitions/13462/.
Figures & tables
Rank
Team
Final Score ↑
PSNR ↑
SSIM ↑
LPIPS ↓
Warp Error ↓
1
RedMediaTech
16.21
14.751
0.554
0.624
0.032
2
tremendous
15.54
14.720
0.533
0.688
0.036
3
Guangong Perception
15.11
14.206
0.531
0.613
0.045
4
quadrillion
14.68
13.775
0.524
0.712
0.026
5
CUIT_Team
14.54
13.539
0.527
0.673
0.030
Table 1: Results of the LoViF 2026 Challenge on Weather Removal in Videos. Teams are ranked by Final Score. Best results in each metric are highlighted in bold . ↑ indicates higher is better, ↓ indicates lower is better.
Figure 1: Visual comparison on a rainy scene. From left to right, top to bottom: Ground Truth, RedMediaTech, tremendous, Guangong Perception, quadrillion, and CUIT_Team.
Figure 2: Visual comparison on a snowy scene. From left to right, top to bottom: Ground Truth, RedMediaTech, tremendous, Guangong Perception, quadrillion, and CUIT_Team.
Figure 3: Overview of the RedMediaTech method. Stage 1: the DiT backbone is pre-trained on FoundIR with MSE and LPIPS losses. Stage 2: the DiT is fine-tuned on the competition data using LoRA with MSE loss. The VAE remains frozen throughout.
Figure 4: Architecture of the TDyConv module proposed by team tremendous. A Transformer encoder generates kernel attention maps that dynamically modulate depthwise convolution weights, enabling content-adaptive processing of heterogeneous weather degradations.
Figure 5: Overall framework of VP-AdaIR proposed by team Guangong Perception. The method takes a degraded video clip as input, extracts a three-channel temporal prior map (background, artifact, and temporal stability cues), and injects it into the AdaIR backbone through prior-guided fusion.
Figure 6: Overall architecture of EF 3 Net proposed by team quadrillion. A Siamese Historyless Encoder independently processes previous and current frames. A Causal History Model (CHM) dynamically fuses temporal memory via Key/Value caching at the bottleneck.
Figure 7: Overall architecture of VD-Diff proposed by CUIT_Team. The framework consists of a conditional diffusion model for implicit prior generation and a wavelet-decomposition-based restoration backbone with Adaptive Window Self-Attention (AWSA), Adaptive Channel Self-Attention (ACSA), and bidirectional temporal propagation.