This paper provides a review of the NTIRE 2026 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on generating natural and realistic outputs while maintaining identity consistency. Its goal is to advance state-of-the-art solutions for perceptual quality and realism, without imposing constraints on computational resources or training data. Performance is evaluated using a weighted image quality assessment (IQA) score and employs the AdaFace model as an identity checker. The competition attracted 96 registrants, with 10 teams submitting valid models; ultimately, 9 teams achieved valid scores in the final ranking. This collaborative effort advances the performance of real-world face restoration while offering an in-depth overview of the latest trends in the field.
Figures & tables
Figure 1: MiPlusCV adopts a two-stage pipeline that combines OSDFace-based coarse restoration with a Z-Image-based one-step detail enhancement stage.
Figure 2: Overview of the CEVI-KLETech pipeline. A semantic-aware wavelet correction block is inserted between the diffusion and naturalness stages.
Figure 3: Overview of the HONORAICamera pipeline.
Figure 4: YuFans combines a one-step SDFace restoration stage with CLIPIQA-guided pixel optimization at test time.
Figure 5: Overview of DeSC-Face. The degraded image is encoded into degraded latent tokens, which are used both as the main condition for the LoRA-adapted FLUX.2 backbone and as the input to the structured control branch. The scene-token stream is iteratively restored and then decoded into the final output image.
Figure 6: Architecture diagram of the DiffBIR v2.1 two-stage pipeline used by NTR. Stage 1 removes degradations with SwinIR, and Stage 2 synthesizes facial textures with IRControlNet conditioned on Stable Diffusion 2.1.
Figure 7: Overall architecture and training objective of MaDENN. The baseline CodeFormer architecture is extended with identity-preserving and ROI-aware supervision, while low-quality inputs are synthesized with a second-order Real-ESRGAN degradation pipeline.
Figure 9: The workflow of PRIDE-Face. GFPGAN provides the structural prior in the first stage, while DiffBIR synthesizes high-fidelity details in the second stage.
Figure 10: The BVI extends TADSR with a residual noise refiner and a detail-aware training strategy inside the one-step student branch.
This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and results. The challenge seeks to identify robust reconstruction pipelines that are robust under real-world adverse conditions, specifically extreme low-light and smoke-degraded environments, as captured by our RealX3D benchmark. A total of 279 participants registered for the competition, of whom 33 teams submitted valid results. We thoroughly evaluate the submitted approaches against state-of-the-art baselines, revealing significant progress in 3D reconstruction under adverse conditions. Our analysis highlights shared design principles among top-performing methods and provides insights into effective strategies for handling 3D scene degradation.
This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provides a common benchmark for evaluating the restoration accuracy, robustness, and generalization capability of models across multiple degradation categories within a unified framework. The competition attracted 158 registered participants, and 20 teams were included in the final ranking after their submitted results were successfully reproduced and verified. This report provides a comprehensive analysis of the submitted solutions and corresponding results, highlighting recent advances in real-world all-in-one image restoration. The summarized methods and empirical findings reveal effective design strategies and establish an updated benchmark for future research in real-world low-level vision.
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provided a unified benchmark to evaluate the robustness and generalization ability of restoration models across multiple degradation categories within a common framework. The competition attracted 124 registered participants and received 9 valid final submissions with corresponding fact sheets, significantly contributing to the progress of real-world all-in-one image restoration. This report provides a detailed analysis of the submitted methods and corresponding results, emphasizing recent progress in unified real-world image restoration. The analysis highlights effective approaches and establishes a benchmark for future research in real-world low-level vision.