Jue Gong

2026

1. arXiv

   LCUDiff: Latent Capacity Upgrade Diffusion for Faithful Human Body Restoration

   Jue Gong, Zihan Zhou, Jingkai Wang, Shu Li, Libo Liu, Jianliang Lan, and Yulun Zhang

   arXiv preprint arXiv:2602.04406, 2026

   Abs arXiv Bib PDF Code

   We propose LCUDiff, a stable one-step framework for human body restoration that upgrades latent diffusion from the 4-channel latent space to a 16-channel latent space. The method uses channel splitting distillation and prior-preserving adaptation to improve fidelity while keeping one-step efficiency, and further introduces a decoder router for per-sample decoder routing under diverse restoration conditions.

   @article{gong2026lcudiff,
     title = {{LCUDiff}: Latent Capacity Upgrade Diffusion for Faithful Human Body Restoration},
     status = {preprint},
     author = {Gong, Jue and Zhou, Zihan and Wang, Jingkai and Li, Shu and Liu, Libo and Lan, Jianliang and Zhang, Yulun},
     journal = {arXiv preprint arXiv:2602.04406},
     year = {2026},
   }

2. ICML

   Light Up Your Face: A Physically Consistent Dataset and Diffusion Model for Face Fill-Light Enhancement

   Jue Gong, Zihan Zhou, Jingkai Wang, Xiaohong Liu, Yulun Zhang, and Xiaokang Yang

   In Proceedings of the International Conference on Machine Learning (ICML), 2026

   Abs arXiv Bib PDF Code Website

   Light Up Your Face introduces a physically grounded dataset and one-step diffusion model for controllable face fill-light enhancement. It uses a 160K-pair dataset with 6D area-disk lighting control and physics-aware conditioning to improve face illumination while preserving identity and background consistency.

   @inproceedings{gong2026lightup,
     title = {{Light Up Your Face}: A Physically Consistent Dataset and Diffusion Model for Face Fill-Light Enhancement},
     status = {accepted},
     author = {Gong, Jue and Zhou, Zihan and Wang, Jingkai and Liu, Xiaohong and Zhang, Yulun and Yang, Xiaokang},
     booktitle = {Proceedings of the International Conference on Machine Learning},
     year = {2026},
   }

3. CVPRW

   The Fourth Challenge on Image Super-Resolution (x4) at NTIRE 2026: Benchmark Results and Method Overview

   Zheng Chen, Kai Liu, Jingkai Wang, Xianglong Yan, Jianze Li, Ziqing Zhang, Jue Gong, Jiatong Li, Lei Sun, Xiaoyang Liu, Radu Timofte, Yulun Zhang^*, and others

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2026

   Role: Challenge organizer

   Abs arXiv Bib PDF Code Website

   This organizer paper reports the NTIRE 2026 Image Super-Resolution (x4) challenge at CVPRW. It benchmarks bicubic x4 super-resolution across restoration and perceptual tracks, summarizes the datasets, metrics, results, and participant methods, and provides a unified view of recent progress in image super-resolution.

   @inproceedings{chen2026ntire_sr_challenge,
     title = {The Fourth Challenge on Image Super-Resolution (x4) at {NTIRE} 2026: Benchmark Results and Method Overview},
     status = {accepted},
     author = {Chen, Zheng and Liu, Kai and Wang, Jingkai and Yan, Xianglong and Li, Jianze and Zhang, Ziqing and Gong, Jue and Li, Jiatong and Sun, Lei and Liu, Xiaoyang and Timofte, Radu and Zhang, Yulun and others},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
     year = {2026},
   }

4. AAAI

   SODiff: Semantic-Oriented Diffusion Model for JPEG Compression Artifacts Removal

   Tingyu Yang^†, Jue Gong^†, Jinpei Guo, Wenbo Li, Yong Guo, and Yulun Zhang^*

   In The AAAI Conference on Artificial Intelligence (AAAI), 2026

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   SODiff improves JPEG artifact removal with semantic-oriented guidance for a pre-trained one-step diffusion model. It introduces a semantic-aligned image prompt extractor and a quality factor-aware time predictor to better handle varying compression levels.

   @inproceedings{yang2026sodiff,
     title = {{SODiff}: Semantic-Oriented Diffusion Model for {JPEG} Compression Artifacts Removal},
     status = {accepted},
     author = {Yang, Tingyu and Gong, Jue and Guo, Jinpei and Li, Wenbo and Guo, Yong and Zhang, Yulun},
     booktitle = {The AAAI Conference on Artificial Intelligence},
     year = {2026},
   }

2025

1. NeurIPS

   HAODiff: Human-Aware One-Step Diffusion via Dual-Prompt Guidance

   Jue Gong^*, Tingyu Yang^*, Jingkai Wang, Zheng Chen, Xing Liu, Hong Gu, Yulun Zhang^†, and Xiaokang Yang

   Advances in Neural Information Processing Systems (NeurIPS), 2025

   Abs arXiv Bib PDF Code

   HAODiff targets practical human-centric restoration under mixed degradation and human motion blur. It introduces triple-branch dual-prompt guidance and a dedicated MPII-Test benchmark, enabling stronger robustness and better visual quality in a single diffusion step.

   @article{gong2025haodiff,
     title = {{HAODiff}: Human-Aware One-Step Diffusion via Dual-Prompt Guidance},
     status = {accepted},
     author = {Gong, Jue and Yang, Tingyu and Wang, Jingkai and Chen, Zheng and Liu, Xing and Gu, Hong and Zhang, Yulun and Yang, Xiaokang},
     journal = {Advances in Neural Information Processing Systems},
     year = {2025},
   }

2. ICML

   Human Body Restoration with One-Step Diffusion Model and a New Benchmark

   Jue Gong^*, Jingkai Wang^*, Zheng Chen, Xing Liu, Hong Gu, Yulun Zhang^†, and Xiaokang Yang

   In Proceedings of the International Conference on Machine Learning (ICML), 2025

   Abs arXiv Bib PDF Code

   This work introduces the PERSONA benchmark for human body restoration and proposes OSDHuman, a one-step diffusion model guided by a high-fidelity image embedder. The method improves restoration quality while addressing the lack of large-scale, task-specific datasets for human-centric restoration.

   @inproceedings{gong2025osdhuman,
     title = {Human Body Restoration with One-Step Diffusion Model and a New Benchmark},
     status = {accepted},
     author = {Gong, Jue and Wang, Jingkai and Chen, Zheng and Liu, Xing and Gu, Hong and Zhang, Yulun and Yang, Xiaokang},
     booktitle = {Proceedings of the International Conference on Machine Learning},
     year = {2025},
   }

3. CVPRW

   NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results

   Zheng Chen, Jingkai Wang, Kai Liu, Jue Gong, Lei Sun, Zongwei Wu, Radu Timofte, Yulun Zhang^*, and others

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2025

   Role: Challenge organizer

   Abs arXiv Bib PDF Code Website

   This organizer paper reviews the NTIRE 2025 Real-World Face Restoration challenge at CVPRW. The challenge focuses on producing realistic face restoration results while preserving identity, using no-reference IQA metrics, FID, and AdaFace-based identity validation to rank participating methods.

   @inproceedings{chen2025ntire_face_challenge,
     title = {{NTIRE} 2025 Challenge on Real-World Face Restoration: Methods and Results},
     status = {published},
     author = {Chen, Zheng and Wang, Jingkai and Liu, Kai and Gong, Jue and Sun, Lei and Wu, Zongwei and Timofte, Radu and Zhang, Yulun and others},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
     year = {2025},
   }

4. CVPRW

   NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results

   Zheng Chen, Kai Liu, Jue Gong, Jingkai Wang, Lei Sun, Zongwei Wu, Radu Timofte, Yulun Zhang^*, and others

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2025

   Role: Challenge organizer

   Abs arXiv Bib PDF Code Website

   This organizer paper presents the NTIRE 2025 Image Super-Resolution (x4) challenge at CVPRW. It covers the bicubic x4 benchmark setting, restoration and perceptual tracks, evaluation protocol, final rankings, and method summaries from participating teams.

   @inproceedings{chen2025ntire_sr_challenge,
     title = {{NTIRE} 2025 Challenge on Image Super-Resolution (x4): Methods and Results},
     status = {published},
     author = {Chen, Zheng and Liu, Kai and Gong, Jue and Wang, Jingkai and Sun, Lei and Wu, Zongwei and Timofte, Radu and Zhang, Yulun and others},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
     year = {2025},
   }

5. CVPR

   OSDFace: One-Step Diffusion Model for Face Restoration

   Jingkai Wang^*, Jue Gong^*, Lin Zhang, Zheng Chen, Xing Liu, Hong Gu, Yutong Liu^†, Yulun Zhang^†, and Xiaokang Yang

   Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

   Abs arXiv Bib PDF Code

   OSDFace is a one-step diffusion model for face restoration that combines a visual representation embedder, identity-aware supervision, and GAN-based guidance. It improves realism and identity consistency while keeping inference efficient.

   @article{wang2025osdface,
     title = {{OSDFace}: One-Step Diffusion Model for Face Restoration},
     status = {accepted},
     author = {Wang, Jingkai and Gong, Jue and Zhang, Lin and Chen, Zheng and Liu, Xing and Gu, Hong and Liu, Yutong and Zhang, Yulun and Yang, Xiaokang},
     journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
     year = {2025},
   }