LiveMoments: Reselected Key Photo Restoration in Live Photos via Reference-guided Diffusion

Clara Xue*, Zizheng Yan*, Zhenning Shi, Yuhang Yu, Jingyu Zhuang, Qi Zhang, Jinwei Chen, Qingnan Fan.

• The first to address the problem of reselected key photo restoration in Live Photos.
• LiveMoments significantly improves perceptual quality and fidelity over existing solutions, including the recent flagships from vivo and iPhone.

BokehDiff: Neural Lens Blur with One-Step Diffusion

Chengxuan Zhu, Qingnan Fan, Qi Zhang, Jinwei Chen, Huaqi Zhang, Chao Xu, Boxin Shi.

arXiv / codes / press: Droider

• The first neural lens blur rendering pipeline based on pretrained diffusion priors.
• A diffusion framework with only one inference step that achieves outstanding quality compared with previous methods, especially in regions where depth prediction fails.

TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution

Linwei Dong*, Qingnan Fan*, Yihong Guo, Zhonghao Wang, Qi Zhang, Jinwei Chen, Yawei Luo, Changqing Zou.

arXiv / codes

• The first image super-resolution work that leverages the pretrained diffusion transformer (DIT) prior, specifically Stable Diffusion 3.
• TSD-SR has superior restoration results (most of the metrics perform the best) and the fastest inference speed (e.g. 40 times faster than SeeSR) compared to the past Real-ISR approaches based on pre-trained diffusion priors.

Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders

Qiming Hu, Linlong Fan, Yiyan Luo, Yuhang Yu, Xiaojie Guo, Qingnan Fan.

arXiv / codes

• The first full-image text super-resolution work utilizing the diffusion priors.
• A novel diffusion-based SR framework, which integrates text-aware attention and joint segmentation decoders to recover not only natural details but also the structural fidelity of text regions in degraded real-world images.

AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior

Guoqiang Liang, Qingnan Fan, Bingtao Fu, Jinwei Chen, Hong Gu, Lin Wang.

arXiv / codes

• Pioneering a new approach by enhancing the generative capabilities of pretrained T2I models for authentic face restoration, moving beyond traditional model design.
• A novel framework, namely AuthFace that achieves highly authentic face restoration results by exploring a face-oriented generative diffusion prior.

A General Decoupled Learning Framework for Parameterized Image Operators

Qingnan Fan*, Dongdong Chen*, Lu Yuan, Gang Hua, Nenghai Yu, Baoquan Chen.

arXiv / codes / bibtex

• A journal extension of our ECCV 2018 paper. We further propose a cheap parameter-tuning version of the decouple learning framework that enables real-time alternation between different image operators.

Image Smoothing via Unsupervised Learning

Qingnan Fan, Jiaolong Yang, David Wipf, Baoquan Chen, Xin Tong.

arXiv / codes / supp file / bibtex

• Treat deep learning as an optimization tool to minimize the proposed image smoothing objective function in an unsupervised manner. Multiple different smoothing effects can be easily learned by adaptively changing the proposed objective function.

Revisiting Deep Intrinsic Image Decompositions

Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf.

arXiv / codes / slides / supp file / poster / bibtex

• The first demonstration of a single basic deep architecture capable of achieving state-of-the-art results when applied to each of the major intrinsic benchmarks.

A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing

Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf.

arXiv / codes / supp file / poster / bibtex

• An advanced deep architecture for low-level vision tasks; A novel reflection image synthesis approach which enables outstanding generalization ability to real images with trained newtork.

JumpCut: Non-Successive Mask Transfer and Interpolation for Video Cutout

Qingnan Fan, Fan Zhong, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen.

codes / slides / video / supp file / dataset / bibtex

• An interactive real-time video segmentation algorithm. Significantly improve the video cutout accuracy and efficiency.

SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos

Yuzheng Liu*, Siyan Dong*, Shuzhe Wang, Yingda Yin, Yanchao Yang, Qingnan Fan, Baoquan Chen.

arXiv / video / codes

• Award: China3DV 2025, Top1 paper.
• SLAM3R is a real-time dense scene reconstruction system that regresses 3D points from video frames using feed-forward neural networks, without explicitly estimating camera parameters.

Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization

Siyan Dong*, Shuzhe Wang*, Shaohui Liu, Lulu Cai, Qingnan Fan, Juho Kannala, Yanchao Yang.

arXiv / video / demo (online) / codes

• Reloc3r is a simple yet effective camera pose estimation framework that combines a pre-trained two-view relative camera pose regression network with a multi-view motion averaging module.

Scene-aware Activity Program Generation with Language Guidance

Zejia Su, Qingnan Fan, Xuelin Chen, Oliver van Kaick, Hui Huang, Ruizhen Hu.

project page / supp file / bibtex

• We address the problem of scene-aware activity program generation, which requires decomposing a given activity task into instructions that can be sequentially performed within a target scene to complete the activity.

C·ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters

Zhiyang Dou, Xuelin Chen, Qingnan Fan, Taku Komura, Wenping Wang.

arXiv / project page / video / bibtex

• We present C·ASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for Elite physics-based characters.

DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Object Manipulation

Yan Zhao*, Ruihai Wu*, Zhehuan Chen, Yourong Zhang, Qingnan Fan, Kaichun Mo, Hao Dong.

arXiv / project page / video / bibtex

• We propose a novel learning framework, DualAfford, to learn collaborative affordance for dual-gripper manipulation tasks. The core design of the approach is to reduce the quadratic problem for two grippers into two disentangled yet interconnected subtasks for efficient learning.

3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification

Jiazhao Zhang*, Liu Dai*, Fanpeng Meng, Qingnan Fan, Xuelin Chen, Kai Xu, He Wang.

arXiv / project page / bibtex

• We propose a framework for the challenging 3D-aware Object goal navigation task based on two straightforward sub-policies. The two sub-polices, namely corner-guided exploration policy and category-aware identification policy, simultaneously perform by utilizing online fused 3D points as observation.

VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects

Ruihai Wu*, Yan Zhao*, Kaichun Mo*, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, Hao Dong.

arXiv / project page / codes / video / bibtex

• Award: WAIC 2025, Young Outstanding Paper Award
• We design an interaction-for-perception framework, VAT-MART, to learn actionable visual representations for more effective manipulation of 3D articulated objects.

AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions

Yian Wang*, Ruihai Wu*, Kaichun Mo*, Jiaqi Ke, Qingnan Fan, Leonidas Guibas, Hao Dong.

arXiv / project page / codes / video / bibtex

• In this paper, we propose a novel framework, named AdaAfford, that learns to perform very few test-time interactions for quickly adapting the affordance priors to more accurate instance-specific posteriors.

Towards Accurate Active Camera Localization

Qihang Fang*, Yingda Yin*, Qingnan Fan, Fei Xia, Siyan Dong, Sheng Wang, Jue Wang, Leonidas Guibas, Baoquan Chen.

arXiv / codes / video / supp file / bibtex

• In this work, we explicitly model the camera and scene uncertainty components to solve the problem of active camera localization by reinforcement learning. Our algorithm improves over the state-of-the-art Markov Localization based approaches by a large margin on the fine-scale camera pose accuracy.

Multi-Robot Active Mapping via Neural Bipartite Graph Matching

Kai Ye*, Siyan Dong*, Qingnan Fan, He Wang, Li Yi, Fei Xia, Jue Wang, Baoquan Chen.

arXiv / codes / video / supp file / poster / bibtex

• We propose a novel multi-robot active mapping algorithm by reducing the problem to bipartite graph matching, solved by the proposed multiplex graph neural network (mGNN) via reinforcement learning.

CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

Yijia Weng*, He Wang*, Qiang Zhou, Yuzhe Qin, Yueqi Duan, Qingnan Fan, Baoquan Chen, Hao Su, Leonidas Guibas.

arXiv / project page / codes / video / bibtex

• For the first time, we propose a unified framework that can handle 9-DoF pose tracking for novel rigid object instances as well as per-part pose tracking for 3D articulated objects.

Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments

Siyan Dong*, Qingnan Fan*, He Wang, Ji Shi, Li Yi, Thomas Funkhouser, Baoquan Chen, Leonidas Guibas.

arXiv / codes / video / bibtex

• A novel outlier-aware neural tree to tackle the camera localization challenges in dynamic indoor environments. It achieves the best performance in the RIO-10 benchmark.

Generative 3D Part Assembly via Dynamic Graph Learning

Jialei Huang*, Guanqi Zhan*, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas Guibas, Hao Dong.

arXiv / project page / codes / bibtex / press (机器之心,AI科技评论)

• A dynamic graph learning algorithm for autonomous part assembly. It learns to reason an assembly-oriented dynamically-evolved relation graph, which indicates the assembly process which is guided by the major parts (chair leg&seat).