Qingnan Fan (樊庆楠)

I am a Lead Researcher and team manager in the Imaging Algorithm Center of VIVO. Our group is the core algorithm team responsible for advancing the photographic quality in the flagship smartphones with the cutting-edge technologies (3D, AIGC, etc).

I was a Senior Researcher in the Visual Computing Center of Tencent AI Lab between 2021 to 2023.

I was a Postdoctoral Researcher in Stanford University supervised by Prof. Leonidas Guibas between 2019 to 2021.

I obtained my PhD degree in the Computer Science and Technology School of Shandong University at 2019. I was supervised by Prof. Baoquan Chen.

If you are interested in the internship in our group for either publishing academic papers or working on engineering projects, feel free to drop me an email.

Email  /  CV  /  Biography  /  Google Scholar  /  LinkedIn  /  Twitter  /  Github

VIVO X200 series: Telephoto Image Enhancement​

I am the host of BlueTalk, which invites the expert speakers in the fields of computer vision, graphics and computational photography. The mission of BlueTalk is to promote the communication between academic and industrial communities, and explore the possibilities of landing the most innovative research ideas into any AI-powered industrial applications. If you are insterested in joining this family to give a talk, feel free to let me know.

2024-12-09: Du Chen, PolyU, Real-world image super-resolution: solutions and challenges

2024-10-30: Mengwei Ren, Adobe, Relightful harmonization: lighting-aware portrait background replacement

2024-08-26: Jianqi Ma, PolyU, AI vision research based on text

2023-09-08: Xintao Wang, Tencent, Visual generation and editing via diffusion models

2023-08-15: Huan Ling, Nvidia, Generative models and vision perception using diffusion models

2023-07-25: Jianmin Bao, MSRA, 2D & 3D visual synthesis and manipulation via diffusion models

My research focus lies in computer graphics, 3D vision, image processing, and human-computer interaction. My recent effort has been spent on pushing the limit of 3D vision and reinforcement learning technologies to implement an intelligent embodied agent in both forms of physical robots and digital humans. For the complete publication list, please refer to my google scholar page.

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.

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.

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

We describe a method to deal with performance drop in semantic segmentation caused by viewpoint changes within multi-camera systems, where temporally paired images are readily available, but the annotations may only be abundant for a few typical views.

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.

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.

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.

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.

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

Reduce the material cost and weight of a given object while providing a durable printed model that is resistant to impact and external forces.

APR. 2022: Active 3D scene understanding and its applications
“三维视觉与智能图形”前沿论坛, 图图名师讲堂, China

OCT. 2021: Visual Localization
Embodied AI Workshop, Valse, China

JAN. 2019: Deep Learning in Computational Photography
USC ICT/UW Reality Lab/Berkeley/Stanford/Google/MSR, US

DEC. 2018: Deep Learning for Single Image Artifact Removal
ACCV Tutorial 2018, Australia

DEC. 2018: Image Smoothing via Unsupervised Learning
SIGGRAPH Asia 2018, Japan; GAMES Webinar, China

AUG. 2018: Discovering Unsupervised Learning in Image Processing
CIA, Cambridge University, UK

JUN. 2018: Revisiting Deep Intrinsic Image Decomposition
CVPR 2018, USA

NOV. 2015: Interactive Real-time Video Segmentation
SIGGRAPH Asia 2015, Japan

2022: Tencent Outstanding Contributor

2020: CCF Doctorial Dissertation Award Nominee (CCF 优博提名)

2019: Outstanding Academic Achievement Award of Shandong University

2018: Academic Star Nominee of Shandong University (10/20000)

2018: National Scholarship

2016: Outstanding Academic Achievement Award of Shandong University

2015: Presidential Scholarship of Shandong University (35/20000) (Highest honor for students in SDU, only 35 elected among around 20000 candidates)

2015: National Scholarship

2015: Pacemaker to Outstanding Graduate Student of Shandong University