Lu Ming

I am a research scientist at AI Research Lab (AIRL), Intel Labs, where I work on Enhanced Visual AI (EVAI). I am also a visiting scholar at HMI Lab, PKUCS supervised by Prof. Zhang Shanghang. I did my Ph.D focused on computer graphics and 3D vision at Tsinghua University, where I was advised by Prof. Zhang Li.

I'm especially interested in 1) AI + Chips (vision ISP/pre and post processing for Codec/rendering acceleration for GPU/etc.), 2) Neural Field Simulator (robot navigation/robot manipulation/autonomous driving/digital human/thermal/underwater/etc.), 3) Large Vision-Language Models (training-free/efficient plugins for SAM/Diffusion Model/LLaVA/Qwen-VL/VLA/VLN/etc.), and 4) AI for Science (neural field for medical data compression/etc.).

I always focus on applied research of EVAI techniques.

Research Intro  /  Email  /  CV  /  Bio  /  Google Scholar  /  Github

profile photo
AI + Chips
[7] CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input
Senmao Tian, Ming Lu, Jiaming Liu, Yandong Guo, Yurong Chen, Shunli Zhang
Conference on Computer Vision and Pattern Recognition (CVPR), 2023

[6] Adaptive Patch Exiting for Scalable Single Image Super-Resolution
Shizun Wang, Jiaming Liu, Kaixin Chen, Xiaoqi Li, Ming Lu, Yandong Guo
European Conference on Computer Vision (ECCV Oral), 2022

[5] Efficient Meta-Tuning for Content-Aware Neural Video Delivery
Xiaoqi Li, Jiaming Liu, Shizun Wang, Cheng Lyu, Ming Lu, Yurong Chen, Anbang Yao, Yandong Guo, Shanghang Zhang
European Conference on Computer Vision (ECCV), 2022

[4] SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
Shizun Wang, Ming Lu, Kaixin Chen, Jiaming Liu, Xiaoqi Li, Ming Wu
British Machine Vision Conference (BMVC), 2021

[3] Deep Likelihood Network for Image Restoration With Multiple Degradation Levels
Yiwen Guo, Ming Lu, Wangmeng Zuo, Changshui Zhang, Yurong Chen
Transactions on Image Processing (TIP), 2021

[2] Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Jiaming Liu, Ming Lu, Kaixin Chen, Xiaoqi Li, Shizun Wang, Zhaoqing Wang, Enhua Wu, Yurong Chen, Chuang Zhang, Ming Wu
International Conference on Computer Vision (ICCV), 2021

[1] A Closed-Form Solution to Universal Style Transfer
Ming Lu, Hao Zhao, Anbang Yao, Yurong Chen, Feng Xu, Zhang Li
International Conference on Computer Vision (ICCV), 2019

Neural Field Simulator
[6] K-Buffers: A Plug-in Method for Enhancing Neural Fields with Multiple Buffers
Haofan Ren, Zunjie Zhu, Xiang Chen, Ming Lu, Rongfeng Lu, Chenggang Yan
International Joint Conference on Artificial Intelligence (IJCAI), 2025

[5] PLGS: Robust Panoptic Lifting with 3D Gaussian Splatting
Yu Wang, Xiaobao Wei, Ming Lu, Guoliang Kang
Transactions on Image Processing (TIP), 2025

[4] SliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation
Jianing Li, Ming Lu, Hao Wang, Chenyang Gu, Wenzhao Zheng, Li Du, Shanghang Zhang
International Conference on Robotics and Automation (ICRA), 2025

[3] ThermalGaussian: Thermal 3D Gaussian Splatting
Rongfeng Lu, Hangyu Chen, Zunjie Zhu, Yuhang Qin, Ming Lu, Le Zhang, Chenggang Yan, Anke Xue
International Conference on Learning Representations (ICLR), 2025

[2] Superpixel-based Efficient Sampling for Learning Neural Fields from Large Input
Zhongwei Xuan, Zunjie Zhu, Shuai Wang, Haibing Yin, Hongkui Wang, Ming Lu
International Conference on Multimedia (MM), 2024

[1] NTO3D: Neural Target Object 3D Reconstruction with Segment Anything
Xiaobao Wei, Renrui Zhang, Jiarui Wu, Jiaming Liu, Ming Lu, Yandong Guo, Shanghang Zhang
Conference on Computer Vision and Pattern Recognition (CVPR), 2024

Large Vision-Language Models
[5] AutoV: Learning to Retrieve Visual Prompt for Large Vision-Language Models
Yuan Zhang, Chun-Kai Fan, Tao Huang, Ming Lu, Sicheng Yu, Junwen Pan, Kuan Cheng, Qi She, Shanghang Zhang
arXiv, 2025

[4] FastInit: Fast Noise Initialization for Temporally Consistent Video Generation
Chengyu Bai, Yuming Li, Zhongyu Zhao, Jintao Chen, Peidong Jia, Qi She, Ming Lu, Shanghang Zhang
arXiv, 2025

[3] Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs
Qizhe Zhang, Mengzhen Liu, Lichen Li, Ming Lu, Yuan Zhang, Junwen Pan, Qi She, Shanghang Zhang
arXiv, 2025

[2] [CLS] Attention is All You Need for Training-Free Visual Token Pruning: Make VLM Inference Faster
Qizhe Zhang, Aosong Cheng, Ming Lu, Zhiyong Zhuo, Minqi Wang, Jiajun Cao, Shaobo Guo, Qi She, Shanghang Zhang
arXiv, 2025

[1] MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders
Jiajun Cao, Yuan Zhang, Tao Huang, Ming Lu, Qizhe Zhang, Ruichuan An, Ningning Ma, Shanghang Zhang
Conference on Computer Vision and Pattern Recognition (CVPR), 2025

AI for Science
[3] Implicit Neural Image Field for Biological Microscopy Image Compression
Gaole Dai, Cheng-Ching Tseng, Qingpo Wuwu, Rongyu Zhang, Shaokang Wang, Ming Lu,..., Jianxu Chen, Shanghang Zhang
Nature Computational Science (NCS), 2025

[2] A Generalist Foundation Model and Database for Open-World Medical Image Segmentation
Siqi Zhang, Qizhe Zhang, Shanghang Zhang, Xiaohong Liu, Jingkun Yue, Ming Lu, ..., Guangyu Wang
Nature Biomedical Engineering (NBE), 2025

[1] I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
Xiaobao Wei, Jiajun Cao, Yizhu Jin, Ming Lu, Guangyu Wang, Shanghang Zhang
European Conference on Computer Vision (ECCV), 2024

Application Demos
blind-date Our 3D face technique has been applied to Chris Lee's MV
blind-date Our 3D body technique has been applied to the opening ceremony of Winter Olympic Games 2022