Mingfei Chen

I am a first-year Ph.D. student in the Electrical and Computer Engineering department at University of Washington, Seattle. Currently, I am a member of NeuroAI Lab advised by Prof. Eli Shlizerman, focusing on the audio-visual learning, as well as the related applications.

Before that, I recieved my B.S. degree from the Computer Science and Technology department of Huazhong University of Science and Technology of China in 2020. I am also lucky to work on language-guided video retrieval with Prof. Chang Wen Chen and Prof. Junsong Yuan, human detection and segmentation in Bytedance AI Lab, human-object interaction (HOI) with Prof. Si Liu, multiple-object tracking (MOT) with Prof. Jenq-Neng Hwang in IPL lab, and 3D photo-realistic digital human rendering with Prof. Shuicheng Yan and Prof. Jiashi Feng in Sea AI Lab.

I'm currently looking for research intern roles for Summer 2024!

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We introduce a novel method and end-to-end integrated rendering pipeline which allows the listener to be everywhere and hear everything (BEE) in a dynamic scene in real time, based on sparse audio-visual samples.

We propose an Implicit Neural Representation for Audio Scenes, INRAS, for efficient representation of spatial audio fields with high fidelity.

We develop a geometry-guided generalizable and efficient Neural Radiance Field (NeRF) pipeline for high-fidelity free-viewpoint human body synthesis under settings with sparse camera views.

We propose a novel Reference Search (RS) module to provide a more reliable association based on the deformable transformer structure, which is natural to learn the feature alignment for each object among frames.

We reformulate HOI detection as an adaptive set prediction problem, with this novel formulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with parallel instances and interaction branches.