Qiqi Hou

I am a machine learning researcher at Qualcomm AI Research. Much of my current research is about the applications of deep learning, such as data compression, Monte Carlo rendering, perceptual quality metrics, frame interpolation, matting, face landmark detection and etc. Before that, I received my Ph.D. and M.E. degrees from Portland State University and Xi'an Jiaotong University in 2023 and 2017, respectively, where I was advised by Feng Liu and Jinjun Wang, respectively.

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We propose a parallel codec for fast and efficient low-latency stereo video compression.

We accelerate Monte-Carlo rendering via super-resoultion guided by high-resolution fast-to-compute auxiliary features.

We propose a perceptual quality metric dedicated to video frame interpolation.

We accelerate the Monte-Carlo rendering via multiple resolution sampling. We also introduce a large scale ray-tracing dataset - BCR dataset.

We can estimate the alpha matte and foreground simultaneously.

A face align recurent network to help avoid over-strong early stage regressors and over-weak later stage regressors.

A multi-channel convolutional neural network approach for face alignment.

Last updated: Oct, 2022. Template from here.