Xintao Alpha
Research Scientist at Tencent
Shenzhen, Guangdong Province, China
Summary
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RockstarXintao Alpha is a research scientist with a decade of hands-on experience in image and video restoration, based at Tencent ARC Lab in Shenzhen. He is a prolific open-source contributor and ML engineer behind widely used projects such as ESRGAN, Real-ESRGAN and GFPGAN, focusing on practical inference, model interoperability, and production-friendly tooling. His work spans low-level CUDA optimizations to high-level model training and deployment, including contributions that enabled CPU testing, checkpoint interpolation, and ONNX export for real-world applications. Known for bridging research and engineering, he consistently adds usability features—like model management, scale support, and robust inference pipelines—that make state-of-the-art restoration methods accessible beyond academia.
10 years of coding experience
Github Skills (43)
pytorch10
text-alignment10
convolutional-neural-networks10
c-language10
python10
dcgan10
machine-learning10
cgan10
detectors10
super-resolution10
mask-rcnn10
detector10
ncnn10
deep-learning10
image-restoration10
Programming languages (9)
Github contributions (5)
xinntao/Real-ESRGAN-ncnn-vulkan
Jul 2021 - Apr 2022
NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.
Role in this project:
Software Engineer
Software EngineerContributions:37 commits, 2 PRs, 14 pushes in 8 months
Contributions summary:Xintao primarily focused on enhancing the `real-esrgan-ncnn-vulkan` project, a tool for image restoration. Their contributions included supporting more models, including those for anime-specific content and newer versions. They also added functionality to create output directories and updated the project's documentation. Furthermore, the user refactored the code for model loading and added support for scale factors, contributing to the project's overall versatility and usability.
image-restorationncnnreal-esrgannvidiaintel
xinntao/ESRGAN
Aug 2018 - Jul 2021
ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
Role in this project:ML Engineer
ML EngineerContributions:61 commits, 3 PRs, 47 pushes in 2 years 11 months
Contributions summary:Xintao primarily contributed to the testing and modification of an ESRGAN model within the repository. They added test codes and command-line arguments to the testing script, demonstrating an understanding of model evaluation. Furthermore, they created a script to interpolate between different model checkpoints (RRDB_PSNR and RRDB_ESRGAN), showcasing an ability to manipulate and combine pre-trained models. The user also added functionality to run the tests on CPU.
super-resolutionesrgan
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