Xiangfei Meng (孟祥飞)

Xiangfei Meng is currently a Staff Engineer at Bigo, specializing in Face Beauty Algorithm, Deep Learning Inference Engine (focused on iOS GPU Acceleration), and Computer Vision (primarily in Video Enhancement). Before joining Bigo in March 2018, he obtained a master’s degree in Computer Science and Technology from Beihang University (BUAA), under the guidance of Professor Hong Qin in the State Key Laboratory of Virtual Reality Technology and Systems (VRLAB), School of Computer Science and Engineering (SCSE). He received his bachelor’s degree in Computer Science and Technology from Civial Aviation University of China (CAUC) as an “Outstanding Graduate” in 2015. He was honored with the title of “Top-10 College Students in CAUC” in Autumn 2014. After that, he received the highest score in the National Entrance Examination for Postgraduate for SCSE, BUAA in December 2014 and became a postgraduate at BUAA in September 2015.

His research interests include Computer Vision, Machine Learning, and Computer Graphics. Recently, he focused on real-time Face Beauty Algorithms on mobile devices.

Personally, he is highly interested in playing musical instruments. He is skilled at playing the Chinese flute（竹笛）. Recently, he embarked on learning to play the piano from square one.

Work Experiences

Time	Company	Title	Working Field
2018/03 - now	Bigo	Staff Engineer	Video Enhancement, Deep Learning Inference Engine
2017/06 - 2017/08	Microsoft	Intern	Visual Studio Development

Education

Time	School	Phase
2015/09 – 2018/03	Beihang University	Postgraduate
2011/09 – 2015/06	Civil Aviation University of China	Undergraduate

Skills

1. Experienced C/C++ programming skills with good coding styles.
2. Familiar with mobile GPU programming, especially Metal.
3. Familiar with face beauty and video enhancement algorithms.
4. NOT familiar with LeetCode.

Company Projects

1. Face Beauty (2021~now)

• Responsible for the face beauty effect in BigoLive. Leading a stable and efficient team consisting of 4 team members (including myself).
• Improved the development quality. Reduced the frequency of online issue reporting from 1 issue per day to 1 issue per month.
• Optimized the effect of skin smoothing, beauty items, and face reshaping items to the industry-leading level. Used the latest AI techniques to further raise the user experience.
• Launched 3 rounds of region-oriented face beauty optimization to enhance the experience of native users. Received highly positive user experience feedback.

2. Deep Learning Inference Engine (2020)

• Deep learning inference engine on mobile devices. Focused on iOS GPU acceleration (Metal).
• Top-level inference speed on typical CNN models (mobilenet, resnet, densenet, etc.) among common commercial inference engines (TFLite, MNN, TNN, CoreML).
• Fastest Metal implementation of Winograd convolution compared with all available inference engines, especially TFLite which was the known fastest implementation at that time.
• DSP integration with low power and high performance.

3. Video Enhancement (2019)

Video Super Resolution

• Video super-resolution algorithms for both light-weight version (mobile device, ML algorithm) and heavy-computation version (GPU server, deep learning).
• Focused on the real-scenario application (with noise, codec artifacts, and long video). Solved various issues when applying deep neural networks to real scenarios.
• Video demo is not released for the commercial issue.

Other Algorithms

• Video Color Enhancement.
• Video Temporal Stabilization.
• Video Denoise.

4. Data-Driven Face Animation (emoji) (2018)

• Data-driven face animation on mobile devices. Track the expression of the face captured by the camera, and retarget it to a cartoon model in real time.
• 3DMM, blendshape, and linear optimization.
• Android demo development, including camera API and rendering with GLES 2.0.

School Projects

1. Fish Motion Capture and Retargeting by Monocular Camera (2017)

Motion capture and retargeting of fish generally have difficulties in marker attachment and feature description of the soft body.

We present a contour-based feature extraction to get the motion pattern of a fish from the camera. A two-level motion retargeting scheme is proposed to retarget the recorded motion to a new fish model, regardless of its body and fin proportions.

With this technique, we can drive a hand-drawn fish or a fish-like character (say a mermaid, or a flower) to swim with the same motion style as the real one in the fish tank.

Publication:
Xiangfei Meng, Junjun Pan, Hong Qin, Pu Ge. Real-time Fish Animation Generation by Monocular Camera. Computers & Graphics. 71(2018): 55-65. [pdf]

Xiangfei Meng, Junjun Pan, Hong Qin. “Motion Capture and Retargeting of Fish by Monocular Camera.” 2017 International Conference on Cyberworlds (CW 2017), IEEE, Chester, UK, September 20-22, 2017. [pdf][video]

2. Part Implementation of STL (2016)

Partly implemented the C++ standard template library (STL), including several types of containers (vector, list, deque), their corresponding iterators (ordinary iterator, constant iterator, reverse iterator), a few container adaptors (stack, queue, priority_queue) and some common algorithms (sort, find). Mainly used C++ features include low-level memory management, specialization and the part specialization of templates, function objects, etc.

3. Ray Tracer (2016)

Implemented a ray tracer as an offline renderer. This renderer is equipped with a parser to analyze model files containing triangles and spheres. The scene is then rendered with global illumination, which includes effects of ambient, diffuse, specular, reflection, refraction, soft shadow and color bleeding. The rendering process is speeded up by OpenMP with thread-level parallelization.

4. Pascal Compiler (2015)

Implemented a Pascal Compiler using pure C++ without other lexical/syntax tools. The compiler parses Pascal source code and translates it to Intel-i386 assembly code. The assembled executable can run on the Windows operating system. The compiler provides full support for nested function definitions, recursion callings of functions, and the parameter passing either by values or references.

5. Spammer Detection in Social Networks (2014)

Spammer detection is a typical scenario of the application of classifiers. To detect spammers in social networks, we collected 4109 profiles from Weibo, extracted their features, and delivered them to several classifiers. We implemented a Bayesian classifier and a C4.5 Decision Tree classifier. Bayesian classifier is theoretically optimal as long as the conditional probability density functions are known, and the Decision Tree is independent of dimensions. Through the 10-fold cross-validation, the Bayes Classifier showed 58.3% in recall, 85.4% in precision, and 0.693 in F1-Measure, and the Decision Tree showed 60.1% in recall rate, 85.4% in precision, and 0.706 in F1-Measure.

Publication:
孟祥飞, 徐路, 王思雨. 基于新浪微博的社交网络垃圾用户分析与检测[J]. 科技与创新, 2014(15):125-127. [pdf]
张宇翔, 孙菀, 杨家海, 周达磊, 孟祥飞, 肖春景. 新浪微博反垃圾中特征选择的重要性分析[J]. 通信学报, 2016, 37(8):24-33. [pdf]

6. Moving Object Tracking (2013)

Moving object tracking is a classical problem of Computer Vision since it can provide essential information on the shape and motion of the foreground objects.

We implemented a real-time moving object tracking system with a static camera.

1. Segment foreground pixels with the Gaussian Mixture Model (GMM), which is a pixel-wise process.
2. Employ a morphology-open filter to eliminate the salt-and-pepper noise and a connected component identification to label the salient components as foreground objects.
3. Identify the correspondence between consecutive frames by a Max A Priori algorithm, and handle the splitting, merging, appearing and vanishing of objects.

4. Estimate trajectories of detected objects by a group of Kalman Filters.

   The system is robust to the slight shaking of the camera, and the gradual change of the light. It can reach 20 FPS with 640×480 resolution on my laptop equipped with a 2.9GHz CPU and 4GB memory.

Contact

MengXiangfei_job@163.com (for recruiting business)
XiangfeiMeng@buaa.edu.cn (for academic communication)