  As the COVID-19 pandemic rampages across the world, the demands of video
conferencing surge. To this end, real-time portrait segmentation becomes a
popular feature to replace backgrounds of conferencing participants. While
feature-rich datasets, models and algorithms have been offered for segmentation
that extract body postures from life scenes, portrait segmentation has yet not
been well covered in a video conferencing context. To facilitate the progress
in this field, we introduce an open-source solution named PP-HumanSeg. This
work is the first to construct a large-scale video portrait dataset that
contains 291 videos from 23 conference scenes with 14K fine-labeled frames and
extensions to multi-camera teleconferencing. Furthermore, we propose a novel
Semantic Connectivity-aware Learning (SCL) for semantic segmentation, which
introduces a semantic connectivity-aware loss to improve the quality of
segmentation results from the perspective of connectivity. And we propose an
ultra-lightweight model with SCL for practical portrait segmentation, which
achieves the best trade-off between IoU and the speed of inference. Extensive
evaluations on our dataset demonstrate the superiority of SCL and our model.
The source code is available at https://github.com/PaddlePaddle/PaddleSeg.
