SynSLaG: Synthetic Sign Language Generator
Teppei Miura, Shinji Sako · 2021
Machine learning techniques have the potential to play an important role in sign language recognition. However, sign language datasets lack the volume and variety necessary to work well. To enlarge these datasets, we introduce SynSLaG, a tool that synthetically generates sign language datasets from 3D motion capture data. SynSLaG generates realistic images of various body shapes with ground truth 2D/3D poses, depth maps, body-part segmentations, optical flows, and surface normals. The large synthetic datasets provide possibilities for advancing sign language recognition and analysis.