BlanketGen - A synthetic blanket occlusion augmentation pipeline for MoCap datasets
This addresses a domain-specific problem for clinical motion analysis by providing an augmented dataset to enhance pose estimation in occluded scenarios, though it is incremental as it builds on existing datasets and methods.
The authors tackled the lack of representative datasets for human motion analysis in clinical in-bed scenarios by implementing BlanketGen, a pipeline that augments videos with synthetic blanket occlusions, and used it to generate BlanketGen-3DPW, which improved model performance with promising results.
Human motion analysis has seen drastic improvements recently, however, due to the lack of representative datasets, for clinical in-bed scenarios it is still lagging behind. To address this issue, we implemented BlanketGen, a pipeline that augments videos with synthetic blanket occlusions. With this pipeline, we generated an augmented version of the pose estimation dataset 3DPW called BlanketGen-3DPW. We then used this new dataset to fine-tune a Deep Learning model to improve its performance in these scenarios with promising results. Code and further information are available at https://gitlab.inesctec.pt/brain-lab/brain-lab-public/blanket-gen-releases.