Multimodal In-bed Pose and Shape Estimation under the Blankets

Advancing technology to monitor our bodies and behavior while sleeping and resting are essential for healthcare. However, keen challenges arise from our tendency to rest under blankets. We present a multimodal approach to uncover the subjects and view bodies at rest without the blankets obscuring the view. For this, we introduce a channel-based fusion scheme to effectively fuse different modalities in a way that best leverages the knowledge captured by the multimodal sensors, including visual- and non-visual-based. The channel-based fusion scheme enhances the model's flexibility in the input at inference: one-to-many input modalities required at test time. Nonetheless, multimodal data or not, detecting humans at rest in bed is still a challenge due to the extreme occlusion when covered by a blanket. To mitigate the negative effects of blanket occlusion, we use an attention-based reconstruction module to explicitly reduce the uncertainty of occluded parts by generating uncovered modalities, which further update the current estimation via a cyclic fashion. Extensive experiments validate the proposed model's superiority over others.