Hence, we propose to use the domain-invariant geometry structure among keypoints as a "bridge" constraint to optimize DAKDN for 6D pose estimation across domains.
The framework is able to capture both local and long-range dependencies via the proposed attention mechanism for the learned appearance representations, which are further enriched by temporally attended physiological cues (remote photoplethysmography, rPPG) that are recovered from videos in the auxiliary task.
Heart beat rhythm and heart rate (HR) are important physiological parameters of the human body.
For the fine extraction stage, we design a new multiscale U-Net (MSU-Net) to effectively remove disease noise and refine the sketch.
However, the GAN-based SR methods only use image discriminator to distinguish SR images and high-resolution (HR) images.