DARTS: Double Attention Reference-based Transformer for Super-resolution

We present DARTS, a transformer model for reference-based image super-resolution. DARTS learns joint representations of two image distributions to enhance the content of low-resolution input images through matching correspondences learned from high-resolution reference images. Current state-of-the-art techniques in reference-based image super-resolution are based on a multi-network, multi-stage architecture. In this work, we adapt the double attention block from the GAN literature, processing the two visual streams separately and combining self-attention and cross-attention blocks through a gating attention strategy. Our work demonstrates how the attention mechanism can be adapted for the particular requirements of reference-based image super-resolution, significantly simplifying the architecture and training pipeline. We show that our transformer-based model performs competitively with state-of-the-art models, while maintaining a simpler overall architecture and training process. In particular, we obtain state-of-the-art on the SUN80 dataset, with a PSNR/SSIM of 29.83 / .809. These results show that attention alone is sufficient for the RSR task, without multiple purpose-built subnetworks, knowledge distillation, or multi-stage training

A finite element model of the face including an orthotropic skin model under in vivo tension

Computer models of the human face have the potential to be used as powerful tools in surgery simulation and animation development applications. While existing models accurately represent various anatomical features of the face, the representation of the skin and soft tissues is very simplified. A computer model of the face is proposed in which the skin is represented by an orthotropic hyperelastic constitutive model. The in vivo tension inherent in skin is also represented in the model. The model was tested by simulating several facial expressions by activating appropriate orofacial and jaw muscles. Previous experiments calculated the change in orientation of the long axis of elliptical wounds on patients’ faces for wide opening of the mouth and an open-mouth smile (both 30 degrees). These results were compared with the average change of maximum principal stress direction in the skin calculated in the face model for wide opening of the mouth (18o) and an openmouth smile (25 degrees). The displacements of landmarks on the face for four facial expressions were compared with experimental measurements in the literature. The corner of the mouth in the model experienced the largest displacement for each facial expression (11–14 mm). The simulated landmark displacements were within a standard deviation of the measured displacements. Increasing the skin stiffness and skin tension generally resulted in a reduction in landmark displacements upon facial expression