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Found 9 papers, 2 papers with code
Efficient conditioned face animation using frontally-viewed embedding
As the quality of few shot facial animation from landmarks increases, new applications become possible, such as ultra low bandwidth video chat compression with a high degree of realism.
Fluid registration between lung CT and stationary chest tomosynthesis images
We demonstrate our approach for the registration between CT and stationary chest tomosynthesis (sDCT) images and show how it naturally leads to an iterative image reconstruction approach.
Deep Decomposition for Stochastic Normal-Abnormal Transport
Extensive comparisons demonstrate that our model successfully distinguishes stroke lesions (abnormal) from normal brain regions, while reconstructing the underlying velocity and diffusion tensor fields.
Accurate Point Cloud Registration with Robust Optimal Transport
Finally, we showcase the performance of transport-enhanced registration models on a wide range of challenging tasks: rigid registration for partial shapes; scene flow estimation on the Kitti dataset; and nonparametric registration of lung vascular trees between inspiration and expiration.
Self-appearance-aided Differential Evolution for Motion Transfer
Image animation transfers the motion of a driving video to a static object in a source image, while keeping the source identity unchanged.
Discovering Hidden Physics Behind Transport Dynamics
To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields.
Perfusion Imaging: A Data Assimilation Approach
In this work we therefore propose a data-assimilation approach (PIANO) which estimates the velocity and diffusion fields of an advection-diffusion model that best explains the contrast dynamics.
Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces
Charting cortical growth trajectories is of paramount importance for understanding brain development.
Local Temperature Scaling for Probability Calibration
Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation.
