Physics-Informed Guided Disentanglement in Generative Networks.

Image-to-image translation (i2i) networks suffer from entanglement effects in presence of physics-related phenomena in target domain (such as occlusions, fog, etc), lowering altogether the translation quality, controllability and variability. In this paper, we propose a general framework to disentangle visual traits in target images. Primarily, we build upon collection of simple physics models, guiding the disentanglement with a physical model that renders some of the target traits, and learning the remaining ones.

Registration of Bony Landmarks and Soft Tissue Laxity during Robotic Total Knee Arthroplasty is Highly Reproducible.

Introduction: There is growing interest and enthusiasm for robotic total knee arthroplasty (TKA). Many robotic systems require registration of bony landmarks as well as a dynamic soft tissue evaluation to plan femoral and tibial resections. Variability in this user-driven registration can introduce error and undermine the purported precision and accuracy offered by robotics.

Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation.

Domain adaptation is an important task to enable learning when labels are scarce. While most works focus only on the image modality, there are many important multi-modal datasets. In order to leverage multi-modality for domain adaptation, we propose cross-modal learning, where we enforce consistency between the predictions of two modalities via mutual mimicking.