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David Coleman - Creating QT interfaces for Maya and MotionBuilder This Masterclass will demonstrate the workflow of creating interfaces in QT Designer, and connecting them to functions within Maya and MotionBuilder. The basics of QT Designer will be covered, along with connecting sliders to drive facial blendshapes using the same interface for both Maya and MotionBuilder. Some techniques for procedurally creating QT widgets using PyQt will also be demonstrated.

The rapid rig is a tool designed to bridge the gap between character modeling and character animation. Using the user interface, the script enables the user to set up a bi-pedal character skeleton and rig in minutes, a process which can take up to several days. The advanced version of the rig comes with enhanced features including: * customizable number of fingers and toes * stretchy and "toony" arms, legs, neck and spine * elbow and knee locking and following in inverse kinematics * orientation control for hips, shoulders, head and eyes * optional geometry created based on the user's rig set-up, as well as other animator-friendly controls. * animation interface for speeding up animation pipeline

thanks for the link, Jakub! :)

FaceTracker is a C/C++ API for real time generic non-rigid face alignment and tracking. Goal: Non-rigid face alignment and tracking is a common problem in computer vision. It is the front-end to many algorithms that require registration, for example face and expression recognition. However, those working on algorithms for these higher level tasks are often unfamiliar with the tools and peculiarities regarding non-rigid registration (i.e. pure machine learning scientists, psychologists, etc.). Even those directly working on face alignment and tracking often find implementing an algorithm from published work to be a daunting task, not least because baseline code against which performance claims can be assessed does not exist. As such, the goal of FaceTracker is to provide source code and pre-trained models that can be used out-of-the-box, for the dual purpose of: 1. Promoting the advancement of higher level inference algorithms that require registration. 2. Providing baseline code to promote quantitative improvements in face registration. Features: * Real time: ranging from 20-30 fps (depending on processor, compiler and use of OpenMP) * Generic: designed to work for most people under most conditions * No training required: a pre-trained model is provided * Detection based initialisation: no user intervention required * Automatic failure detection: requires no user re-initialisation * Camera or video input

Thanks Maurizio for the link!