MEDUSA Sample Clauses

MEDUSA. UPF is working on a machine learning (ML) based human character animation engine. The ML network learns how humans move in different styles (e.g. running, walking, sad, happy...). Through the core technology, the learned animation can be applied to an arbitrary human character. This approach is thought for virtual characters which receive the orders directly from the artist. Furthermore, UPF is working on another approach, focused on automatically controlling background characters, reducing the time spent if this task was done manually. UPF has developed a web tool, called MEDUSA, in order to determine the characters’ identity, including the behaviour and the style. This background character will be controlled by a graph programmed Hybrid Behaviour Tree, an evolution of common Behaviour Trees. With this approach virtual characters are always aware of their surroundings to allow the Hybrid Behaviour Tree system to compute the final action taking into account the characters properties and their environment. Additionally a character can interact with scene elements and extract relevant information from them. For example, a virtual character could go to a store and acquire some item needed to perform a concrete task, or react in real-time to changes on the scenario or its properties, adapting automatically the animations to those events. All this can be authored in a low-coding tool in a web browser as the engine is based on WebGL [1]. Interface of the web based Hybrid Behaviour Tree editor MEDUSA The Hybrid Behaviour Tree system is controlled by a library called HBTree.js, which is agnostic to the rest of the components of the tool. This fact allows that library to be integrated into different web based render engines, as an API is provided to overwrite the needed methods (used by specific nodes) to access the scenario information and the virtual character properties. The generated HBT only deals with data and returns a behaviour, which is interpretable by each approach. Thus, Medusa could be used as a testing or simulation platform for other developments, like game AIs or virtual productions. Through the introduced VPET animation streaming API, both approaches are easily integrated into the tablet tools and can even coexist within the same scene. FA provided the training data for the ML algorithm as high quality optical motion capturings [2]. This database is also released publicly.
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