Unity Technologies Sf

Florian Deconinck

Richard Chi Lei

Sander Van Der Steen

Niall J Lenihan

Adam Christensen

John McCarten

Leo Hutson

An aspect provides a computer-implemented method for processing computer code. The method comprises receiving a first editable code set. In response to a first signal the method processes the first editable code set into first executable code by a first compiler within a first code processing path generating the first executable code. In response to a second signal the method both processes the first editable code set into a second editable code set, and processes the second editable code set into second executable code by a second compiler within a second code processing path generating the second executable code.

Method and system for processing computer code

An aspect provides a computer-implemented method for compiling software code. The method comprises: receiving software code to compile; receiving a set of parameters associated with settings and software employed to compile the software code; forming a first hash of the set of parameters to establish a unique identification of the set of parameters used to compile the software code; and associating the first hash with the compiled code. A further aspect provides a computer-implemented method of checking compatibility of compiled software code.

Method and system for identifying incompatibility between versions of compiled software code

An aspect provides a computer-implemented method for training controls for an animation control rig using a neural network. The method comprises receivinga combination of training data, wherein the combination of training data includes a first set of training data derived from image capture poses of a physical object and a second set of training data derived from posing an animation version of the physical object; receivinga set of coarse error data derived from a first training process of the combination of training data, the first training process comprising passing at least some of the combination of training data to the neural network as a sequence of training steps; determininga rate of change of training errors associated with the coarse error data; in response to detectinga rate of change of training errors that crosses a rate of change threshold, during a second processing of the combination of training data, varyinga learning rate over time based on a slope of the rate of change of training errors.

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Wellington, New Zealand

Florian Deconinck