Loading patent…
AssigneeAdobe Inc.
InventorsJonathan Brandt, Chen Fang, Byungmoon Kim, Biao Jia
Estimating the term…
Some embodiments involve a reinforcement learning based framework for training a natural media agent to learn a rendering policy without human supervision or labeled datasets. The reinforcement learning based framework feeds the natural media agent a training dataset to implicitly learn the rendering policy by exploring a canvas and minimizing a loss function. Once trained, the natural media agent can be applied to any reference image to generate a series (or sequence) of continuous-valued primitive graphic actions, e.g., sequence of painting strokes, that when rendered by a synthetic rendering environment on a canvas, reproduce an identical or transformed version of the reference image subject to limitations of an action space and the learned rendering policy.
One or more non-transitory computer readable media for training a natural media agent to implicitly learn a rendering policy in a multi-dimensional continuous action space from a set of training references, the one or more non-transitory computer readable media comprising instructions that, when executed by at least one processor of a reinforcement learning-based system, iteratively cause the system to:
A computer-implemented method comprising:
A system comprising:
Dependencies are inferred from the claim text.