State detection using machine-learning model trained on simulated image data

A system comprising:

• one or more data processors;
• and
• a non-transitory computer readable storage medium containing instructions which when executed on the one or more data processors, cause the one or more data processors to perform actions including:
• identifying a set of states represented in a procedural workflow;
• for each state of the set of states:
• accessing one or more base images that corresponds to the state;
• and
• generating, for each base image of the one or more base images, image-segmentation data;
• identifying a set of target rendering specifications, wherein the set of target rendering specifications include, for each image-parameter variable of one or more image-parameter variables, multiple different variable values for the image-parameter variable;
• generating a set of virtual images based on the set of target rendering specifications and the one or more base images, wherein, for each of the set of states, the set of virtual images includes at least one virtual image based on the base image that corresponds to the state;
• generating, for each virtual image of the set of virtual images, corresponding data that includes an indication of the state of the set of states with which the virtual image is associated;
• transferring, for each virtual image of at least some of the set of virtual images, an image style to the virtual image to generate a stylized virtual image;
• training a machine-learning model using a plurality of stylized virtual images to define a set of parameter values;
• accessing a real image;
• processing the real image via execution of the trained machine-learning model using the set of parameter values;
• generating, based on the processing, an output that corresponds to a particular state of the set of states;
• and
• presenting or transmitting the output.

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including:

• identifying a set of states represented in a procedural workflow;
• for each state of the set of states:
• accessing one or more base images that corresponds to the state;
• and
• generating, for each base image of the one or more base images, image-segmentation data;
• identifying a set of target rendering specifications, wherein the set of target rendering specifications include, for each image-parameter variable of one or more image-parameter variables, multiple different variable values for the image-parameter variable;
• generating a set of virtual images based on the set of target rendering specifications and the one or more base images, wherein, for each of the set of states, the set of virtual images includes at least one virtual image based on the base image that corresponds to the state;
• generating, for each virtual image of the set of virtual images, corresponding data that includes an indication of the state of the set of states with which the virtual image is associated;
• transferring, for each virtual image of at least some of the set of virtual images, an image style to the virtual image to generate a stylized virtual image;
• training a machine-learning model using a plurality of stylized virtual images to define a set of parameter values;
• accessing a real image;
• processing the real image via execution of the trained machine-learning model using the set of parameter values;
• generating, based on the processing, an output that corresponds to a particular state of the set of states;
• and
• presenting or transmitting the output.