Optimizations for Dynamic Object Instance Detection, Segmentation, and Structure Mapping

A system comprising:

• one or more processors and one or more computer-readable non-transitory storage media coupled to one or more of the processors, the one or more computer-readable non-transitory storage media comprising instructions operable when executed by one or more of the processors to cause the system to perform operations comprising:
• accessing a plurality of pose probability models for a plurality of predetermined parts of a body that is depicted in an image, respectively, wherein each of the plurality of pose probability models is configured for determining a probability of the associated predetermined part of the body being at a location in the image, wherein the plurality of pose probability models is generated by a machine-learning model;
• determining a candidate pose that is defined by a set of coordinates representing candidate locations of the predetermined parts of the body in the image;
• determining a first probability score for the candidate pose based on the plurality of pose probability models and the set of coordinates of the candidate pose;
• generating a pose representation for the candidate pose using a transformation model and the candidate pose;
• determining a second probability score for the pose representation based on a pose-representation probability model;
• and
• selecting the candidate pose to represent a pose of the body depicted in the image based on at least the first probability score and the second probability score.

One or more computer-readable non-transitory storage media embodying software that is operable when executed to cause one or more processors to perform operations comprising:

• accessing a plurality of pose probability models for a plurality of predetermined parts of a body that is depicted in an image, respectively, wherein each of the plurality of pose probability models is configured for determining a probability of the associated predetermined part of the body being at a location in the image, wherein the plurality of pose probability models is generated by a machine-learning model;
• determining a candidate pose that is defined by a set of coordinates representing candidate locations of the predetermined parts of the body in the image;
• determining a first probability score for the candidate pose based on the plurality of pose probability models and the set of coordinates of the candidate pose;
• generating a pose representation for the candidate pose using a transformation model and the candidate pose;
• determining a second probability score for the pose representation based on a pose-representation probability model;
• and
• selecting the candidate pose to represent a pose of the body depicted in the image based on at least the first probability score and the second probability score.