Multimodal color variations using learned color distributions

A system, comprising:

• at least one processor;
• and
• a memory including instructions stored thereon which, when executed by the at least one processor, cause the system to:
• obtain an input image;
• determine one or more color priors based on the input image;
• encode the one or more color priors into an input tensor that represents color space values and weight values associated with each of the one or more color priors;
• predict, using a first machine learning model, a color space value of a color of a color theme variation, wherein the color is based on the one or more color priors, and wherein the first machine learning model has been trained to model a color distribution of a training dataset, the training dataset including a plurality of color images having different color themes;
• generate a plurality of color theme variations by iteratively predicting one or more colors of each color theme variation of the plurality of color theme variations;
• rank, using a second machine learning model, the plurality of color theme variations to obtain a plurality of ranked color theme variations;
• and
• recolor the input image to generate a plurality of recolored output images using a number of top ranked color theme variations of the plurality of ranked color theme variations, wherein recoloring includes changing pixel color values of the input image according to the plurality of color theme variations.

A system, comprising:

• means for obtaining an input image;
• means for determining one or more color priors based on the input image;
• means for encoding the one or more color priors into an input tensor that represents color space values and weight values associated with each of the one or more color priors;
• means for predicting a color space value of a color of a color theme variation using a machine learning model, wherein the color is based on the one or more color priors and wherein the machine learning model has been trained to model a color distribution of a training dataset, the training dataset including a plurality of color images having different color themes;
• means for generating a plurality of color theme variations by iteratively predicting one or more colors of each color theme variation of the plurality of color theme variations;
• means for ranking the plurality of color theme variations to obtain a plurality of ranked color theme variations;
• and
• means for recoloring the input image to generate a plurality of recolored output images using a number of top ranked color theme variations of the plurality of ranked color theme variations.