An image rendering method for a virtual scene, comprising the steps of: for an image of the virtual scene rendered at a particular virtual camera viewpoint; for a plurality of identifiers of virtual scene elements, 'IDs', generating a respective binary mask of the image identifying elements of the virtual scene that are associated with a respective ID, wherein a value of a given pixel in the respective binary mask is set to one if the given pixel is associated with the respective ID, or is set to zero otherwise; for the resulting plurality of masks, dividing a respective binary mask into a plurality of tiles; and discarding tiles that do not identify any elements of the virtual scene that are associated with the respective ID, wherein each discarded tile comprises pixels whose values are all set to zero; for the resulting plurality of remaining tiles, selecting a respective trained machine learning model from among a plurality of machine learning models, the respective machine learning model having been trained to generate radiance data contributing to a render of at least a part of an image, based upon elements of the scene associated with the same respective ID as the elements identified in the mask from which the respective tile was divided; and using the respective trained machine learning model to generate respective radiance data contributing to a render of at least a part of the image based upon position input data and direction input data at least for the identified elements in the respective tile, where the position input is a three-dimensional position of the identified element in the virtual scene, and the direction input incorporates information about a viewing direction of the virtual camera viewpoint. for an image of the virtual scene rendered at a particular virtual camera viewpoint; for a plurality of identifiers of virtual scene elements, 'IDs', generating a respective binary mask of the image identifying elements of the virtual scene that are associated with a respective ID, wherein a value of a given pixel in the respective binary mask is set to one if the given pixel is associated with the respective ID, or is set to zero otherwise; for the resulting plurality of masks, dividing a respective binary mask into a plurality of tiles; and discarding tiles that do not identify any elements of the virtual scene that are associated with the respective ID, wherein each discarded tile comprises pixels whose values are all set to zero; dividing a respective binary mask into a plurality of tiles; and discarding tiles that do not identify any elements of the virtual scene that are associated with the respective ID, wherein each discarded tile comprises pixels whose values are all set to zero; for the resulting plurality of remaining tiles, selecting a respective trained machine learning model from among a plurality of machine learning models, the respective machine learning model having been trained to generate radiance data contributing to a render of at least a part of an image, based upon elements of the scene associated with the same respective ID as the elements identified in the mask from which the respective tile was divided; and using the respective trained machine learning model to generate respective radiance data contributing to a render of at least a part of the image based upon position input data and direction input data at least for the identified elements in the respective tile, where the position input is a three-dimensional position of the identified element in the virtual scene, and the direction input incorporates information about a viewing direction of the virtual camera viewpoint. selecting a respective trained machine learning model from among a plurality of machine learning models, the respective machine learning model having been trained to generate radiance data contributing to a render of at least a part of an image, based upon elements of the scene associated with the same respective ID as the elements identified in the mask from which the respective tile was divided; and using the respective trained machine learning model to generate respective radiance data contributing to a render of at least a part of the image based upon position input data and direction input data at least for the identified elements in the respective tile, where the position input is a three-dimensional position of the identified element in the virtual scene, and the direction input incorporates information about a viewing direction of the virtual camera viewpoint.