A computer-implemented method for decomposing videos into multiple layers that can be re-combined with modified relative timings, the computer-implemented method comprising: obtaining, by a computing system comprising one or more computing devices, video data, the video data comprising a plurality of image frames depicting one or more objects; and characterised by , for each of the plurality of frames: generating, by the computing system, one or more object maps, wherein each of the one or more object maps is descriptive of a respective location of at least one object of the one or more objects within the image frame; inputting, by the computing system, the image frame and the one or more object maps into a machine-learned layer renderer model, comprising iteratively individually inputting each of the one or more object maps into the machine-learned layer renderer model; and receiving, by the computing system as output from the machine-learned layer renderer model, a background layer illustrative of a background of the video data and one or more object layers respectively associated with one of the one or more object maps; wherein, for each image frame, each of the one or more object layers comprises image data illustrative of the at least one object and one or more trace effects at least partially attributable to the at least one object such that the one or more object layers and the background layer can be re-combined with modified relative timings, wherein the one or more trace effects comprise scene changes correlated with an object of the one or more objects. obtaining, by a computing system comprising one or more computing devices, video data, the video data comprising a plurality of image frames depicting one or more objects; and characterised by , for each of the plurality of frames: generating, by the computing system, one or more object maps, wherein each of the one or more object maps is descriptive of a respective location of at least one object of the one or more objects within the image frame; inputting, by the computing system, the image frame and the one or more object maps into a machine-learned layer renderer model, comprising iteratively individually inputting each of the one or more object maps into the machine-learned layer renderer model; and receiving, by the computing system as output from the machine-learned layer renderer model, a background layer illustrative of a background of the video data and one or more object layers respectively associated with one of the one or more object maps; generating, by the computing system, one or more object maps, wherein each of the one or more object maps is descriptive of a respective location of at least one object of the one or more objects within the image frame; inputting, by the computing system, the image frame and the one or more object maps into a machine-learned layer renderer model, comprising iteratively individually inputting each of the one or more object maps into the machine-learned layer renderer model; and receiving, by the computing system as output from the machine-learned layer renderer model, a background layer illustrative of a background of the video data and one or more object layers respectively associated with one of the one or more object maps; wherein, for each image frame, each of the one or more object layers comprises image data illustrative of the at least one object and one or more trace effects at least partially attributable to the at least one object such that the one or more object layers and the background layer can be re-combined with modified relative timings, wherein the one or more trace effects comprise scene changes correlated with an object of the one or more objects. <EOS>