Abstract: Systems and methods are described for generating images of synthesized bodies wearing a garment. For instance, a source image of a human or mannequin wearing a garment may be submitted to a synthesized human generation system. In response to receiving the source image, the synthesized human generation system may use a classifier to classify the image as depicting one or more body types or orientations. The synthesized human generation system may also apply segmentation to the source image to segment the garment pixels. The synthesized human generation system may then select one or more body generation machine learning models based on the classification of the source image. The synthesized human generation system may utilize the selected machine learning models to generate one or more output images of synthesized bodies that appear to be wearing the garment, using the segmented garment as input.

Abstract: A computer-implemented method for mixture distribution estimation of multi-modal future predictions comprising a training phase of a convolutional neural network comprising the steps of: (1) inputting a set of images of a driving environment, each containing at least one object of interest, and a set of future ground truths corresponding to the objects of interest; (2) sampling the solution space of the multi-modal future of the object of interest with an evolving winner-takes-all loss strategy by generating a predetermined number of hypotheses, penalizing all hypotheses equally, gradually releasing one part of the hypotheses by penalizing only the other part of the hypotheses being closer to the corresponding ground truth, so-called winning hypotheses, until only the best hypothesis being the closest one is penalized, and outputting final hypotheses; (3) sequentially fitting a multi-modal mixture distribution of future predictions to the final hypotheses.

Abstract: A computer-implemented method for multimodal egocentric future prediction in a driving environment of an autonomous vehicle (AV) or an advanced driver assistance system (ADAS) equipped with a camera and comprising a trained reachability prior deep neural network (RPN), a trained reachability transfer deep neural network (RTN) and a trained future localization deep neural network (FLN) and/or a trained future emergence prediction deep neural network (EPN).

Abstract: A computer-implemented method for mixture distribution estimation of multi-modal future predictions comprising a training phase of a convolutional neural network comprising the steps of: (1) inputting a set of images of a driving environment, each containing at least one object of interest, and a set of future ground truths corresponding to the objects of interest; (2) sampling the solution space of the multi-modal future of the object of interest with an evolving winner-takes-all loss strategy by generating a predetermined number of hypotheses, penalizing all hypotheses equally, gradually releasing one part of the hypotheses by penalizing only the other part of the hypotheses being closer to the corresponding ground truth, so-called winning hypotheses, until only the best hypothesis being the closest one is penalized, and outputting final hypotheses; (3) sequentially fitting a multi-modal mixture distribution of future predictions to the final hypotheses.