Abstract: A computer-implemented method includes rendering a plurality of two-dimensional views of a three-dimensional object generated by a generative model, using a two-dimensional landmark regressor to process the plurality of two-dimensional views to generate respective sets of two-dimensional landmarks, fitting a set of three-dimensional landmarks to the respective sets of two-dimensional landmarks. The method includes processing at least a first two-dimensional view of the object using a three-dimensional landmark regressor to determine a candidate set of three-dimensional landmarks for the first two-dimensional view of the object, and updating the three-dimensional landmark regressor based at least in part on a loss function comprising a term that evaluates a deviation between the candidate set of three-dimensional landmarks and the fitted set of three-dimensional landmarks.

Abstract: The invention provides an anti-CCR9 antibody molecule for use in a method of treatment of cancer in a mammalian subject wherein the anti-CCR9 antibody molecule is administered simultaneously, sequentially or separately with a chemotherapeutic agent selected from the group consisting of: vincristine, docetaxel, paclitaxel, nanoparticle albumin-bound paclitaxel, and vinblastine, wherein said anti-CCR9 antibody molecule and said chemotherapeutic agent are not conjugated together. Also provided are related methods of treatment.

Abstract: A method includes determining, using a motion tracker, a plurality of face tracks from one or more sequences of image frames. Each face track corresponds to a respective instance of a respective face and includes a respective sequence of image frame crops. The method includes fine-tuning, using the determined plurality of face tracks, a pre-trained face identification model to generate, for image frame crops of a common face track, respective embeddings that have a mutually high degree of similarity as measured by a loss function. The method then includes grouping the plurality of face tracks into common identity clusters based at least in part on similarities, as measured by the loss function, between respective embeddings generated using the fine-tuned face identification model for image frame crops within different face tracks.

Abstract: A computer-implemented method includes selecting an image from a set of images, processing the image using a detector portion of a machine learning model to determine a point cluster comprising points representing candidate keypoints and a set of uncertainties, each uncertainty corresponding to a respective point in the point cluster. Includes using the determined point cluster and the determined set of uncertainties to generate a differentiable map representing the point cluster and a spatial variation of uncertainty associated with the point cluster, and processing the differentiable map and a portion of the image using a decoder portion of the model to generate a candidate reconstructed image. Includes updating the model to reduce a loss function comprising a term penalizing a difference between at least one representation of the image and a corresponding at least one representation of the candidate reconstructed image.

Abstract: A computer-implemented method comprises obtaining a source image and a target image, determining a first point cluster by processing the source image using a machine learning mode, determining a second point cluster by processing the target image using the machine learning model, uplifting the first point cluster to obtain an uplifted first point cluster, uplifting the second point cluster to obtain an uplifted second point cluster, estimating a transformation that maps the uplifted first point cluster to the uplifted second point cluster, applying the estimated transformation to the uplifted first point cluster to obtain a candidate reconstruction of the uplifted second point cluster, and updating the machine learning model to reduce a loss function including a term penalizing a difference between the uplifted second point cluster and the candidate reconstruction of the uplifted second point cluster.

Abstract: A computer-implemented method of processing video data comprising a sequence of image frames. The method includes isolating an instance of an object within the sequence of image frames, generating a modified instance of the object using a machine learning model, and modifying the video data to smoothly transition between at least part of the isolated instance of the object and a corresponding at least part of the modified instance of the object over a subsequence of the sequence of image frames.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method of processing video data comprising a sequence of image frames. The method includes isolating an instance of an object within the sequence of image frames, generating a modified instance of the object using a machine learning model, and modifying the video data to smoothly transition between at least part of the isolated instance of the object and a corresponding at least part of the modified instance of the object over a subsequence of the sequence of image frames.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.