Abstract: A system includes a hardware processor and a memory storing software code. The hardware processor executes the software code to receive a three-dimensional (3-D) image including a plurality of pixels each having a plurality of bins with respective depth values, select, for a first bin of the plurality of bins of a first pixel, a second bin in each of one or more nearest neighbor pixels of the first pixel, the second bin in each of the one or more nearest neighbor pixels having a most similar depth value to a depth value of the first bin. The hardware processor further executes the software code to generate a first depth-aware bin group including the first bin and the second bin in each of the one or more nearest neighbor pixels, and process, using the first depth-aware bin group, the 3-D image to produce a corresponding 3-D output image.

Abstract: A system includes a pre-processor configured to receive three-dimensional (3-D) image data, flatten the 3-D image data to produce corresponding two-dimensional (2-D) image data, and concatenate the 3-D image data and the corresponding 2-D image data to provide concatenated image data. The system further includes an encoder including one or more first neural networks (NNs), the encoder configured to use the one or more first NNs to encode the concatenated image data to provide encoded data, a decoder including one or more second NNs, the decoder configured to use the one or more second NNs to decode the encoded data to provide decoded data, and a reconstructor including a plurality of hybrid 2-D/3-D reconstructors configured to reconstruct the decoded data to provide a denoised 3-D output image corresponding to the 3-D image data.

Abstract: Techniques are disclosed for characterizing audience engagement with one or more characters in a media content item. In some embodiments, an audience engagement characterization application processes sensor data; such as video data capturing the faces of one or more audience members consuming a media content item, to generate an audience emotion signal. The characterization application also processes the media content item to generate a character emotion signal associated with one or more characters in the media content item. Then, the characterization application determines an audience engagement score based on an amount of alignment and/or misalignment between the audience emotion signal and the character emotion signal.

Abstract: Techniques are disclosed for resampling images. In some embodiments, a resampling model includes (1) one or more feature extraction layers that extract features from an input image and a degradation map; (2) one or more resampling layers that generate warped features from the extracted features and a warp grid; and (3) one or more prediction layers that generate, from the warped features, an output image or resampling kernels that can be applied to the input image to generate an output image. In some embodiments, the resampling model can be trained by applying degradation maps to output images in a training data set to generate corresponding input images, and training the resampling model using the input images and the corresponding output images.

Abstract: Certain aspects of the present disclosure provide techniques for adaptive sampling for rendering using deep learning. This includes receiving, at a sampler in a rendering pipeline, a plurality of rendered pixel data, wherein the sampler includes a first machine learning (ML) model. It further includes generating a sampling map for the rendering pipeline using the first ML model and the plurality of rendered pixel data, including predicting a plurality of pixel values in the sampling map based on a generated distribution of pixel values. It further includes rendering an image using the sampler, the sampling map, and a denoiser in the rendering pipeline.

Abstract: Techniques are disclosed for characterizing audience engagement with one or more characters in a media content item. In some embodiments, an audience engagement characterization application processes sensor data, such as video data capturing the faces of one or more audience members consuming a media content item, to generate an audience emotion signal. The characterization application also processes the media content item to generate a character emotion signal associated with one or more characters in the media content item. Then, the characterization application determines an audience engagement score based on an amount of alignment and/or misalignment between the audience emotion signal and the character emotion signal.