Abstract: A system predicts future positions or vergence depth of the user's eyes and generates gaze contingent content, such as for a head-mounted display (HMD), based on the predicted positions or vergence depth. The system includes an eye tracking controller that creates eye tracking information defining positions of a first eye and a second eye of a user over time. The eye tracking information is input to a neural network model that outputs the predicted positions or vergence depth. The predicted positions or vergence depth is then used to render the gaze contingent content, or to change other configurations of the HMD. Latency between the detection of user eye movement and the output of corresponding content is reduced to provide a more immersive real-time user experience.

Abstract: A compression system includes an encoder and a decoder. The encoder can be deployed by a sender system to encode a tensor for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode and reconstruct the encoded tensor. The encoder receives a tensor for compression. The encoder also receives a quantization mask and probability data associated with the tensor. Each element of the tensor is quantized using an alphabet size allocated to that element by the quantization mask data. The encoder compresses the tensor by entropy coding each element using the probability data and alphabet size associated with the element. The decoder receives the quantization mask data, the probability data, and the compressed tensor data. The quantization mask and probabilities are used to entropy decode and subsequently reconstruct the tensor.