Abstract: Aspects of the subject technology provide for human subject Gaussian splatting using machine learning. A method includes receiving a video input having a scene and a subject. The method also includes obtaining a three-dimensional (3D) reconstruction of the subject and the scene from the video input. The method includes generating a 3D Gaussian representation of each of the scene and the subject. The method also includes generating a deformed 3D Gaussian representation of the subject by adapting the 3D Gaussian representation of the subject to the 3D reconstruction of the subject. The method includes rendering a visual output comprising an animatable avatar of the subject and the scene using differentiable Gaussian rasterization based at least in part on the deformed 3D Gaussian representation of the subject and the 3D Gaussian representation of the scene.

Abstract: Systems and processes for training and compressing a convolutional neural network model include the use of quantization and layer fusion. Quantized training data is passed through a convolutional layer of a neural network model to generate convolutional results during a first iteration of training the neural network model. The convolutional results are passed through a batch normalization layer of the neural network model to update normalization parameters of the batch normalization layer. The convolutional layer is fused with the batch normalization layer to generate a first fused layer and the fused parameters of the fused layer are quantized. The quantized training data is passed through the fused layer using the quantized fused parameters to generate output data, which may be quantized for a subsequent layer in the training iteration.

Abstract: Systems and processes for training and compressing a convolutional neural network model include the use of quantization and layer fusion. Quantized training data is passed through a convolutional layer of a neural network model to generate convolutional results during a first iteration of training the neural network model. The convolutional results are passed through a batch normalization layer of the neural network model to update normalization parameters of the batch normalization layer. The convolutional layer is fused with the batch normalization layer to generate a first fused layer and the fused parameters of the fused layer are quantized. The quantized training data is passed through the fused layer using the quantized fused parameters to generate output data, which may be quantized for a subsequent layer in the training iteration.