3D Human Motion Generation From The Text Via Gesture Action Classification and The Autoregressive Model

This paper presents a deep network based on unrolling of a diffusion process with morphological Laplacian. The diffusion process is an iterative algorithm that represents time evolution of a partial differential equation with Laplacian. We introduce the morphological Laplacian to the basic diffusion and unwrap to the deep network. The morphological filters are nonlinear operators and have parameters that are referred to as structuring elements. By the training using error back propagation, the network of the morphology can be adapted to image processing applications. Since the morphological filters is realized with addition, max and min, the error due to bit-length of data is not amplified. By this property, the morphological parts of the network are implemented in unsigned 8-bit integer with Single instruction multiple data set (SIMD) to achieve fast computation on the small micro processors. We applied the proposed network to Gaussian denoising and image completion. The results are compared with other denoising algorithm and networks.