Presenting a Deep Motion Blending Approach for Simulating Natural Reach Motions

In this paper, we propose a new machine learning approach for target detection in radar, based solely on measured radar data.. By solely using measured radar data, we remove

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated

We take a motion blending approach and construct motion blending modules with a set of small number of motion capture data for each type of motions: standing movements,

Although this approach is a viable means of goal-based animation it is very destructive to the integrity of the initial motion capture data, in many cases, rendering the

Figure 5: Image series of generated reach motions of the novel DNN approach with varying person’s height as input parameter and a constant reach goal. Figure 3: Illustration

The goal of the work is to create a generative model for stylistic motion modeling and synthesis, based on a neutral motion database and a few of style examples.. Our motion

Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models.. Neural rendering is

We present a low cost and accessible end-to-end framework for 3D modeling and texture capture of Humans using deep neural networks and a single RGB camera.. We generate a texture