Learning detailed models from images and videos using machine learning techniques and applications to graphics

现在，合成图像和真实场景的动画在医学、电脑游戏、故事片、电视广告、国土安全和美术等不同领域中很常见。创建这些图像和动画是计算机图形学的首要目标。传统的计算机图形学方法从几何模型开始，然后生成并显示虚拟表示。许多计算机图形学方法都取得了成功。然而，很明显，合成图像和动画看起来仍然是人造的，成本和时间必须降低。在过去，计算机图形学领域一直被认为是计算机视觉的反面。事实上，计算机视觉从输入的图像和视频开始，并对它们进行处理，以理解对象和场景的几何和物理属性。我的研究目标是整合计算机视觉和计算机图形学技术，并创建一个框架，在这个框架中，这两个领域通过机器学习技术进行协作，直接根据测量结果对我们周围的世界(例如人体和运动、自然场景、刚性和非刚性对象)进行建模。这些测量将从真实的图像和场景中学习。事实上，近年来在高质量传感器领域取得了重大的技术发展，简化了世界内容的获取。

Synthetic images and animations of real scenes are now common place in different domains such as medicine, computer games, feature films, TV advertising, homeland security, and fine arts. Creating these images and animation is the first goal of computer graphics. Traditional computer graphics approaches start with geometric models and then generate and display virtual representations. Many computer graphics approaches have been successful. However, it is clear that synthetic images and animation still look artificial and that the cost and time have to be lowered. In the past, the field of computer graphics has been considered as the inverse of computer vision. Indeed, computer vision starts with input images and videos and process them to understand the geometric and physical properties of objects and scenes. The objectives of my research are the integration of computer vision and computer graphics techniques, and the creation of a framework in which these two domains collaborate through machine learning techniques to model the world around us (e.g. human body and motion, natural scenes, rigid and non-rigid objects) directly from measurements. These measurements will be learned from real images and scenes. Indeed, recent years have seen a significant technological development in the areas of high-quality sensors which have simplified the acquisition of the world content.