RI-Small: Human Shape and Pose from Images

该项目将开发用于恢复图像和视频序列中人体的三维（3D）形状和姿势的新方法。 该方法将使用从2000多人的激光范围扫描中学习的详细3D身体模型。 该方法将对人的形状变化以及由于姿势变化而引起的非刚性形状变化进行建模。 该项目将开发和测试在监控视频序列中，在强照明场景中，从快照集合和不受约束的电视/电影序列中恢复体形的方法。 恢复的身体模型将用于产生各种生物特征测量。大多数图像和视频序列都是人类的，识别人及其动作是计算机视觉的核心问题。 然而，这个问题是具有挑战性的，因为人体是一个复杂的，非刚性的，铰接的结构，可以在姿势，形状和外观上显着变化。 目前的方法主要集中在估计人体姿态，通常忽略了人体形状估计的问题。 该项目将把这些问题放在一起处理，从而产生更强大的解决方案，这将在多个学科中产生广泛的影响。 人体姿态估计目前用于步态分析、特效、游戏开发、人为因素和运动训练等领域。 像这里开发的这样强大的基于视频的系统将把应用范围扩展到家庭娱乐、老人护理、自动驾驶汽车和动物运动分析。 通过扩展以前的方法，还可以估计图像和视频序列中人体的三维形状，该项目将在视频取证，监控，预防医学和特效中实现额外的应用。 更一般地说，像这里开发的那些方法，在复杂的图像和视频流中鲁棒地恢复人的形状和姿势，将适用于对象识别和跟踪中的更广泛的问题。http://www.cs.brown.edu/~black/SCAPE.html

This project will develop new approaches for recovering the three-dimensional (3D) shape and pose of the human body in images and video sequences. The methods will use a detailed 3D body model learned from laser range scans of over 2000 people. The approach will model the shape variation across people as well as the non-rigid shape variation due to changes in pose. The project will develop and test methods for robustly recovering the body shape in surveillance video sequences, in scenes with strong lighting, from collections of snapshots and in unconstrained television/film sequences. The recovered body model will be used to produce a variety of biometric measurements.The majority of images and video sequences are of humans and recognizing people and their actions is a core problem in computer vision. The problem is challenging however because the human body is a complex, non-rigid, and articulated structure that can vary dramatically in pose, shape and appearance. Current methods focus on estimating human pose and typically ignore the problem of human shape estimation. This project will treat these problems together resulting in more robust solutions which will have a wide ranging impact in multiple disciplines. Human pose estimation is currently used in areas such as gait analysis, special effects, game development, human factors, and sports training to name a few. Robust video-based systems like the one developed here will extend the range of applications to home entertainment, elder care, autonomous vehicles and animal movement analysis. By extending previous methods to also estimate the three-dimensional shape of the human body in images and video sequences this project will enable additional applications in video forensics, surveillance, preventative medicine and special effects. More generally, methods like those developed here, that robustly recover the shape and pose of people in complex images and video streams, will be applicable to a wider range of problems in object recognition and tracking.Project website: http://www.cs.brown.edu/~black/SCAPE.html