Name
Abstract | ||
|---|---|---|
In this paper, we present a novel automatic pipeline to build personalized parametric models of dynamic people using a single RGB camera. Compared to previous approaches that use monocular RGB images, our system can model a 3D human body automatically and incrementally, taking advantage of human motion. Based on coarse 2D and 3D poses estimated from image sequences, we first perform a kinematic classification of human body parts to refine the poses and obtain reconstructed body parts. Next, a personalized parametric human model is generated by driving a general template to fit the body parts and calculating the non-rigid deformation. Experimental results show that our shape estimation method achieves comparable accuracy with reconstructed models using depth cameras, yet requires neither user interaction nor any dedicated devices, leading to the feasibility of using this method on widely available smart phones. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/s16030402 | SENSORS |
Keywords | Field | DocType |
structure from motion,pose estimation,single RGB camera,non-rigid reconstruction,SCAPE,motion classification | Structure from motion,Computer vision,Parametric model,Kinematics,Pose,Parametric statistics,RGB color model,Artificial intelligence,Engineering,Monocular,Human body | Journal |
Volume | Issue | ISSN |
16 | 3.0 | 1424-8220 |
Citations | PageRank | References |
5 | 0.43 | 20 |
Authors | ||
4 | ||