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In this paper we present a novel autonomous pipeline to build a personalized parametric model (pose-driven avatar) using a single depth sensor. Our method first captures a few high-quality scans of the user rotating herself at multiple poses from different views. We fit each incomplete scan using template fitting techniques with a generic human template, and register all scans to every pose using global consistency constraints. After registration, these watertight models with different poses are used to train a parametric model in a fashion similar to the SCAPE method. Once the parametric model is built, it can be used as an animitable avatar or more interestingly synthesizing dynamic 3D models from single-view depth videos. Experimental results demonstrate the effectiveness of our system to produce dynamic models.

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We have developed manufacturable approaches for forming single, vertically aligned carbon nanotubes, where the tubes are centered precisely, and placed within a few hundred nm of 1-1.5 μm deep trenches. These wafer-scale approaches were enabled by using chemically amplified resists and high density, low pressure plasma etching techniques to form the 3D nanoscale architectures. The tube growth was performed using dc plasma-enhanced chemical vapor deposition (PECVD), and the materials used in the pre-fabricated 3D architectures were chemically and structurally compatible with the high temperature (700 • C) PECVD synthesis of our tubes, in an ammonia and acetylene ambient. Such scalable, high throughput top-down fabrication processes, when integrated with the bottom-up tube synthesis techniques, should accelerate the development of plasma grown tubes for a wide variety of applications in electronics, such as nanoelectromechanical systems, interconnects, field emitters and sensors.

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