Recurrent 3D Pose Sequence Machines

3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherently ambiguous from the monocular imagery. It is thus critical to exploit rich spatial and temporal long-range dependencies among body joints for accurate 3D pose sequence prediction. Existing approaches usually manually design some elaborate prior terms and human body kinematic constraints for capturing structures, which are often insufficient to exploit all intrinsic structures and not scalable for all scenarios. In contrast, this paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically learn the image-dependent structural constraint and sequence-dependent temporal context by using a multi-stage sequential refinement. At each stage, our RPSM is composed of three modules to predict the 3D pose sequences based on the previously learned 2D pose representations and 3D poses: (i) a 2D pose module extracting the image-dependent pose representations, (ii) a 3D pose recurrent module regressing 3D poses and (iii) a feature adaption module serving as a bridge between module (i) and (ii) to enable the representation transformation from 2D to 3D domain. These three modules are then assembled into a sequential prediction framework to refine the predicted poses with multiple recurrent stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset show that our RPSM outperforms all state-of-the-art approaches for 3D pose estimation.Comment: Published in CVPR 201

Polypyrrole and associated hybrid nanocomposites as chemiresistive gas sensors: A comprehensive review

The detection of toxic and flammable gases entwines a wide diversity of application purposes, such as medical diagnosis, food quality monitoring, environmental tracking, and so forth. In recent, polypyrrole (PPy) nanostructure and its hybrid composite have been emerged as an auspicious gas sensing element because of their unique physicochemical attributes. This review article demonstrates a comprehensive outlook of rapid progress in polypyrrole (PPy) and associated hybrid composite based chemiresistive gas sensors till now. Furthermore, the role of PPy nanostructures and organic or inorganic additives (CNT, graphene or its derivative, metal nanoparticle, metal oxides, metal sulfides) in PPy matrix towards improved gas sensing performance are discussed hereunder. The detailed and systematic discussion on the synthesis strategies, gas sensing principle of the PPy nanostructures, and its composites along with the development of sensor device configuration provide an in-depth understanding of the aforesaid topic to the readers. However, some relevant limitations of PPy and associated composites based gas sensor are addressed after investigating the thorough literature survey. Finally, this article will promote an advanced as well as focused direction to the readers for further development of PPy based high-performance gas sensing devices