Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection

While the voxel-based methods have achieved promising results for multi-person 3D pose estimation from multi-cameras, they suffer from heavy computation burdens, especially for large scenes. We present Faster VoxelPose to address the challenge by re-projecting the feature volume to the three two-dimensional coordinate planes and estimating X, Y, Z coordinates from them separately. To that end, we first localize each person by a 3D bounding box by estimating a 2D box and its height based on the volume features projected to the xy-plane and z-axis, respectively. Then for each person, we estimate partial joint coordinates from the three coordinate planes separately which are then fused to obtain the final 3D pose. The method is free from costly 3D-CNNs and improves the speed of VoxelPose by ten times and meanwhile achieves competitive accuracy as the state-of-the-art methods, proving its potential in real-time applications.Comment: 22 pages, 7 figures, submitted to ECCV 202

Downscaling estimation of NEP in the ecologically-oriented county based on multi-source remote sensing data

Net ecosystem productivity (NEP) serves as a pivotal metric for quantitatively elucidating the carbon sink function of terrestrial ecosystems. As a prototype county for the development of an ecological civilization in China, the quantitative estimation of the ecotypic county’s ecosystem carbon sink capacity holds immense significance in comprehending the carbon cycle and facilitating the sustainable advancement of regional ecosystems. This study undertook the estimation of NEP in Wuning County from 2000 to 2020, employing a fusion of multi-source remote sensing data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the improved Carnegie-Ames-Stanford Approach model, and the soil respiration model. Furthermore, we delved into the differences in NEP across various types of land cover. In addition, we employed the Theil-Sen Median trend analysis and Mann-Kendall test to discern the spatio-temporal trends of NEP. The findings indicated the following: (1) The downscaled NDVI generated by STARFM exhibited a remarkable consistency with Landsat NDVI overall (R2 > 0.95, P  grassland > cropland. The application of STARFM has provided valuable insights into the methodology for precise delineation of spatio-temporal dynamics of NEP at the county scale. The outcomes of this study have furnished support for implementing climate change mitigation strategies in ecologically-oriented counties and the bottom-up promotion of China's carbon peaking and carbon neutrality goals

Moisture-Resilient Graphene-Dyed Wool Fabric for Strain Sensing

E-textile consisting of natural fabrics has become a promising material to construct wearable sensors due to its comfortability and breathability on the human body. However, the reported fabric-based e-textile materials, such as graphene-treated cotton, silk, and flax, generally suffer from the electrical and mechanical instability in long-term wearing. In particular, fabrics on the human body have to endure heat variation, moisture evaporation from metabolic activities, and even the immersion with body sweat. To face the above challenges, here we report a wool-knitted fabric sensor treated with graphene oxide (GO) dyeing followed by l-ascorbic acid (l-AA) reduction (rGO). This rGO-based strain sensor is highly stretchable, washable, and durable with rapid sensing response. It exhibits excellent linearity with more than 20% elongation and, most importantly, withstand moisture from 30 to 90% (or even immersed with water) and still maintains good electrical and mechanical properties. We further demonstrate that, by integrating this proposed material with the near-field communication (NFC) system, a batteryless, wireless wearable body movement sensor can be constructed. This material can find wide use in smart garment applications

Freestanding 3D Mesostructures, Functional Devices, and Shape-Programmable Systems Based on Mechanically Induced Assembly with Shape Memory Polymers

Capabilities for controlled formation of sophisticated 3D micro/nanostructures in advanced materials have foundational implications across a broad range of fields. Recently developed methods use stress release in prestrained elastomeric substrates as a driving force for assembling 3D structures and functional microdevices from 2D precursors. A limitation of this approach is that releasing these structures from their substrate returns them to their original 2D layouts due to the elastic recovery of the constituent materials. Here, a concept in which shape memory polymers serve as a means to achieve freestanding 3D architectures from the same basic approach is introduced, with demonstrated ability to realize lateral dimensions, characteristic feature sizes, and thicknesses as small as ≈500, 10, and 5 µm simultaneously, and the potential to scale to much larger or smaller dimensions. Wireless electronic devices illustrate the capacity to integrate other materials and functional components into these 3D frameworks. Quantitative mechanics modeling and experimental measurements illustrate not only shape fixation but also capabilities that allow for structure recovery and shape programmability, as a form of 4D structural control. These ideas provide opportunities in fields ranging from micro-electromechanical systems and microrobotics, to smart intravascular stents, tissue scaffolds, and many others