GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning

Image-based Virtual Try-ON aims to transfer an in-shop garment onto a specific person. Existing methods employ a global warping module to model the anisotropic deformation for different garment parts, which fails to preserve the semantic information of different parts when receiving challenging inputs (e.g, intricate human poses, difficult garments). Moreover, most of them directly warp the input garment to align with the boundary of the preserved region, which usually requires texture squeezing to meet the boundary shape constraint and thus leads to texture distortion. The above inferior performance hinders existing methods from real-world applications. To address these problems and take a step towards real-world virtual try-on, we propose a General-Purpose Virtual Try-ON framework, named GP-VTON, by developing an innovative Local-Flow Global-Parsing (LFGP) warping module and a Dynamic Gradient Truncation (DGT) training strategy. Specifically, compared with the previous global warping mechanism, LFGP employs local flows to warp garments parts individually, and assembles the local warped results via the global garment parsing, resulting in reasonable warped parts and a semantic-correct intact garment even with challenging inputs.On the other hand, our DGT training strategy dynamically truncates the gradient in the overlap area and the warped garment is no more required to meet the boundary constraint, which effectively avoids the texture squeezing problem. Furthermore, our GP-VTON can be easily extended to multi-category scenario and jointly trained by using data from different garment categories. Extensive experiments on two high-resolution benchmarks demonstrate our superiority over the existing state-of-the-art methods.Comment: 8 pages, 8 figures, The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR

Role of NLRP3 inflammasome in diabetes and COVID-19 role of NLRP3 inflammasome in the pathogenesis and treatment of COVID-19 and diabetes NLRP3 inflammasome in diabetes and COVID-19 intervention

2019 Coronavirus Disease (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). A “cytokine storm”, i.e., elevated levels of pro-inflammatory cytokines in the bloodstream, has been observed in severe cases of COVID-19. Normally, activation of the nucleotide-binding oligomeric domain-like receptor containing pyrin domain 3 (NLRP3) inflammatory vesicles induces cytokine production as an inflammatory response to viral infection. Recent studies have found an increased severity of necrobiosis infection in diabetic patients, and data from several countries have shown higher morbidity and mortality of necrobiosis in people with chronic metabolic diseases such as diabetes. In addition, COVID-19 may also predispose infected individuals to hyperglycemia. Therefore, in this review, we explore the potential relationship between NLRP3 inflammatory vesicles in diabetes and COVID-19. In contrast, we review the cellular/molecular mechanisms by which SARS-CoV-2 infection activates NLRP3 inflammatory vesicles. Finally, we propose several promising targeted NLRP3 inflammatory vesicle inhibitors with the aim of providing a basis for NLRP3-targeted drugs in diabetes combined with noncoronary pneumonia in the clinical management of patients

An Electrochemically Prepared Mixed Phase of Cobalt Hydroxide/Oxyhydroxide as a Cathode for Aqueous Zinc Ion Batteries

Cobalt hydroxide is a widely studied electrode material for supercapacitor and alkaline zinc ion batteries. The large interlayer spacing of Co(OH)2 is also attractive to store Zn ions. However, Co(OH)2 is quite unstable in the acidic ZnSO4 electrolyte due to its amphoteric nature. Herein, we synthesized a mixed phase of Co(OH)2/CoOOH via a two-step electrochemical preparation. As the cathode material for an aqueous zinc ion battery (AZIB), Co(OH)2/CoOOH delivered a maximum capacity of 164 mAh g−1 at 0.05 A g−1 and a high energy density of 275 Wh kg−1. Benefiting from the low charge-transfer resistance, a capacity of 87 mAh g−1 was maintained at 1.6 A g−1, showing a good rate performance of the mixed phase. Various spectroscopy analyses and simulations based on the density functional theory (DFT) suggested a higher thermal stability of the mixed phase than pure Co(OH)2, due to its less local structural disorder. The reduced Co-Co and Co-O shells increased the mechanical strength of the mixed phase to accommodate Zn2+ ions and endure the electrostatic repulsion, resulting in an enhanced cycling stability. The mixed phased also delivered a good stability at the current density of 0.05 A g−1. After 200 cycles, a capacity retention of 78% was retained, with high Coulombic efficiencies. These results provide a new route to synthesize high-performance LDH for aqueous zinc ion batteries