ABSTRACT
In this paper, we aim to address the challenge of novel view rendering of human performers that wear clothes with complex texture patterns using a sparse set of camera views. Although some recent works have achieved remarkable rendering quality on humans with relatively uniform textures using sparse views, the rendering quality remains limited when dealing with complex texture patterns as they are unable to recover the high-frequency geometry details that are observed in the input views. To this end, we propose HDhuman, which uses a human reconstruction network with a pixel-aligned spatial transformer and a rendering network with geometry-guided pixel-wise feature integration to achieve high-quality human reconstruction and rendering. The designed pixel-aligned spatial transformer calculates the correlations between the input views and generates human reconstruction results with high-frequency details. Based on the surface reconstruction results, the geometry-guided pixel-wise visibility reasoning provides guidance for multi-view feature integration, enabling the rendering network to render high-quality images at 2k resolution on novel views. Unlike previous neural rendering works that always need to train or fine-tune an independent network for a different scene, our method is a general framework that is able to generalize to novel subjects. Experiments show that our approach outperforms all the prior generic or specific methods on both synthetic data and real-world data. Source code and test data will be made publicly available for research purposes.
ABSTRACT
In the last few decades, the world has faced some natural issues, due to which economic growth faces a severe threat. Natural disasters like pandemic outbreaks and man-made disasters like pollution emissions are very frequent in the current times, which also influenced the economic growth, where the institutes could play a primary role in economic growth stimulation. This study aims to analyze the association of public health expenditures, institutional quality, renewable energy, and economic performance in China. This study uses quarterly data covering the period from 1996Q1 to 2020Q4 and employs various time-series estimating approaches. The Augmented Dickey-Fuller estimates asserted that all the variables are stationary at first difference. Also, the Bayer-Hanck combined cointegration validates that all the variables are cointegrated. Employing the three long-run estimators, i.e., Fully Modified Ordinary Least Square, Dynamic Ordinary Least Square, and canonical cointegrating regression, the results asserted public health expenditures and institutional quality (including government efficiency and political stability) significantly enhances economic performance in China. Whereas two indicators of corruption control and regulatory quality do not play any significant role in promoting the economic performance of China. On the contrary, renewable energy is found negatively associated with economic performance. Also, the Pair-wise Granger causality validates mixed causal associations between the study variables. As a developing and fossil energy-dependent economy, this study provides relevant policy implications for maintaining economic growth and rebalancing economic performance in China.
