A data-driven method was proposed to realistically animate garments on human poses in reduced space. Firstly, a gradient based method was extended to generate motion sequences and garments were simulated on the sequen...A data-driven method was proposed to realistically animate garments on human poses in reduced space. Firstly, a gradient based method was extended to generate motion sequences and garments were simulated on the sequences as our training data. Based on the examples, the proposed method can fast output realistic garments on new poses. Our framework can be mainly divided into offline phase and online phase. During the offline phase, based on linear blend skinning(LBS), rigid bones and flex bones were estimated for human bodies and garments, respectively. Then, rigid bone weight maps on garment vertices were learned from examples. In the online phase, new human poses were treated as input to estimate rigid bone transformations. Then, both rigid bones and flex bones were used to drive garments to fit the new poses. Finally, a novel formulation was also proposed to efficiently deal with garment-body penetration. Experiments manifest that our method is fast and accurate. The intersection artifacts are fast removed and final garment results are quite realistic.展开更多
The North Atlantic Oscillation(NAO)is the most prominent mode of atmospheric variability in the Northern Hemisphere.Because of the close relationship between the NAO and regional climate in Eurasia,North Atlantic,and ...The North Atlantic Oscillation(NAO)is the most prominent mode of atmospheric variability in the Northern Hemisphere.Because of the close relationship between the NAO and regional climate in Eurasia,North Atlantic,and North America,improving the prediction skill for the NAO has attracted much attention.Previous studies that focused on the predictability of the NAO were often based upon simulations by climate models.In this study,the authors took advantage of Slow Feature Analysis to extract information on the driving forces from daily NAO index and introduced it into phase-space reconstruction.By computing the largest Lyapunov exponent,the authors found that the predictability of daily NAO index shows a significant increase when its driving force signal is considered.Furthermore,the authors conducted a short-term prediction for the NAO by using a global prediction model for chaotic time series that incorporated the driving-force information.Results showed that the prediction skill for the NAO can be largely increased.In addition,results from wavelet analysis suggested that the driving-force signal of the NAO is associated with three basic drivers:the annual cycle(1.02 yr),the quasi-biennial oscillation(QBO)(2.44 yr);and the solar cycle(11.6 yr),which indicates the critical roles of the QBO and solar activities in the predictability of the NAO.展开更多
基金Project(20104307110003)supported by the Research Fund for the Doctoral Program of Higher Education of ChinaProjects(61379103,61202333,61303185)supported by the National Natural Science Foundation of China+1 种基金Project(2012M520392)supported by the China Postdoctoral Science FoundationProject(CX2012B027)supported by the Hunan Province Graduate Student Innovation Program,China
文摘A data-driven method was proposed to realistically animate garments on human poses in reduced space. Firstly, a gradient based method was extended to generate motion sequences and garments were simulated on the sequences as our training data. Based on the examples, the proposed method can fast output realistic garments on new poses. Our framework can be mainly divided into offline phase and online phase. During the offline phase, based on linear blend skinning(LBS), rigid bones and flex bones were estimated for human bodies and garments, respectively. Then, rigid bone weight maps on garment vertices were learned from examples. In the online phase, new human poses were treated as input to estimate rigid bone transformations. Then, both rigid bones and flex bones were used to drive garments to fit the new poses. Finally, a novel formulation was also proposed to efficiently deal with garment-body penetration. Experiments manifest that our method is fast and accurate. The intersection artifacts are fast removed and final garment results are quite realistic.
基金supported by the National Key R&D Program of China [grant number 2017YFC1501804]the National Natural Science Foundation of China [grant number41575058]
文摘The North Atlantic Oscillation(NAO)is the most prominent mode of atmospheric variability in the Northern Hemisphere.Because of the close relationship between the NAO and regional climate in Eurasia,North Atlantic,and North America,improving the prediction skill for the NAO has attracted much attention.Previous studies that focused on the predictability of the NAO were often based upon simulations by climate models.In this study,the authors took advantage of Slow Feature Analysis to extract information on the driving forces from daily NAO index and introduced it into phase-space reconstruction.By computing the largest Lyapunov exponent,the authors found that the predictability of daily NAO index shows a significant increase when its driving force signal is considered.Furthermore,the authors conducted a short-term prediction for the NAO by using a global prediction model for chaotic time series that incorporated the driving-force information.Results showed that the prediction skill for the NAO can be largely increased.In addition,results from wavelet analysis suggested that the driving-force signal of the NAO is associated with three basic drivers:the annual cycle(1.02 yr),the quasi-biennial oscillation(QBO)(2.44 yr);and the solar cycle(11.6 yr),which indicates the critical roles of the QBO and solar activities in the predictability of the NAO.
