Recent progress in mesh recovery has been restricted to images of non-obese people . We show our method acts as adrop-in to improve the performance of contemporary meshestimation methods . A key innovation of this technique is that it does not rely onsupervision from expensive-to-create mesh parameters . We conduct extensive experiments on multiple datasets .comprising both standard and obese person images and demonstrate the efficacyof our proposed techniques . We present a generalized human meshoptimization algorithm that substantially improves the . performance of existing methods on both obese . person images as well as community-standard benchmark .datasets. We conduct . experiments on . multiple datasetscomprising . both standard . and obese people images and demonstrated the efficacy of our proposed . proposed techniques. We demonstrate the effectiveness of our . proposed methods. We present our proposed methods to demonstrate the . efficacy of their proposed techniques’s proposed techniques and demonstrate their . proposed policies. We also demonstrate the. efficacyof their proposed

Author(s) : Ren Li, Meng Zheng, Srikrishna Karanam, Terrence Chen, Ziyan Wu

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Keywords : proposed - demonstrate - methods - images - obese -

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