𝗗𝗮𝘆-𝟰𝟳𝟴 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 JIFF: Jointly-aligned Implicit Face Function for High-Quality Single View Clothed Human Reconstruction by The University of Hong Kong Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 This paper is published #CVPR 2021. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 👉 This paper addresses the problem of single-view 3D human reconstruction. Recent implicit function-based methods have shown impressive results, but they fail to recover fine face details in their reconstructions.  👉 This largely degrades user experience in applications like 3D telepresence. 👉 In this paper, we focus on improving the quality of face in the reconstruction and propose a novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of the implicit function-based approach and model-based approach.  👉 We employ a 3D morphable face model as our shape prior and compute space-aligned 3D features that capture detailed face geometry information.  👉 Such space-aligned 3D features are combined with pixel-aligned 2D features to jointly predict an implicit face function for high-quality face reconstruction. 👉 We further extend our pipeline and introduce a coarse-to-fine architecture to predict high-quality texture for our detailed face model.  👉 Extensive evaluations have been carried out on public datasets and our proposed JIFF has demonstrates superior performance (both quantitatively and qualitatively) over existing state-of-the-art. #computervision #artificialintelligence #deeplearning #technology #machinelearning #pytorch #tensorflow #data #analytics #datascientist