Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟭𝟴𝟲 Computer Vision Learning 𝗣𝗼𝘀𝗲2𝗠𝗲𝘀𝗵: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose by ECE & ASRI, Seoul National University, Korea Follow me for a similar post:  🇮🇳 Ashish Patel Interesting Facts : 🔸 This is a paper in ECCV2020 with over 31 citations. 🔸 It outperforms HMR, Graph CMR, SPIN etc. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/ePc9WFH Code : https://lnkd.in/eMtkmQ5 ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Most of the recent deep learning-based 3D human pose and mesh estimation methods regress the pose and shape parameters of human mesh models, such as SMPL and MANO, from an input image. 🔸 The first weakness of these methods is an appearance domain gap problem, due to different image appearance between train data from controlled environments, such as a laboratory, and test data from in-the-wild environments. The second weakness is that the estimation of the pose parameters is quite challenging owing to the representation issues of 3D rotations. 🔸To overcome the above weaknesses, we propose Pose2Mesh, a novel graph convolutional neural network (GraphCNN)-based system that estimates the 3D coordinates of human mesh vertices directly from the 2D human pose. 🔸The 2D human pose as input provides essential human body articulation information, while having a relatively homogeneous geometric property between the two domains. Also, the proposed system avoids the representation issues, while fully exploiting the mesh topology using a GraphCNN in a coarse-to-fine manner. #computervision #artificialintelligence #data