Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction
ECCV 2020的內容，屬於幾何深度學習，同時隸屬於當下火熱的方法隱式函數,下文是本篇論文的讀書筆記。 文獻一開始提出了隱式函數在進行生成的時候的缺點: （1）生成的物體表面是靜態的 （2）模型的表面不能進行編輯 作者提出的方法結合了隱式函數對模型細節的表達能力+對模型參數化的表達從而去重建人體的三維模型。（讓surface與相應的語義對應） 本篇paper的核心目標: In this pape
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相關文章
- 1. Deep Learning Models and Code for Pose Estimation
- 2. Camera Calibration and 3D Reconstruction
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- 5. Image-based 3D Object Reconstruction
- 6. 論文：3D Convolutional Neural Networks for Human Action Recognition
- 7. Structure and Function of Human Eye
- 8. 【3D 目標檢測】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
- 9. (3D-HPE)Learning to Estimate 3D Human Pose and Shape from a Single Color Image
- 10. 論文解讀之——《Learning Beyond Human Expertise with Generative Models for Dental Restorations》