End to End Face Reconstruction via Differentiable PnP

Autor:	Lu, Yiren, Wei, Huawei
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-031-25072-9_28
Popis:	This is a challenge report of the ECCV 2022 WCPA Challenge, Face Reconstruction Track. Inside this report is a brief explanation of how we accomplish this challenge. We design a two-branch network to accomplish this task, whose roles are Face Reconstruction and Face Landmark Detection. The former outputs canonical 3D face coordinates. The latter outputs pixel coordinates, i.e. 2D mapping of 3D coordinates with head pose and perspective projection. In addition, we utilize a differentiable PnP (Perspective-n-Points) layer to finetune the outputs of the two branch. Our method achieves very competitive quantitative results on the MVP-Human dataset and wins a $3^{rd}$ prize in the challenge. Comment: Accepted by ECCV2022 workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.14249 Zobrazit plný text záznamu View this record from Arxiv