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资料链接：https://mp.weixin.qq.com/s/CH46uvOshGss7O2bbxFoWQ
自动化所智能感知与计算研究中心在生成对抗网络（GAN）基础上提出高保真度的姿态不变模型来克服人脸识别任务中最为经典的姿态不一致问题。该模型不仅在多个基准数据集的视觉效果和定量指标都优于目前已有的基于生成对抗网络的方法，而且将生成图像的分辨率在原有基础上提升了一倍。该论文已被神经信息处理系统大会(NIPS)收录。
讲者介绍
曹杰：中国科学院自动化研究所智能感知与计算研究中心成员，导师为孙哲南研究员， 研究方向生物特征识别、生成式对抗网络等。
报告题目：Rotating is Believing
报告摘要：Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful results and preserve texture details in a high-resolution. This paper proposes a High Fidelity Pose Invariant Model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose Adversarial Residual Dictionary Learning (ARDL) to supervise facial texture map recovering with only monocular images. Exhaustive experiments on both controlled and uncontrolled environments demonstrate that the proposed method not only boosts the performance of pose-invariant face recognition but also dramatically improves high-resolution frontalization appearances.
参考资料
https://www.bilibili.com/video/BV1Lt411X7GT/
https://bbs.sffai.com/d/14-rotating-is-believing