Talking head video generation aims to animate a human face in a still image with dynamic poses and expressions using motion information derived from a target-driving video, while maintaining the person's identity in the source image. However, dramatic and complex motions in the driving video cause ambiguous generation, because the still source image cannot provide sufficient appearance information for occluded regions or delicate expression variations, which produces severe artifacts and significantly degrades the generation quality. To tackle this problem, we propose to learn a global facial representation space, and design a novel implicit identity representation conditioned memory compensation network, coined as MCNet, for high-fidelity talking head generation.~Specifically, we devise a network module to learn a unified spatial facial meta-memory bank from all training samples, which can provide rich facial structure and appearance priors to compensate warped source facial features for the generation. Furthermore, we propose an effective query mechanism based on implicit identity representations learned from the discrete keypoints of the source image. It can greatly facilitate the retrieval of more correlated information from the memory bank for the compensation. Extensive experiments demonstrate that MCNet can learn representative and complementary facial memory, and can clearly outperform previous state-of-the-art talking head generation methods on VoxCeleb1 and CelebV datasets. Please check our \href{https://github.com/harlanhong/ICCV2023-MCNET}{Project}.

通过学习全局面部表达空间并设计了一个新颖的隐式身份表示条件化内存补偿网络（MCNet），我们提出了一种高保真的谈话头生成方法，能够从训练样本中学习统一的空间面部元记忆库，并通过离散关键点的隐式身份表示来有效查询这个记忆库以获得更相关的信息进行补偿，实验证明MCNet可以学习到代表性和互补的面部记忆，并在VoxCeleb1和CelebV数据集上明显优于现有的谈话头生成方法。

隐式身份表达条件化记忆补偿网络用于说头视频生成