In this paper, we proposed a generative model that learns to synthesize the 4D facial expression with the neutral landmark. Existing works mainly focus on the generation of sequences guided by expression labels, speech, etc, while they are not robust to the change of different identities. Our LM-4DGAN utilizes neutral landmarks to guide the facial expression generation while adding an identity discriminator and a landmark autoencoder to the basic WGAN for achieving better identity robustness. Furthermore, we add a cross-attention mechanism to the existing displacement decoder which is suitable for the given identity.

翻译：本文提出了一种生成模型，该模型学习利用中性地标合成四维面部表情。现有研究主要集中于通过表情标签、语音等引导序列生成，但对不同身份变化的鲁棒性不足。我们的LM-4DGAN利用中性地标引导面部表情生成，同时在基础WGAN中引入身份判别器与地标自编码器以提升身份鲁棒性。此外，我们在现有位移解码器中增加了适用于特定身份的交叉注意力机制。