We propose a 3D face generative model with local weights to increase the model's variations and expressiveness. The proposed model allows partial manipulation of the face while still learning the whole face mesh. For this purpose, we address an effective way to extract local facial features from the entire data and explore a way to manipulate them during a holistic generation. First, we factorize the latent space of the whole face to the subspace indicating different parts of the face. In addition, local weights generated by non-negative matrix factorization are applied to the factorized latent space so that the decomposed part space is semantically meaningful. We experiment with our model and observe that effective facial part manipulation is possible and that the model's expressiveness is improved.
翻译:我们提出了一个带有本地重量的三维面部变形模型,以增加模型的变形和表达性。 拟议的模型允许部分操控面部, 同时仍然学习整个面部网格。 为此, 我们解决了从整个数据中提取本地面部特征的有效方法, 并探索了在整体一代中对其进行操控的方法。 首先, 我们将整个面部的潜伏空间与子空间的分层空间相乘, 表明面部的不同部分。 此外, 由非负矩阵化因子生成的本地权重被应用到因子化的潜在空间, 以便分解的部位空间具有内涵意义。 我们实验了我们的模型, 并观察到有效的面部部位操控是可能的, 并且模型的表达性得到了改进 。