论文标题
通过CFAN-VAE的表面无监督的几何分离
Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAE
论文作者
论文摘要
几何分离，内在（即身份）和外部（即姿势）几何形状的潜在代码的分离，是非欧盟数据（例如3D可变形模型）生成模型的重要任务。它为潜在空间提供了更大的解释性，并导致世代相传。这项工作引入了网格功能，共形因子和正常功能（CFAN），以用于网格卷积自动编码器。我们进一步提出了CFAN-VAE，这是一种新型的结构，使用CFAN功能剥夺了身份和摆姿势。在训练过程中，不需要有关身份或姿势的标签信息，CFAN-VAE在不审美的道路上实现了几何分离。我们的全面实验，包括重建，插值，生成和身份/姿势转移，证明了CFAN-VAE在无监督的几何分解范围内实现了最新的表现。我们还成功地检测了网格卷积自动编码器中的几何分离水平，该自动编码器通过将其潜在空间注册到CFAN-VAE的空间直接编码XYZ-COORDINE。
Geometric disentanglement, the separation of latent codes for intrinsic (i.e. identity) and extrinsic(i.e. pose) geometry, is a prominent task for generative models of non-Euclidean data such as 3D deformable models. It provides greater interpretability of the latent space, and leads to more control in generation. This work introduces a mesh feature, the conformal factor and normal feature (CFAN),for use in mesh convolutional autoencoders. We further propose CFAN-VAE, a novel architecture that disentangles identity and pose using the CFAN feature. Requiring no label information on the identity or pose during training, CFAN-VAE achieves geometric disentanglement in an unsupervisedway. Our comprehensive experiments, including reconstruction, interpolation, generation, and identity/pose transfer, demonstrate CFAN-VAE achieves state-of-the-art performance on unsupervised geometric disentanglement. We also successfully detect a level of geometric disentanglement in mesh convolutional autoencoders that encode xyz-coordinates directly by registering its latent space to that of CFAN-VAE.
