With the strength of deep generative models, 3D pose transfer regains intensive research interests in recent years. Existing methods mainly rely on a variety of constraints to achieve the pose transfer over 3D meshes, e.g., the need for the manually encoding for shape and pose disentanglement. In this paper, we present an unsupervised approach to conduct the pose transfer between any arbitrate given 3D meshes. Specifically, a novel Intrinsic-Extrinsic Preserved Generative Adversarial Network (IEP-GAN) is presented for both intrinsic (i.e., shape) and extrinsic (i.e., pose) information preservation. Extrinsically, we propose a co-occurrence discriminator to capture the structural/pose invariance from distinct Laplacians of the mesh. Meanwhile, intrinsically, a local intrinsic-preserved loss is introduced to preserve the geodesic priors while avoiding the heavy computations. At last, we show the possibility of using IEP-GAN to manipulate 3D human meshes in various ways, including pose transfer, identity swapping and pose interpolation with latent code vector arithmetic. The extensive experiments on various 3D datasets of humans, animals and hands qualitatively and quantitatively demonstrate the generality of our approach. Our proposed model produces better results and is substantially more efficient compared to recent state-of-the-art methods. Code is available: https://github.com/mikecheninoulu/Unsupervised_IEPGAN.
翻译:随着深层基因模型的强度, 3D 代表近年来的转移将重新获得密集的研究兴趣。 现有方法主要依靠各种限制来实现3Dmeses 的外形转换, 例如, 需要手工编码形状和造成分解。 在本文中, 我们展示了一种不受监督的方法来进行任何3D meshes 提供的仲裁之间的外形转换。 具体地说, 一个全新的 Intrinsic-Exprrinsic Preservid General Aversarial Network ( IEP- GAN), 既用于内在( 形状) 也用于 Exprintsion 3Dmmmmeshes ( e. depossicialike) 的信息保存 。 我们提议了一种共同的辨别性区分, 来捕捉到来自不同面的面部之间的结构/ 。 同时, 一个本地的内置的内置值损失被引入来保存地理学前的模型, 同时避免重算。 最后, 我们展示了使用 IEP- GAN 的可能性, 来以不同的方式, 管理 3D 的最近人类的内置的内置的内置的内置的内置,,, 和内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置,, 和内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置的内置 。