The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervision of the ground truth, whose availability is limited in real-world scenarios. In this work, we present X-DualNet, a simple yet effective approach that enables unsupervised 3D pose transfer. In X-DualNet, we introduce a generator $G$ which contains correspondence learning and pose transfer modules to achieve 3D pose transfer. We learn the shape correspondence by solving an optimal transport problem without any key point annotations and generate high-quality meshes with our elastic instance normalization (ElaIN) in the pose transfer module. With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision. Besides that, we also adopt an as-rigid-as-possible deformer in the training process to fine-tune the body shape of the generated results. Extensive experiments on human and animal data demonstrate that our framework can successfully achieve comparable performance as the state-of-the-art supervised approaches.

翻译：3D姿态迁移的目标是将源网格的姿态迁移至目标网格，同时保留目标网格的身份信息（如面部、体型等）。基于深度学习的方法提升了3D姿态迁移的效率与性能，然而大多数方法需依赖于真实数据的监督训练，而这类数据在现实场景中获取有限。本文提出X-DualNet——一种简洁高效的免监督3D姿态迁移方法。在X-DualNet中，我们设计了生成器$G$，其包含对应关系学习与姿态迁移模块以实现3D姿态迁移。通过求解最优传输问题，我们无需任何关键点标注即可学习形状对应关系，并基于弹性实例归一化（ElaIN）在姿态迁移模块中生成高质量网格。以$G$为基础组件，我们提出跨一致性学习方案与双重建构目标，实现无监督的姿态迁移。此外，我们在训练过程中引入尽可能刚性的形变器，对生成结果的体型进行微调。在人体与动物数据集上的大量实验表明，本框架可达到与主流监督方法相媲美的性能。