We present a customized 3D mesh Transformer model for the pose transfer task. As the 3D pose transfer essentially is a deformation procedure dependent on the given meshes, the intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. Specifically, we propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes. Moreover, locally, a simple yet efficient central geodesic contrastive loss is further proposed to improve the regional geometric-inconsistency learning. At last, we present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task towards unknown spaces. The massive experimental results prove the efficacy of our approach by showing state-of-the-art quantitative performances on SMPL-NPT, FAUST and our new proposed dataset SMG-3D datasets, as well as promising qualitative results on MG-cloth and SMAL datasets. It's demonstrated that our method can achieve robust 3D pose transfer and be generalized to challenging meshes from unknown spaces on cross-dataset tasks. The code and dataset are made available. Code is available: https://github.com/mikecheninoulu/CGT.
翻译:我们为配置配置任务展示了一个定制的 3D 网目变换模型。 由于 3D 显示的转换基本上是一个取决于给定的模件的变形程序, 这项工作的直觉是看到给定的模件与强大的自我注意机制之间的几何不一致。 具体地说, 我们提议了一个新型的几何- 调动变异器, 具有高效的 3D 结构化的感知能力, 以洞察到给给定的模件之间的全球几何差异。 此外, 在当地, 进一步提议了一个简单而高效的中央大地对比损失, 以改善区域几何不一致性学习。 最后, 我们展示了一个潜值的偏差校正校正化模块, 与一个新型半合成的半合成数据集一起, 将3D 构成向未知空间的转移任务。 大规模实验结果证明了我们的方法的功效, 展示了SMPL- NPT、 FAustust 和我们新提议的数据集 SMG-3D 3D 的定量表现, 以及预示的MG- 和 SMAL 数据集/ 数据集的定性结果。 它展示了我们的方法能够实现3D 3D 的通用数据转换为具有挑战性的数据转换。