3D pose transfer aims to transfer the pose-style of a source mesh to a target character while preserving both the target's geometry and the source's pose characteristic. Existing methods are largely restricted to characters with similar structures and fail to generalize to category-free settings (e.g., transferring a humanoid's pose to a quadruped). The key challenge lies in the structural and transformation diversity inherent in distinct character types, which often leads to mismatched regions and poor transfer quality. To address these issues, we first construct a million-scale pose dataset across hundreds of distinct characters. We further propose MimiCAT, a cascade-transformer model designed for category-free 3D pose transfer. Instead of relying on strict one-to-one correspondence mappings, MimiCAT leverages semantic keypoint labels to learn a novel soft correspondence that enables flexible many-to-many matching across characters. The pose transfer is then formulated as a conditional generation process, in which the source transformations are first projected onto the target through soft correspondence matching and subsequently refined using shape-conditioned representations. Extensive qualitative and quantitative experiments demonstrate that MimiCAT generalizes plausible poses across diverse character morphologies, surpassing prior approaches restricted to narrow-category transfer (e.g., humanoid-to-humanoid).

翻译：三维姿态迁移旨在将源网格的姿态风格迁移至目标角色，同时保留目标的几何特征与源的姿态特性。现有方法主要受限于结构相似的角色，无法泛化至无类别场景（如将人形姿态迁移至四足动物）。关键挑战源于不同角色类型固有的结构与变换多样性，这常导致区域错配及迁移质量低下。为解决这些问题，我们首先构建了涵盖数百种不同角色的百万级姿态数据集，并进一步提出MimiCAT——一种专用于无类别三维姿态迁移的级联变换器模型。MimiCAT不依赖严格的一对一对应映射，而是利用语义关键点标签学习新颖的软对应机制，实现跨角色的灵活多对多匹配。姿态迁移被建模为条件生成过程：源变换首先通过软对应匹配投影至目标，随后经形状条件表示进行精细化处理。大量定性与定量实验表明，MimiCAT能在不同角色形态间生成合理的姿态，显著超越局限于窄类别迁移（如人形到人形）的先前方法。