MimiCAT：基于对应感知级联 Transformer 的类别无关 3D 姿态迁移
MimiCAT: Mimic with Correspondence-Aware Cascade-Transformer for Category-Free 3D Pose Transfer
提出基于语义关键点标签的软对应匹配机制，将姿态迁移建模为条件生成过程，可复用于跨类别形状对应与变形迁移任务。
原文依据 MimiCAT leverages semantic keypoint labels to learn a novel soft correspondence that enables flexible many-to-many matching across characters.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论3D 姿态迁移旨在将源网格的姿态风格迁移到目标角色上，同时保持目标的几何形状和源的姿态特征。现有方法大多局限于结构相似的角色，无法泛化到类别无关的场景（例如将人形角色的姿态迁移到四足动物）。关键挑战在于不同角色类型固有的结构和变换多样性，这往往导致区域不匹配和迁移质量低下。为解决这些问题，我们首先构建了一个涵盖数百种不同角色的百万级姿态数据集。进一步提出 MimiCAT，一种用于类别无关 3D 姿态迁移的级联 Transformer 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一种新颖的软对应关系，从而实现跨角色的灵活多对多匹配。随后，姿态迁移被建模为条件生成过程，其中源变换首先通过软对应匹配投影到目标上，然后利用形状条件表示进行细化。大量定性和定量实验表明，MimiCAT 能够在多样化的角色形态之间泛化出合理的姿态，超越了先前局限于窄类别迁移（例如人形到人形）的方法。
英文原文摘要 1437 字符
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).