无需动物表情数据，将人脸表情迁移到3D动物脸 mesh 上
Learning Domain Agnostic Latent Embeddings of 3D Faces for Zero-shot Animal Expression Transfer
- 用几何特征与解耦嵌入分离身份与表情
- 仅用人脸数据训练，实现跨物种表情迁移
- 通过几何一致性损失提升迁移真实感
我们提出一种零样本框架，将人类面部表情迁移到3D动物脸网格。方法结合固有几何描述符（HKS/WKS）与网格无关的潜在嵌入，解耦面部身份与表情。身份潜在空间捕捉跨物种的共性面部结构，表情潜在空间编码可泛化的变形模式。仅使用人类表情配对数据训练，模型即可学习嵌入、解耦与重构跨身份表情，实现无需动物表情数据的表达迁移。为保证几何一致性，引入雅可比损失、顶点位置损失与拉普拉斯损失。实验表明，该方法能实现合理跨物种表情迁移，有效缩小人类与动物面部形状间的几何差异。
原文摘要 · Abstract (English)
We present a zero-shot framework for transferring human facial expressions to 3D animal face meshes. Our method combines intrinsic geometric descriptors (HKS/WKS) with a mesh-agnostic latent embedding that disentangles facial identity and expression. The ID latent space captures species-independent facial structure, while the expression latent space encodes deformation patterns that generalize across humans and animals. Trained only with human expression pairs, the model learns the embeddings, decoupling, and recoupling of cross-identity expressions, enabling expression transfer without requiring animal expression data. To enforce geometric consistency, we employ Jacobian loss together with vertex-position and Laplacian losses. Experiments show that our approach achieves plausible cross-species expression transfer, effectively narrowing the geometric gap between human and animal facial shapes.
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