We introduce MIGS (Multi-Identity Gaussian Splatting), a novel method that learns a single neural representation for multiple identities, using only monocular videos. Recent 3D Gaussian Splatting (3DGS) approaches for human avatars require per-identity optimization. However, learning a multi-identity representation presents advantages in robustly animating humans under arbitrary poses. We propose to construct a high-order tensor that combines all the learnable 3DGS parameters for all the training identities. By assuming a low-rank structure and factorizing the tensor, we model the complex rigid and non-rigid deformations of multiple subjects in a unified network, significantly reducing the total number of parameters. Our proposed approach leverages information from all the training identities, enabling robust animation under challenging unseen poses, outperforming existing approaches. We also demonstrate how it can be extended to learn unseen identities.
我们介绍了 MIGS（多身份高斯散射），这是一种新型方法，仅使用单目视频就能学习多个身份的单一神经表示。最近的用于人体头像的 3D 高斯散射（3DGS）方法需要针对每个身份进行优化。然而，学习多身份表示在任意姿势下稳健地为人体制作动画方面具有优势。 我们提出构建一个高阶张量，该张量结合了所有训练身份的所有可学习 3DGS 参数。通过假设低秩结构并对张量进行因子分解，我们在一个统一的网络中建模多个主体的复杂刚性和非刚性变形，显著减少了总参数数量。 我们提出的方法利用了所有训练身份的信息，使其能够在具有挑战性的未见姿势下进行稳健的动画制作，优于现有方法。我们还展示了如何将其扩展到学习未见身份。