[Submitted on 10 Jul 2024]
View a PDF of the paper titled MIGS: Multi-Identity Gaussian Splatting via Tensor Decomposition, by Aggelina Chatziagapi and 2 other authors
Abstract: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.
Submission history
From: Aggelina Chatziagapi [view email]
[v1]
Wed, 10 Jul 2024 00:30:06 UTC (21,452 KB)
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