A Controllable 3D Deepfake Generation Framework with Gaussian Splatting

• URL: http://arxiv.org/abs/2509.11624v1
• Date: Mon, 15 Sep 2025 06:34:17 GMT
• Title: A Controllable 3D Deepfake Generation Framework with Gaussian Splatting
• Authors: Wending Liu, Siyun Liang, Huy H. Nguyen, Isao Echizen,
• Abstract summary: We propose a novel 3D deepfake generation framework based on 3D Gaussian Splatting.<n>It enables realistic, identity-preserving face swapping and reenactment in a fully controllable 3D space.<n>Our approach bridges the gap between 3D modeling and deepfake synthesis, enabling new directions for scene-aware, controllable, and immersive visual forgeries.
• Score: 6.969908558294805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel 3D deepfake generation framework based on 3D Gaussian Splatting that enables realistic, identity-preserving face swapping and reenactment in a fully controllable 3D space. Compared to conventional 2D deepfake approaches that suffer from geometric inconsistencies and limited generalization to novel view, our method combines a parametric head model with dynamic Gaussian representations to support multi-view consistent rendering, precise expression control, and seamless background integration. To address editing challenges in point-based representations, we explicitly separate the head and background Gaussians and use pre-trained 2D guidance to optimize the facial region across views. We further introduce a repair module to enhance visual consistency under extreme poses and expressions. Experiments on NeRSemble and additional evaluation videos demonstrate that our method achieves comparable performance to state-of-the-art 2D approaches in identity preservation, as well as pose and expression consistency, while significantly outperforming them in multi-view rendering quality and 3D consistency. Our approach bridges the gap between 3D modeling and deepfake synthesis, enabling new directions for scene-aware, controllable, and immersive visual forgeries, revealing the threat that emerging 3D Gaussian Splatting technique could be used for manipulation attacks.

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