SelfieAvatar: Real-time Head Avatar reenactment from a Selfie Video

• URL: http://arxiv.org/abs/2601.18851v1
• Date: Mon, 26 Jan 2026 14:26:16 GMT
• Title: SelfieAvatar: Real-time Head Avatar reenactment from a Selfie Video
• Authors: Wei Liang, Hui Yu, Derui Ding, Rachael E. Jack, Philippe G. Schyns,
• Abstract summary: This study introduces a method for detailed head avatar reenactment using a selfie video.<n>A detailed reconstruction model is proposed, incorporating mixed loss functions for foreground reconstruction and avatar image generation.
• Score: 8.770698303337428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Head avatar reenactment focuses on creating animatable personal avatars from monocular videos, serving as a foundational element for applications like social signal understanding, gaming, human-machine interaction, and computer vision. Recent advances in 3D Morphable Model (3DMM)-based facial reconstruction methods have achieved remarkable high-fidelity face estimation. However, on the one hand, they struggle to capture the entire head, including non-facial regions and background details in real time, which is an essential aspect for producing realistic, high-fidelity head avatars. On the other hand, recent approaches leveraging generative adversarial networks (GANs) for head avatar generation from videos can achieve high-quality reenactments but encounter limitations in reproducing fine-grained head details, such as wrinkles and hair textures. In addition, existing methods generally rely on a large amount of training data, and rarely focus on using only a simple selfie video to achieve avatar reenactment. To address these challenges, this study introduces a method for detailed head avatar reenactment using a selfie video. The approach combines 3DMMs with a StyleGAN-based generator. A detailed reconstruction model is proposed, incorporating mixed loss functions for foreground reconstruction and avatar image generation during adversarial training to recover high-frequency details. Qualitative and quantitative evaluations on self-reenactment and cross-reenactment tasks demonstrate that the proposed method achieves superior head avatar reconstruction with rich and intricate textures compared to existing approaches.

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