TextToon: Real-Time Text Toonify Head Avatar from Single Video

• URL: http://arxiv.org/abs/2410.07160v1
• Date: Mon, 23 Sep 2024 15:04:45 GMT
• Title: TextToon: Real-Time Text Toonify Head Avatar from Single Video
• Authors: Luchuan Song, Lele Chen, Celong Liu, Pinxin Liu, Chenliang Xu,
• Abstract summary: We propose TextToon, a method to generate a drivable toonified avatar. Given a short monocular video sequence and a written instruction about the avatar style, our model can generate a high-fidelity toonified avatar.
• Score: 34.07760625281835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose TextToon, a method to generate a drivable toonified avatar. Given a short monocular video sequence and a written instruction about the avatar style, our model can generate a high-fidelity toonified avatar that can be driven in real-time by another video with arbitrary identities. Existing related works heavily rely on multi-view modeling to recover geometry via texture embeddings, presented in a static manner, leading to control limitations. The multi-view video input also makes it difficult to deploy these models in real-world applications. To address these issues, we adopt a conditional embedding Tri-plane to learn realistic and stylized facial representations in a Gaussian deformation field. Additionally, we expand the stylization capabilities of 3D Gaussian Splatting by introducing an adaptive pixel-translation neural network and leveraging patch-aware contrastive learning to achieve high-quality images. To push our work into consumer applications, we develop a real-time system that can operate at 48 FPS on a GPU machine and 15-18 FPS on a mobile machine. Extensive experiments demonstrate the efficacy of our approach in generating textual avatars over existing methods in terms of quality and real-time animation. Please refer to our project page for more details: https://songluchuan.github.io/TextToon/.

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