VectorTalker: SVG Talking Face Generation with Progressive Vectorisation
Hao Hu, Xuan Wang, Jingxiang Sun, Yanbo Fan, Yu Guo, Caigui Jiang

TL;DR
VectorTalker introduces a scalable vector graphic method for high-quality, audio-driven talking head generation that supports diverse styles, outperforming raster-based approaches in reconstruction and animation quality.
Contribution
The paper presents a novel hierarchical vector image reconstruction and landmark-driven deformation method for efficient, high-fidelity, audio-driven talking head generation across multiple styles.
Findings
Outperforms raster-based methods in quality
Supports diverse portrait styles including manga and photorealistic images
Demonstrates superior vector graphic reconstruction and animation results
Abstract
High-fidelity and efficient audio-driven talking head generation has been a key research topic in computer graphics and computer vision. In this work, we study vector image based audio-driven talking head generation. Compared with directly animating the raster image that most widely used in existing works, vector image enjoys its excellent scalability being used for many applications. There are two main challenges for vector image based talking head generation: the high-quality vector image reconstruction w.r.t. the source portrait image and the vivid animation w.r.t. the audio signal. To address these, we propose a novel scalable vector graphic reconstruction and animation method, dubbed VectorTalker. Specifically, for the highfidelity reconstruction, VectorTalker hierarchically reconstructs the vector image in a coarse-to-fine manner. For the vivid audio-driven facial animation, we…
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Taxonomy
TopicsFace recognition and analysis · Generative Adversarial Networks and Image Synthesis · Human Motion and Animation
