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arXiv · 2409.07649

DiffTED: One-shot Audio-driven TED Talk Video Generation with Diffusion-based Co-speech Gestures

Steven Hogue·Chenxu Zhang·Hamza Daruger·Yapeng Tian·Xiaohu Guo
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Abstract

Audio-driven talking video generation has advanced significantly, but existing methods often depend on video-to-video translation techniques and traditional generative networks like GANs and they typically generate taking heads and co-speech gestures separately, leading to less coherent outputs. Furthermore, the gestures produced by these methods often appear overly smooth or subdued, lacking in diversity, and many gesture-centric approaches do not integrate talking head generation. To address these limitations, we introduce DiffTED, a new approach for one-shot audio-driven TED-style talking video generation from a single image. Specifically, we leverage a diffusion model to generate sequences of keypoints for a Thin-Plate Spline motion model, precisely controlling the avatar's animation while ensuring temporally coherent and diverse gestures. This innovative approach utilizes classifier-free guidance, empowering the gestures to flow naturally with the audio input without relying on pre-trained classifiers. Experiments demonstrate that DiffTED generates temporally coherent talking videos with diverse co-speech gestures.

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Steven Hogue, Chenxu Zhang, Hamza Daruger, Yapeng Tian, Xiaohu Guo. 2024-09-11. DiffTED: One-shot Audio-driven TED Talk Video Generation with Diffusion-based Co-speech Gestures. https://arxiv.org/abs/2409.07649

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