O. Fried, A. Tewari, M. Zollhöfer, A. Finkelstein, E. Shechtman, D. B Goldman, K. Genova, Z. Jin, C. Theobalt and M. Agrawala
We propose a novel text-based editing approach for talking-head video. Given an edited transcript, our approach produces a realistic output video in which the dialogue of the speaker has been modified and the resulting video maintains a seamless audio-visual flow (i.e. no jump cuts).
Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched
together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis.
The research was published in ACM Transactions on Graphics (Proc. SIGGRAPH) on 7/28/2019. The research is part of Ohad Fried’s Brown Research Fellowship.
Access the paper: https://www.ohadf.com/projects/text-based-editing/
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