Summary of Audio-driven Emotional 3d Talking-head Generation, by Wenqing Wang and Yun Fu
Audio-Driven Emotional 3D Talking-Head Generation
by Wenqing Wang, Yun Fu
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel system for synthesizing high-fidelity, audio-driven video portraits with accurate emotional expressions. The proposed approach utilizes a variational autoencoder (VAE)-based audio-to-motion module to generate facial landmarks, which are then concatenated with emotional embeddings to produce emotional landmarks through the motion-to-emotion module. These emotional landmarks are used to render realistic emotional talking-head videos using a Neural Radiance Fields (NeRF)-based emotion-to-video module. The method also includes a pose sampling approach for generating natural idle-state videos in response to silent audio inputs. Experimental results demonstrate improved accuracy and fidelity of the proposed system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make realistic video portraits that match the emotions expressed in an audio recording. This is important for making virtual humans look more natural and engaging in film-making and other applications. The team developed a special module that takes audio signals and uses them to create facial expressions that match the emotions being conveyed. They also created a method to generate idle-state videos, where the person in the video doesn’t speak but still looks natural. Overall, this technology has potential to improve virtual human interactions and film-making. |
Keywords
» Artificial intelligence » Variational autoencoder
