Summary of Embedded Representation Learning Network For Animating Styled Video Portrait, by Tianyong Wang et al.
Embedded Representation Learning Network for Animating Styled Video Portrait
by Tianyong Wang, Xiangyu Liang, Wangguandong Zheng, Dan Niu, Haifeng Xia, Siyu Xia
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel generative paradigm, Embedded Representation Learning Network (ERLNet), is proposed to overcome challenges in synthesizing style-controllable talking heads using Neural Radiance Fields (NeRF). The approach involves two learning stages: audio-driven FLAME (ADF) for producing facial expression and head pose sequences, and dual-branch fusion NeRF (DBF-NeRF) for rendering the final images. Compared to existing algorithms, ERLNet effectively renders more realistic talking heads. This is achieved by generating facial expressions and head poses synchronized with content audio and style video, followed by a novel NeRF-based approach that explores these contents to render the final images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of making computer-generated faces talk has been developed! The goal was to create talking heads that can be controlled by the person speaking. This is useful for things like digital avatars or animated characters. To achieve this, a special network called ERLNet was created. It’s made up of two parts: one that makes the face and head move in sync with what someone is saying, and another that uses those movements to create the final image. The results are more realistic talking heads than before. |
Keywords
» Artificial intelligence » Representation learning
