Summary of Portraittalk: Towards Customizable One-shot Audio-to-talking Face Generation, by Fatemeh Nazarieh et al.
PortraitTalk: Towards Customizable One-Shot Audio-to-Talking Face Generation
by Fatemeh Nazarieh, Zhenhua Feng, Diptesh Kanojia, Muhammad Awais, Josef Kittler
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces PortraitTalk, a novel audio-driven talking face generation framework that addresses limitations in existing methods. The proposed approach utilizes a latent diffusion framework consisting of IdentityNet and AnimateNet components to generate realistic talking faces. The model integrates an audio input with reference images, reducing reliance on reference-style videos, and incorporates text prompts through decoupled cross-attention mechanisms for expanded creative control. The paper presents extensive experiments demonstrating superior performance over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make realistic talking faces that look like real people. It uses special computer programs to generate these faces from just audio and some pictures of the person’s face. This is important because most current methods only focus on making lips move in sync with the audio, but don’t care as much about how the rest of the face looks or moves. The new method also lets you control what the person says by typing out a script, which makes it more useful for real-world uses. |
Keywords
» Artificial intelligence » Cross attention » Diffusion
