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Joyvasa
Overview :
JoyVASA is an audio-driven character animation technique based on diffusion models that generates facial dynamics and head movements by separating dynamic facial expressions from static 3D facial representations. This technology enhances video quality and lip-sync accuracy, expands into animal facial animation, supports multiple languages, and improves training and inference efficiency. Key advantages of JoyVASA include the ability to generate longer videos, motion sequence generation independent of character identity, and high-quality animation rendering.
Target Users :
Target audience includes video producers, animators, game developers, and any professionals who require audio-driven character animations. JoyVASA is particularly well-suited for creators who need to produce realistic animations and multilingual content, thanks to its high-quality animation generation and extensive language support.
Use Cases
Video producers use JoyVASA to create realistic audio-driven character animations for films.
Game developers utilize JoyVASA to generate dynamic facial expressions and head movements for characters in games.
In the education sector, JoyVASA is used to create dynamic characters in multilingual instructional videos to enhance learner engagement.
Features
Separate dynamic facial expressions from static 3D facial representations to create longer videos.
Directly generate motion sequences from audio prompts using a diffusion transformer, independent of character identity.
The generator trained in the first phase uses 3D facial representations and generated motion sequences as input to render high-quality animations.
Support for animal facial animation allows for seamless expansion.
Trained on a mixed dataset including Chinese and English data, supporting multiple languages.
Experimental results validate the effectiveness of the method.
How to Use
1. Provide a reference image to extract 3D facial appearance features and a series of learned 3D keypoints using an appearance encoder.
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2. Process the input audio to extract audio features using a wav2vec2 encoder.
3. Sample an audio-driven motion sequence using a diffusion model in a sliding window fashion.
4. Calculate target keypoints based on the 3D keypoints from the reference image and the sampled target motion sequence.
5. Distort the 3D facial appearance features based on the source and target keypoints.
6. Render the final output video using the generator based on the distorted features.
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