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Gaussianspeech
Overview :
GaussianSpeech is an innovative method capable of synthesizing high-fidelity animated sequences from speech signals to create realistic, personalized 3D head avatars. The technology combines speech signals with 3D Gaussian drawing techniques to capture human head expressions and detailed movements, including skin wrinkling and finer facial motions. Key advantages of GaussianSpeech include real-time rendering speed, natural visual dynamics, and the ability to exhibit a variety of facial expressions and styles. The underlying technology involves the creation of large-scale, multi-view audio-visual sequence datasets and the development of audio conditional transformation models that can directly extract lip and expression features from audio input.
Target Users :
The target audience for GaussianSpeech includes professionals in fields such as virtual reality, augmented reality, game development, film production, and animation. These users require realistic 3D head avatars to enhance user experience, which is precisely what the high fidelity and real-time rendering capabilities of GaussianSpeech deliver.
Use Cases
In virtual reality, a 3D head avatar created using GaussianSpeech can represent the user in virtual worlds, providing a more natural and authentic interactive experience.
In film production, GaussianSpeech can generate realistic facial animations, reducing the need for actors during actual shoots, which lowers costs and improves efficiency.
In game development, GaussianSpeech can be used to create facial animations for NPCs, making the expressions of game characters more rich and genuine, thereby enhancing immersion.
Features
? Audio-driven: Synthesizes realistic 3D head avatar animations from speech signals.
? High fidelity: Generates detailed animations that include teeth, wrinkles, and the sheen in the eyes.
? Real-time rendering: Presents natural visual dynamics at real-time rendering speeds.
? Personalized expression: Generates personalized colors related to expressions based on speech signals.
? Dataset support: Trains using large-scale multi-view audio-visual sequence datasets.
? Audio feature extraction: Utilizes the Wav2Vec 2.0 encoder to extract general audio features and map them to personalized lip features.
? Multi-modal fusion: Merges lip and expression features into the decoder through cross-attention layers.
? 3DGS Avatar representation: Generates expression-dependent colors and applies wrinkles and perceptual loss to enhance photorealism.
How to Use
1. Visit the GaussianSpeech GitHub page to download the necessary code and datasets.
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2. Set up the development environment and install the required libraries according to the documentation.
3. Process the input speech signal using the Wav2Vec 2.0 encoder to extract audio features.
4. Extract lip and wrinkle features from the audio characteristics using the Lip Transformer Encoder and Wrinkle Transformer Encoder.
5. Synthesize FLAME expressions using the Expression Encoder and combine these expressions with lip features via the Expression2Latent MLP.
6. Input the combined features to the motion decoder to predict FLAME vertex offsets.
7. Add the predicted vertex offsets to the template mesh to generate vertex animation in canonical space.
8. During training, further refine the animation through optimized 3DGS avatars and color MLPs, and improve accuracy using render loss.
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