NEUROSYNC Audio To Face Blendshape
Are you looking for a way to bring your characters to life with realistic facial animations? The NEUROSYNC Audio To Face Blendshape model is here to help. This innovative model uses a transformer-based encoder-decoder architecture to transform audio features into facial blendshape coefficients, enabling real-time character animation. With its ability to stream generated facial blendshapes into Unreal Engine 5 using LiveLink, this model is perfect for creating immersive experiences. But what makes it truly remarkable is its efficiency and speed. By leveraging a seq2seq model, it can map sequences of 128 frames of audio features to facial blendshapes, ensuring accurate and realistic animations. Whether you're a developer or an artist, this model is sure to take your character animations to the next level.
Table of Contents
Model Overview
The NeuroSync Audio-to-Face Blendshape Transformer Model is a game-changer for real-time character animation. It can transform audio features into facial blendshape coefficients, making it perfect for integrating with Unreal Engine via LiveLink.
How It Works
This model uses a transformer-based encoder-decoder architecture to capture complex dependencies between audio features and facial expressions. It maps sequences of 128 frames of audio features to facial blendshapes used for character animation.
Key Features
- Audio-to-Face Transformation: Converts raw audio features into facial blendshape coefficients for driving facial animations.
- Transformer Seq2Seq Architecture: Uses transformer encoder-decoder layers to capture complex dependencies between audio features and facial expressions.
- Integration with Unreal Engine (LiveLink): Supports real-time streaming of generated facial blendshapes into Unreal Engine 5 through the NeuroSync Player using LiveLink.
Capabilities
This model can take raw audio features and convert them into facial expressions that can be used to animate characters in real-time. It’s like magic!
What Can It Do?
Here are some of the model’s primary tasks:
- Audio-to-Face Transformation: Converts raw audio features into facial blendshape coefficients for driving facial animations.
- Real-time Streaming: Supports real-time streaming of generated facial blendshapes into Unreal Engine 5 using LiveLink.
What Makes It Special?
This model uses a transformer-based encoder-decoder architecture to capture complex dependencies between audio features and facial expressions. This means it can generate highly accurate blendshape coefficients that can be used to create realistic facial animations.
Strengths
- High Accuracy: The model generates highly accurate blendshape coefficients that can be used to create realistic facial animations.
- Real-time Capabilities: The model supports real-time streaming of generated facial blendshapes into Unreal Engine 5 using LiveLink.
- Flexibility: The model can be used for a variety of applications, including real-time character animation and integration with Unreal Engine.
Real-World Applications
So, what are some real-world applications of this model? Here are a few examples:
- Real-time character animation
- Integration with Unreal Engine via LiveLink
- Facial animation from audio input
Limitations
This model is not perfect, and it has some limitations. Let’s take a closer look:
Limited Output Coefficients
The model outputs 61 blendshape coefficients, but only the first 52 are used for facial animations. The remaining 9 coefficients (52-61) pertain to head movements and emotional states, which are not streamed into LiveLink.
No Support for Certain Facial Movements
The model excludes certain facial movements, such as tongue movements, from being sent to LiveLink. This might limit its use in certain applications where these movements are crucial.
Non-Commercial License
The model is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which means you can only use it for non-commercial purposes. If you want to use it for commercial purposes, you’ll need to explore other options.
Format
This model is a seq2seq model that transforms sequences of audio features into corresponding facial blendshape coefficients. This model is designed to work with audio inputs and generate facial animations in real-time.
Architecture
The model uses a transformer-based encoder-decoder architecture, which consists of:
- Encoder: A transformer encoder that processes audio features and applies positional encodings to capture temporal relationships.
- Decoder: A transformer decoder with cross-attention, which attends to the encoder outputs and generates the corresponding blendshape coefficients.
- Blendshape Output: The output consists of 52 blendshape coefficients used for facial animations.
Data Formats
The model accepts input in the form of audio features, which are sequences of 128 frames. The output is a sequence of 61 blendshape coefficients, including:
- Eye movements (e.g., EyeBlinkLeft, EyeSquintRight)
- Jaw movements (e.g., JawOpen, JawRight)
- Mouth movements (e.g., MouthSmileLeft, MouthPucker)
- Brow movements (e.g., BrowInnerUp, BrowDownLeft)
- Cheek and nose movements (e.g., CheekPuff, NoseSneerRight)
Note that coefficients 52 to 68 should be ignored (or used to drive additive sliders) as they pertain to head movements and emotional states.
Input and Output Requirements
To use this model, you need to:
- Pre-process your audio input into sequences of 128 frames
- Pass the pre-processed audio features through the model
- Use the output blendshape coefficients to drive facial animations in your application
Here’s an example of how to handle inputs and outputs for this model:
# Pre-process audio input
audio_features = pre_process_audio(audio_input)
# Pass audio features through the model
blendshape_coefficients = model(audio_features)
# Use output blendshape coefficients to drive facial animations
facial_animation = drive_facial_animation(blendshape_coefficients)
Integration with Unreal Engine
The model supports real-time streaming of generated facial blendshapes into Unreal Engine 5 using LiveLink. You can set up the local API for this model using the NeuroSync Local API repository or apply for access to the NeuroSync Alpha API for non-local usage.
