

MEMO
Overview :
MEMO is an advanced open-weight model designed for audio-driven talking video generation. By utilizing a memory-guided temporal module and emotion-aware audio module, it enhances long-term identity consistency and motion smoothness, while refining facial expressions based on the emotions detected in the audio. The primary advantages of MEMO include more realistic video generation, improved audio-lip sync, identity consistency, and emotional expression alignment. Technical background information shows that MEMO generates more authentic talking videos across various image and audio types, surpassing existing state-of-the-art methods.
Target Users :
The target audience includes video creators, animators, game developers, and any professionals who need to generate or edit talking video content. MEMO is suitable for them as it provides an efficient and realistic way to create and edit videos, making the content more vivid and expressive.
Use Cases
Generate a talking video using an image of Einstein and audio from 'The Lion King.'
Combine an image of Audrey Hepburn with audio from 'La La Land' to create an expressive video.
Use an image of Jang Won-young with audio from ROSé & Bruno Mars to generate a singing video.
Features
Memory-guided temporal module: enhances long-term identity consistency and motion smoothness by developing memory states that store contextual information from the past.
Emotion-aware audio module: replaces traditional cross-attention with multi-modal attention to enhance audio-video interaction and detect emotions from audio for facial expression refinement.
Supports multiple image styles: including portraits, sculptures, digital art, and animations.
Supports various audio types: including speech, singing, and rapping.
Multi-language support: such as English, Mandarin, Spanish, Japanese, Korean, and Cantonese.
Expressive video generation: capable of creating expressive videos or emotive shifts within videos.
Supports different head poses: able to generate talking videos with various head orientations.
Long video generation: capable of producing longer talking videos, minimizing artifacts and error accumulation.
How to Use
1. Access the MEMO GitHub page to download and install the necessary models and code.
2. Prepare the required audio files and reference images, ensuring they meet the model's input requirements.
3. Use the MEMO model to input the audio and images into the system to begin generating talking videos.
4. Adjust model parameters as needed to optimize audio-lip sync, identity consistency, and emotional expression alignment.
5. The generated videos can be further edited or used directly for various applications, such as social media, advertising, or educational materials.
6. Ensure compliance with relevant laws, cultural norms, and ethical standards when using content generated by MEMO, respecting the rights of all parties involved.
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