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Omniavatar
Introduction :
OmniAvatar is an advanced audio-driven video generation model that can generate high-quality virtual character animations. Its importance lies in combining audio and visual content to achieve efficient body animation, applicable to various scenarios. This technology uses deep learning algorithms to achieve high-fidelity animation generation, supports multiple input formats, and is positioned for the film, gaming, and social media sectors. The model is open source, promoting technology sharing and application.
Target Users :
This product is suitable for film and television creators, game developers, and social media content creators. Due to its efficient animation generation capabilities, users can quickly generate high-quality animation content, improving creative efficiency and reducing costs.
Usage Scenarios
Virtual streamer generation: Use audio to generate animated performances of virtual streamers.
Game character animation: Generate dynamic actions for game characters based on voice input.
Social media content creation: Quickly generate short videos that match the audio rhythm.
Product Features
Audio-driven animation generation: Generate synchronized virtual character animations based on input audio.
Adaptive body animation: The model can dynamically adjust the character's actions and expressions based on different inputs.
Efficient inference speed: Uses optimized algorithms to improve the efficiency of animation generation.
Diverse input support: Supports various audio formats and visual description inputs.
Model scalability: Provides pre-trained models, allowing users to perform secondary development according to their needs.
Support for multi-GPU inference: Utilizes multiple GPU cards to improve generation efficiency, suitable for large projects.
Flexible parameter adjustment: Users can adjust audio and prompt parameters according to their needs to achieve personalized effects.
Open community support: Encourages users to contribute code and cases, enriching features and application scenarios.
Usage Tutorial
Clone the project code: Use git commands to clone the OmniAvatar code repository.
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Install required dependencies: Install Python dependencies and models as required.
Download pre-trained models: Use huggingface-cli to download the required models.
Prepare input files: Create input files containing prompts and audio paths.
Run the inference script: Use the torchrun command to execute inference and generate animation.
View the output results: Check the generated animation videos in the specified folder.
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