Hallo3
H
Hallo3
Overview :
Hallo3 is a technology for portrait image animation that utilizes a pre-trained transformer-based video generation model. It is capable of generating highly dynamic and realistic videos, effectively addressing challenges such as non-frontal perspectives, dynamic object rendering, and immersive background generation. This technology has been jointly developed by researchers from Fudan University and Baidu, showcasing strong generalization capabilities and bringing new breakthroughs to the field of portrait animation.
Target Users :
The target audience includes researchers, developers, and individuals or enterprises interested in portrait animation technology. This technology is suitable for users who need to create realistic and dynamic portrait animations in areas such as virtual reality, augmented reality, game development, and video production.
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Use Cases
Create realistic character animations in virtual reality applications.
Generate dynamic expressions and actions for characters in game development.
Add vivid animated effects to static portraits in video production.
Features
Employs a pre-trained transformer-based video generation model to produce high dynamic and realistic portrait animation videos.
Implements an identity reference network, including a causal 3D VAE and stacked transformer layers, to ensure facial identity consistency in the video sequences.
Explores various voice audio conditions and motion frame mechanisms to achieve voice-driven continuous video generation.
Demonstrates significant improvements in generating realistic portraits from multiple orientations through experiments on benchmark and newly proposed outdoor datasets.
Provides code and models to facilitate further research and application by researchers and developers.
How to Use
1. Visit the project page of Hallo3 to learn about the technical details and usage guidelines.
2. Download the provided code and models, and install the necessary dependencies.
3. Prepare input data, such as portrait images and voice audio files.
4. Use the identity reference network to process the input images, ensuring facial identity consistency.
5. Apply voice audio conditions and motion frame mechanisms to generate a continuous video sequence.
6. Adjust parameters to optimize the quality and dynamic effects of the generated video.
7. Utilize the generated video in your target projects, such as virtual reality, gaming, or video production.
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