

One Shot, One Talk
Overview :
One Shot, One Talk is a deep learning-based image generation technology capable of reconstructing full-body dynamic speaking avatars with personalized details from a single image, supporting realistic animation effects, including vivid body movements and natural expression changes. The significance of this technology lies in its ability to greatly lower the barrier for creating realistic, animated virtual images, enabling users to generate highly personalized and expressive virtual avatars with just one image. Background information indicates that this technology was developed by research teams from the University of Science and Technology of China and Hong Kong Polytechnic University, integrating the latest image-to-video diffusion models and 3DGS-mesh hybrid avatar representations, while employing key regularization techniques to reduce inconsistencies caused by imperfect labels.
Target Users :
The target audience includes professionals and enthusiasts in virtual reality, augmented reality, game development, social media, and the entertainment industry. This technology is suitable for them as it offers a fast and efficient way to create personalized virtual avatars for diverse applications such as virtual hosts, customer service agents, and game character designs.
Use Cases
Example 1: A game developer quickly generates dynamic models of game characters for promotional trailers using this technology.
Example 2: A social media platform utilizes this technology to provide users with personalized virtual images, enhancing user interaction experiences.
Example 3: A film production team uses this technology to create realistic dynamic models of movie characters for special effects.
Features
- Single Image Input: Users need only provide one image to generate a full-body dynamic speaking avatar.
- Realistic Animation: The generated avatars can perform realistic animations, including body movements and expression changes.
- Personalized Details: Avatars are capable of capturing and reproducing the user's personalized features.
- Dynamic Modeling: Advanced dynamic modeling techniques enable natural movements of the avatars.
- Pseudo-label Generation: Utilizes pre-trained generative models to create imperfect video frames as pseudo-labels.
- 3DGS-mesh Hybrid Avatar Representation: Combines 3DGS meshes and mesh representations to enhance the realism and expressiveness of avatars.
- Key Regularization Techniques: Reduces inconsistencies caused by imperfect labels, improving avatar quality.
- Cross-identity Action Replaying: Using the same driving pose, avatars of different identities can be driven in the same manner.
How to Use
1. Visit the product page and download the relevant code.
2. Prepare a personal image as input.
3. Configure the required environment and parameters according to the code documentation.
4. Run the code with the personal image as input; the system will automatically generate a dynamic avatar.
5. Adjust the parameters of the generated avatar, such as actions and expressions, to meet personalized needs.
6. Save the generated dynamic avatar for use in the desired application scenarios.
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