V-Express

V-Express is an avatar video generation model developed by Tencent AI Lab. It balances different control signals through a series of progressive discarding operations, so that the generated video can consider posture, input image and audio at the same time. This model is specifically optimized for situations where the audio signal is weak, solving the challenge of generating avatar videos with varying signal strengths.

Demand group:

" V-Express is suitable for developers and researchers who need to generate realistic avatar videos, especially those professionals who focus on human-computer interaction, virtual assistants, games and entertainment. It can help them create virtual characters that resemble real people, Enhance user experience."

Example of usage scenario:

Generate a virtual news anchor that resembles a specific person.

Create virtual teachers for online education platforms.

In game development, generate realistic dialogue animations for non-player characters.

Product features:

Use the Generative Model Enhancement Adapter for controlled generation.

Balancing different control signals through progressive discarding operations.

Specially optimized for weak audio signals.

Supports generating speaking videos that are consistent with a given video.

Ability to generate vivid mouth movements for fixed faces.

Generate mouth movements accompanied by slight facial movements.

Provide parameter adjustment to adapt to different input conditions.

English audio is supported, other languages ​​have not been tested in detail.

Usage tutorial:

Download the required model files.

Prepare reference images, audio and V-Kps sequences.

Choose an appropriate redirection strategy based on the similarity between the target video and the reference image.

Run the demo script and enter the necessary parameters, such as reference image path, audio path, etc.

Adjust the reference_attention_weight and audio_attention_weight parameters as needed.

Generate and view the output avatar video.

Further adjust parameters based on feedback to optimize generation results.