HunyuanVideo-Avatar - A voice-activated digital human model launched by Tencent Hunyuan.

What is HunyuanVideo-Avatar?

HunyuanVideo-Avatar is a voice-based digital human model jointly developed by Tencent Hunyuan Team and Tencent Music Tianqin Lab. Based on a multimodal diffusion Transformer architecture, it can generate dynamic, emotionally controllable, and multi-role dialogue videos. The model features a character image injection module to eliminate conditional mismatches between training and inference, ensuring character consistency. The Audio Emotion Module (AEM) extracts emotional cues from emotion reference images, enabling emotional style control. The Face-Aware Audio Adapter (FAA) allows for independent audio injection in multi-role scenarios. It supports various styles, species, and multi-person scenes, and can be applied to short video creation, e-commerce advertising, and more.

Main functions of HunyuanVideo-Avatar

• Video generationUsers only need to upload a picture of a person and the corresponding audio. The model can automatically analyze the emotions in the audio and the environment in which the person is located, and generate a video that includes natural facial expressions, lip-sync, and full-body movements.
• Multi-role interactionIn multi-person interactive scenarios, the model can accurately drive multiple characters, ensuring that the lip movements, facial expressions, and actions of each character are perfectly synchronized with the audio, achieving natural interaction, and generating video clips such as dialogues and performances in various scenarios.
• Multiple styles supportedIt supports various styles, species, and multiplayer scenes, including cyberpunk, 2D animation, and Chinese ink painting. Creators can easily upload cartoon characters or virtual avatars to generate stylized dynamic videos, meeting the creative needs of animation, games, and other fields.

The technical principles of HunyuanVideo-Avatar

• Multimodal Diffusion Transformer Architecture (MM-DiT)The architecture can process multiple modalities of data simultaneously, such as images, audio, and text, enabling highly dynamic video generation. Through a hybrid "two-stream to single-stream" model design, video and text data are processed independently first, and then fused together, effectively capturing the complex interactions between visual and semantic information.
• Character Image Injection ModuleIt replaces the traditional additive character condition method, solves the problem of condition mismatch between training and inference, and ensures the dynamic movement and consistency of characters in the generated video.
• Audio Emotion Module (AEM)Emotional cues are extracted from emotional reference images and transferred to the target generated video, enabling fine-grained control over emotional style.
• Facial Awareness Audio Adapter (FAA)By isolating audio-driven characters through a potential level of facial masking, independent audio injection is achieved in multi-character scenarios, enabling each character to generate independent actions and expressions based on its own audio.
• Potential space of spacetime compressionBased on Causal 3D VAE technology, video data is compressed into a latent representation and then reconstructed back into the original data through a decoder, which accelerates the training and inference process and improves the quality of the generated video.
• MLLM text encoderUsing a pre-trained multimodal large language model (MLLM) as a text encoder, MLLM performs better than traditional CLIP and T5-XXL in terms of image-text alignment, image detail description, and complex reasoning.

HunyuanVideo-Avatar project address

• Project official website:https://hunyuanvideo-avatar.github.io/
• Github repository:https://github.com/Tencent-Hunyuan/HunyuanVideo-Avatar
• HuggingFace model library:https://huggingface.co/tencent/HunyuanVideo-Avatar
• arXiv technical paper:https://arxiv.org/pdf/2505.20156

Application scenarios of HunyuanVideo-Avatar

• Product introduction videoBusinesses can quickly generate high-quality advertising videos based on product characteristics and target input prompts. For example, cosmetic advertisements can showcase product effects and enhance brand awareness.
• Knowledge VisualizationPresenting abstract knowledge in video format enhances teaching effectiveness. For example, in mathematics teaching, videos showing the rotation and transformation of geometric figures can be generated to help students understand; in language arts teaching, the artistic conception of a poet's work can be displayed.
• Vocational skills trainingGenerate simulated operation videos to help trainees master the key operation points.
• VR game developmentGenerate realistic environments and interactive scenes in VR games, such as exploring ancient ruins.