Product Introduction

1. HunyuanVideo-Avatar is an open-source framework developed by Tencent that generates high-fidelity, emotion-controllable talking avatar videos from audio input, supporting multiple characters in dynamic scenarios. It leverages multimodal diffusion transformer (MM-DiT) technology to ensure character consistency and precise synchronization between audio and visual outputs. The system is designed for applications requiring realistic human animations, such as virtual influencers, educational content, and multi-character storytelling. Code, pre-trained models, and implementation details are publicly available on GitHub and Hugging Face.

2. The core value of HunyuanVideo-Avatar lies in its ability to solve three critical challenges in audio-driven animation: maintaining character consistency during dynamic motion, achieving fine-grained emotion alignment with audio, and enabling multi-character interactions. By integrating advanced modules like the Face-Aware Audio Adapter (FAA) and Audio Emotion Module (AEM), it ensures immersive and scalable avatar generation for diverse use cases. Its open-source nature allows developers and researchers to customize and extend the framework for specialized applications.

Main Features

1. Character Image Injection Module: This feature replaces traditional additive conditioning with direct image-based character embedding, eliminating training-inference mismatches and ensuring consistent avatar appearance across dynamic motions. It integrates reference character images into the diffusion process via cross-attention layers, preserving facial features and clothing details even during rapid movements.

2. Audio Emotion Module (AEM): The AEM extracts emotional cues from reference images (e.g., a smiling face) and transfers them to generated videos, enabling precise emotion control aligned with audio tone. It uses a dual-branch architecture to separately process emotion features and audio-visual synchronization, allowing independent adjustment of emotional intensity and lip movements.

3. Face-Aware Audio Adapter (FAA): Designed for multi-character scenarios, the FAA isolates audio streams for individual avatars using latent-level face masks and cross-attention mechanisms. This enables simultaneous animation of multiple characters in a single video, with each avatar responding independently to assigned audio tracks without interference.

Problems Solved

1. Dynamic Motion vs. Character Consistency: Traditional methods often fail to maintain stable facial features during high-intensity movements, causing distortions. HunyuanVideo-Avatar resolves this through its image injection module, which anchors character identity to reference images at the latent space level.

2. Target User Groups: The product serves content creators, digital marketers, and AI developers needing scalable avatar solutions for videos, virtual assistants, or interactive storytelling. Academic researchers also benefit from its open-source code for studying multimodal generative models.

3. Typical Use Cases: Applications include generating dialogue videos for virtual influencers on social media, creating multilingual educational content with expressive instructors, and producing animated scenes with interacting characters for gaming or film pre-visualization.

Unique Advantages

1. Multimodal Architecture: Unlike single-modality approaches, HunyuanVideo-Avatar’s MM-DiT framework jointly processes audio, text, and visual data through a unified transformer, enabling richer context-aware animations. This contrasts with methods relying solely on audio-to-visual mapping without emotional or contextual controls.

2. Innovative Emotion Transfer: The AEM introduces a novel emotion transfer mechanism that decouples emotion style from lip movements, allowing users to adjust emotional expressions (e.g., happiness, anger) independently while maintaining accurate lip-syncing. Competitors typically require manual post-editing for such adjustments.

3. Open-Source Scalability: As one of the few open-source models supporting multi-character animation, it provides pre-trained weights and modular codebases for customization. This contrasts with proprietary solutions like Synthesia, which offer limited flexibility and no access to underlying models.

Frequently Asked Questions (FAQ)

1. How does HunyuanVideo-Avatar ensure character consistency in generated videos? The character image injection module directly embeds reference images into the diffusion process via cross-attention, bypassing additive noise injection that causes feature drift. This ensures stable facial attributes across frames.

2. Can the model animate multiple characters speaking different audio tracks simultaneously? Yes, the Face-Aware Audio Adapter (FAA) uses latent masks to isolate audio streams per character, enabling independent lip-syncing and motion generation for up to four characters in a single scene.

3. How is emotion control achieved without manual intervention? The Audio Emotion Module (AEM) automatically extracts emotion styles from reference images (e.g., a photo of a sad face) and transfers them to avatars via feature alignment, synchronized with audio emotion cues like pitch and intensity.

4. Is the framework compatible with non-English audio inputs? Yes, the audio encoder supports multilingual inputs by leveraging universal speech representations, though optimal performance requires fine-tuning on target language datasets.

5. What hardware is required to run the model locally? A GPU with at least 16GB VRAM (e.g., NVIDIA V100 or RTX 3090) is recommended for inference, while training demands multi-GPU setups. Quantized models are available for edge-device testing.