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Thanks to the rapid advancement of artificial intelligence (AI), natural language processing (NLP), and computer graphics, digital humans have evolved from science fiction into reality. Today, they are widely used in fields such as customer service, education, and entertainment. By combining high-precision facial animations with voice synthesis, digital humans enable natural interactions and provide highly realistic experiences. In this article, we’ll explore the technical architecture and implementation of realtime talking digital human platforms. We hope it could offer insights to help tech enthusiasts and developers understand their construction and applications.

1. What is a Digital Human?

A digital human is a virtual figure created using advanced technology. It is able to interact with users through a screen while delivering lifelike visuals and realistic behavioral responses. Their core functions typically include visual representation, speech recognition and synthesis, emotional recognition and expression, and intelligent dialogue. Digital human can understand and respond to voice commands or text inputs. Further more, they can adapt their behavior to align with the user’s emotions and needs, offering personalized and human-like services.

1.1 Components of Digital Human

1.1.1 Visual Representation

Motion Capture and Animation: Digital human can ensure smooth and natural movements by simulating real human motion data.

Speech and Language Processing

Automatic Speech Recognition (ASR): Digital human can convert user voice input into text and understand it through speech recognition technology.

Natural Language Processing (NLP): Understanding and generating natural language, digital human can respond appropriately based on user input.

Text-to-Speech (TTS): Converts textual content into natural and smooth voice responses, simulating human speech to enhance interactivity.

1.1.2 Artificial Intelligence and Emotion Computing

Dialogue Management System: Digital human is able to interact with users through intelligent dialogue management systems, enabling them to understand context and sustain coherent, multi-turn conversations.

Emotional Recognition and Expression: Digital human is able to analyze user tone, facial expressions, and other cues to detect emotions and respond with appropriate expressions. By doing so, it could create more natural and human-like communication.

1.1.3 Interactive Experience and User Customization

Multimodal Interaction: Digital human supports various interaction methods, including voice, text, and touch, enriching the user experience.

Personalization and Customization: Digital human can be customized in appearance, behavior, and dialogue style to meet user needs and suit various application scenarios.

1.2 Application Areas of Digital Humans

The application scenarios for digital human are vast, covering multiple industries:

Customer Service: Digital human can serve as intelligent customer service agents, handling user inquiries, answering questions, and providing technical support.

Education and Training: Digital human can act as virtual tutors, offering personalized lessons and engaging with students through voice and expressions.

Healthcare: Digital human can function as virtual health advisors, offering health advice and psychological counseling.

Entertainment and Socializing: Digital human can act as virtual characters in film and gaming, engaging users in immersive interactions.

Brand Marketing: Digital human can serve as brand ambassadors or virtual hosts, attracting users through interaction and enhancing brand image.

2. Traditional Methods of Implementing Digital Human

Traditional digital human systems often rely on the integration of multiple technological components to achieve high-quality interactive experiences. Despite their widespread use, these technologies still face challenges in real-time interaction, fluid lip-syncing, and system latency. Below are the common technology stacks in traditional digital human systems:

2.1 Traditional Technology Stack

Automatic Speech Recognition (ASR): ASR technology converts user voice input into text, helping digital human understand user intent. While current ASR technology has matured, recognition accuracy under noisy conditions remains a challenge, especially when users speak quickly or unclearly.

Text-to-Speech Synthesis (TTS): TTS technology converts text into speech, generating natural-sounding voice responses for digital human to interact with users. Although traditional TTS can produce high-quality speech, its expressiveness and emotional depth remain limited, particularly in real-time conversations where it struggles to generate personalized responses based on dialogue context.

Natural Language Processing (NLP): NLP helps digital human understand user input and generate relevant replies. Traditional NLP methods often rely on rules or templates, or achieve more complex semantic analysis with deep learning assistance; however, many traditional systems still struggle with highly complex, multi-turn dialogues.

Facial Animation and Lip Syncing: This is one of the most challenging aspects of traditional digital humans. It typically requires generating facial expressions through video preprocessing tools (such as DeepFaceLab) and synchronizing them with speech. The generation of facial animations often relies on pre-recorded video clips or facial capture techniques, which are generally not flexible enough to respond to user dynamics in real-time.

2.2 Limitations of Traditional Methods

Real-time Issues: Traditional approaches often require video preprocessing and post-processing steps, failing to achieve true “real-time” interaction. Facial animation generation often causes delays, resulting in noticeable interactive lags for users.

Unnatural Lip Syncing: Traditional lip-syncing techniques typically depend on facial capture data or keyframes from static videos. While this approach can provide reasonably accurate lip movements, it often cannot achieve perfect synchronization with real-time audio input, resulting in mismatches between lip movements and speech.

Hardware Requirements: Some traditional methods require high-precision motion capture hardware (such as professional cameras or motion capture systems) to capture facial expressions and movements, increasing costs and system complexity.

3. MuseTalk + OpenAI Realtime API: An Innovative Implementation of Digital Human

In contrast to traditional methods, we utilize a combination of MuseTalk and OpenAI Realtime API in our innovative technological architecture, fundamentally enhancing the real-time capabilities, naturalness, and interactivity of digital human systems.

3.1 What is MuseTalk?

MuseTalk is a platform designed for video generation and lip-syncing. It creates animated videos of digital humans or virtual characters, synchronizing their lip movements with speech using audio data. Developed by Tencent Music Entertainment’s Lyra Lab, MuseTalk focuses on achieving high-quality real-time lip synchronization, especially for video content production. It employs Latent Space Inpainting technology to transform audio signals into matching lip animation.

3.1.1 Key Features

Real-time Lip Synchronization: MuseTalk can precisely synchronize any audio file with the lip movements of characters in the video, ensuring smooth animation at 30 frames per second, generating natural and vivid facial expressions.

Multilingual Support: The tool supports various languages, including English, Japanese, and Chinese, making it suitable for global users.

Open Source and Free: MuseTalk is available under the MIT license, enabling users to freely use and modify it. This makes it especially ideal for developers and researchers.

High-Quality Output: Through the Latent Space Inpainting technology, MuseTalk can generate highly realistic lip-synced animations without relying on additional upsamplers used in previous models (like VideoReTalking).

3.1.2 Working Principle

MuseTalk uses a neural network architecture inspired by U-Net that combines audio and image features, integrating them through cross-attention mechanisms. The model utilizes embedded vectors extracted from audio to influence the visual output of the video, particularly the synchronization of lip movements. Unlike diffusion model-based technologies, MuseTalk employs a repair model to achieve precise lip synchronization through modifications in latent space.

3.1.3 Application Scenarios

Voice-over and Video Localization: MuseTalk can be used for voice-over in videos, replacing original dialogue with new language versions while maintaining consistent character facial expressions. This makes it ideal for multilingual content creation.

AI-generated Virtual Characters: The tool can also be used to create lifelike virtual images, animating static images or videos by synchronizing them with audio.

Photo Animation: While MuseTalk mainly supports video lip synchronization, it can be paired with other tools like MuseV to transform static photos into “talking” images.

3.1.4 Limitations

High Computational Resource Requirements: MuseTalk requires strong computational capabilities when processing high-resolution videos or long audio tracks, and ordinary devices may struggle to run it smoothly.

Usage Learning Curve: Although open-source, MuseTalk may have a learning curve for non-technical users for installation and usage. However, users can execute it in the cloud through platforms like Google Colab.

Occasional Synchronization Errors: In certain cases, particularly with significant facial dynamic changes, the synchronization between lip movements and audio may not be perfectly accurate.

3.2 Installing and Using MuseTalk

3.2.1 Installing and Configuring MuseTalk

Visit GitHub Page: First, go to MuseTalk’s GitHub repository, and download or clone the project code: MuseTalk GitHub.

Install Dependencies: Install the necessary dependencies and software environment as per the project’s README documentation:

pip install -r requirements.txt
pip install --no-cache-dir -U openmim
mim install mmengine
mim install "mmcv>=2.0.1"
mim install "mmdet>=3.1.0"
mim install "mmpose>=1.1.0"

Download Models Folder: Download the models folder and place it in the project root directory. The download link is: Models.

Install FFmpeg: Choose an appropriate version and configure the environment variables for installation. Download link: FFmpeg Download.

Set FFmpeg in Virtual Environment: For Windows command line:

$env:PATH = 'C:\Software\ffmpeg-7.1-essentials_build\bin;' + $env:PATH

3.2.2 Running MuseTalk

Once the environment is set up, we can execute the commands provided by the project. There are two ways to run the project: Standard Inference and Real-time Inference.

3.2.2.1 Standard Inference (python -m scripts.inference)

Applicable Scenario: Applicable Scenario: Ideal for batch processing (e.g., generating multiple videos or handling larger datasets) or when more control over parameters is required during inference.

Input: video_path can be a video file, image file, or image directory, and supports audio_path for video generation and lip synchronization.

Output: Generates video or images, commonly frame by frame, with result adjustments possible through different bbox_shift settings.

Inference Steps:

Conduct model inference and generate videos or images.

Suitable for complex tasks requiring preprocessing phases like facial detection and parsing.

Control Parameters: Adjust results using parameters such as –bbox_shift, tweaking mouth opening or mask areas, specific adjustments need to adhere to default configuration values.

Applicable Devices: Typically suited for devices with strong processing capabilities; inference speed is slower, especially with larger video datasets.

Use Case: If you require precise control over inference results (like mouth opening degree, lip sync effects) and can accept slower generation speed.

Example Command:

python -m scripts.inference --inference_config configs/inference/test.yaml --bbox_shift -7

3.2.2.2 Real-time Inference (python -m scripts.realtime_inference)

Applicable Scenario: Suitable for real-time video or audio lip synchronization, especially in applications requiring quick feedback and real-time inference.

Input: Set the preparation flag to True and provide data from avatar, audio clips, etc., for real-time inference.

Output: Real-time inference results are sent through subprocess, enabling users to instantly view generated video or audio lip-sync effects.

Inference Steps: Designed for real-time processing, this method accelerates generation by focusing solely on the UNet and VAE decoder during inference. It avoids the traditional batch processing steps by streaming generated images or videos.

Control Parameters: Use –skip_save_images to bypass saving images and further speed up the inference process.

Applicable Devices: Suited for high-performance devices (like NVIDIA Tesla V30 or other powerful GPUs) to achieve high frame rates and quick responses.

Use Case: Quickly generate videos with real-time streaming outputs, suitable for instant interactions in applications.

Example Command:

python -m scripts.realtime_inference --inference_config configs/inference/realtime.yaml --batch_size 4 --skip_save_images

Comparison:

Standard Inference: Use this method for batch processing or when fine-tuning individual videos is required.

Real-time Inference: Use when you need to generate videos interactively, especially suitable for live broadcasts or real-time voice/video generation applications.

In this project, we will certainly leverage real-time inference.

3.3 What is the OpenAI Realtime API?

The OpenAI Realtime API is an efficient real-time API provided by OpenAI, designed to offer instant feedback through reduced latency, particularly suitable for applications requiring low-latency interactions, such as chatbots, real-time voice generation, and real-time dialogue systems. Compared to traditional batch processing APIs, the Realtime API maintains low latency while processing requests, enabling better support for real-time applications and interactive scenarios.

Key Features of OpenAI Realtime API:

Low Latency Responses: The Realtime API is designed to provide low-latency services for real-time interactions. This is especially critical for applications needing quick feedback, like chatbots, voice recognition, video generation, and real-time dialogue.

Streaming: With streaming capabilities, the OpenAI Realtime API returns generated content progressively, allowing you to receive results while requests are still being processed, without waiting for the entire response to complete.

Real-time Voice Generation: Real-time Voice Generation: The OpenAI Realtime API integrates with voice synthesis technologies (such as TTS) to generate audio data in real time, making it perfect for developing virtual assistants, AI customer support, and interactive gaming digital human.

Support for Multimodal Interactions: Similar to other OpenAI multimodal models, the Realtime API supports the combination of text, images, and audio for interaction. For example, you can simultaneously pass text and image inputs to receive combined outputs.

High Concurrency Support: The OpenAI Realtime API can handle numerous concurrent requests, making it highly suitable for applications needing multi-user support, such as online customer service, live interactions, and real-time meetings.

Thank you all for reading the first blog on the topic of real-time talking digital human. I will elaborate it further in the upcoming blog posts. Additionally, a video version of this blog is available below—stay tuned and enjoy watching!

And welcome to explore my Youtube channel https://www.youtube.com/@frankfu007 for more exciting content. If you enjoy my video, don’t forget to like and subscribe for more insights!

Interested in more application of Realtime API in practical projects? Explore my articles linked below!

• Realtime API Model Comparison
• OpenAI RealtimeAPI+MuseTalk: Technical Challenges and Solutions 3
• OpenAI RealtimeAPI+MuseTalk：Make a Realtime Talking Digital Human Facial Animation and Lip Syncing 2
• How to Build a Multi-Agent System Integrated with OpenAI Realtime API