Digital Human Technology Explained: Concepts, Classification, and Engineering Practices

Defining the Concept: A Digital Human Is First a Computable, Drivable, Interactive Software Entity

A digital human generally refers to a human-like avatar or human-like interactive product built with computer graphics, machine learning, speech processing, and natural language processing technologies. It may have a human appearance or behavior patterns, but it is not a recording of a real person, nor does it depend on a live human presenter to exist. The digital human itself runs on computing devices—PCs, smartphones, headsets, or cloud servers—and is presented to users through display devices, voice channels, or real-time video streams. It can be as simple as a set of pre-rendered video assets, or as capable as a real-time digital employee with voice interaction, knowledge Q&A, and transaction handling.

From an engineering perspective, a digital human consists of at least five separable modules: avatar assets, driving pipeline, interaction system, knowledge content, and runtime environment. The avatar assets determine “who it looks like”; the driving pipeline determines “how it moves and speaks”; the interaction system determines “whether it can understand and respond”; the knowledge content determines “what it can answer and whom it represents”; and the runtime environment determines “where it runs in real time and how it is deployed and monitored.” A digital human is therefore not a single model or a single video, but an orchestrable software system.

The boundary between “digital human” and “virtual human” is not consistently defined across the industry. Virtual humans lean more toward visual identity and content creation, emphasizing appearance, character设定, and distribution; digital humans emphasize computability, drivability, interactivity, and operability. In practice, the two terms are often used interchangeably. This article takes a product and technology perspective: any human-like figure with an independent identity that runs on computing devices and interacts with users through display or voice falls within the scope of digital humans. A figure that exists only as images or short videos without real-time interaction can be considered a lightweight brand-ambassador-type digital human.

Classification: Three Product Types with Different Technical Depth and Cost Structures

1. Brand Ambassador Digital Humans

This is the most common type. Its core goal is brand expression, content distribution, and image presentation, typically delivered as short videos, posters, promotional clips, or simple motions. It does not necessarily appear in livestreams or support voice interaction. Two technical routes are available: 3D modeling and animation, or ML-based portrait generation and driving. Hyper-realistic 3D avatars place high demands on modeling, materials, hair, lighting, and rigging; industry experience shows that asset production through deliverable video often reaches the million-RMB scale. Cartoon or semi-realistic 3D avatars cost significantly less. ML-based portrait generation and driving is usually cheaper, but requires attention to likeness rights, content compliance, and generation stability.

2. Livestreaming Digital Humans

This type emphasizes continuous output, scripted presentation, and real-time streaming, commonly seen in e-commerce and content platform livestreams. The technical focus is not complex emotion, but lip sync, natural motion, product script management, pacing control, and stable streaming. The system typically consists of avatar driving, speech synthesis, script orchestration, a product knowledge base, real-time video compositing, and content distribution. Cost depends on whether driving is real-time, whether multiple camera angles are supported, whether a product catalog is integrated, and whether hyper-realism is required. Lightweight solutions can use pre-generated videos played on a schedule, while real-time solutions require a low-latency rendering and streaming pipeline.

3. Digital Employee Humans

This type serves scenarios such as customer service, guided tours, in-store assistance, public consultation, and training, emphasizing voice interaction, knowledge Q&A, and closed-loop business handling. It typically requires speech recognition, speech synthesis, natural language understanding, dialogue management, retrieval-augmented generation (RAG) over knowledge bases, and business system integration. Key metrics for digital employees include intent recognition accuracy, knowledge hit rate, response latency, lip sync quality, interruption recovery, and multi-turn dialogue stability. Compared with brand ambassador types, digital employees are technically more complex, but deliver more direct business value.

Technology Stack: A Layered Architecture from Avatar Generation to Real-Time Operation

A production-ready digital human system can be designed in layers:

1. Avatar layer: 2D portrait, 3D cartoon, 3D hyper-realistic, or stylized avatars. 2D solutions are low-cost and fast to launch; 3D solutions support multi-angle presentation but involve high asset and rigging costs.
2. Driving layer: skeletal animation, facial expression driving, lip sync, motion capture combined with manual performance, and audio-driven motion. Pre-generated video suits content-oriented scenarios; real-time driving suits interactive scenarios.
3. Interaction layer: speech recognition, speech synthesis, natural language understanding, dialogue management, retrieval-augmented generation, and business intent routing. This layer determines whether the digital human “understands, answers accurately, and completes tasks.”
4. Content layer: persona design, dialogue scripts, knowledge base, product catalog, business rules, and compliance boundaries. The persona should clearly define identity, tone, knowledge scope, and refusal policies to avoid overstepping commitments.
5. Runtime layer: device-side or cloud-side rendering, real-time audio/video, content distribution, object storage, message queues, and logging/monitoring with alerting. On the cloud side, general compute, object storage, message queues, logging, and monitoring services—such as those offered by Baidu AI Cloud—can be combined to build a stable runtime environment.

An illustrative configuration, expressing only engineering fields and not a specific API, could look like this:

{
  "avatar": {
    "type": "3d_cartoon",
    "assetVersion": "v1.2",
    "lipSync": "audio_driven"
  },
  "dialogue": {
    "asr": true,
    "tts": "neural_voice",
    "knowledgeBase": "product_faq_v3",
    "fallback": "handoff_to_human"
  },
  "runtime": {
    "renderMode": "cloud_realtime",
    "streaming": "rtmp_or_webrtc",
    "monitoring": ["latency", "error_rate", "session_duration"]
  }
}

Cost and Production Routes: Look at the Full Pipeline, Not Just the Avatar

Digital human costs are composed of avatar assets, driving production, voice interaction, knowledge base construction, livestreaming, system integration, and long-term operations. Hyper-realistic 3D avatars are expensive mainly due to modeling, materials, rigging, animation, and tuning; cartoon and 2D solutions cost less. Pre-generated videos can be produced with animation software or motion capture plus manual performance, suitable for batch content; real-time interaction requires joint optimization of rendering, speech, dialogue, and streaming pipelines. When choosing a solution, organizations should first clarify their goal: short-term promotion, always-on livestreaming, or a closed-loop consultation service. Different goals call for different levels of technical depth—avoid using hyper-realistic assets for lightweight content tasks, and avoid promising complex business capabilities with low-interaction solutions.

Implementation Workflow and Governance Recommendations

A recommended sequence is: scenario definition → avatar assets → driving pipeline → dialogue and knowledge → integration testing → launch and operations. Define measurable metrics first, such as response latency, subjective lip sync scoring, knowledge hit rate, human handoff rate, and session completion rate. Then establish version control and rollback mechanisms so that avatars, scripts, and knowledge bases remain traceable. On compliance, prioritize likeness rights, copyright, content labeling, privacy protection, and review of generated content, with clear identity labeling for the digital human and a defined human takeover strategy. For customer service and sales-assistance scenarios, set refusal boundaries and fallback responses for sensitive questions to prevent the digital human from making unauthorized commitments. Only by treating a digital human as a testable, operable software system—rather than a one-off video asset—can teams achieve a stable return on investment.