Volcano Engine Virtual Digital Human Technology Overview

Volcano Engine defines virtual digital humans as AI‑enhanced, visual agents that replace real staff for communication across devices such as mobile, PC, and VR, offering listening, expression, interaction, and perception capabilities.

The platform categorizes digital humans into AI‑driven and human‑driven types; AI‑driven avatars are built on multimodal AI technologies, while human‑driven avatars rely on real‑person driving, with current research focusing on AI‑driven models.

AI‑driven digital humans are further divided by ability (broadcast, interactive, perception) and by visual form (2D and 3D). 2D avatars support head, body, and system modules, including head‑motion, lip‑sync, expression algorithms, facial customization (face swapping, editing, beautification), body motion prediction, and a pipeline for semantic feature extraction from audio‑visual training data.

Multi‑language support is achieved by extracting unsupervised acoustic features that encode prosody and style, enabling cross‑language synthesis without requiring multilingual training sets.

The interaction system integrates multimodal AI to transition among expression, listening, and idle states via a state machine driven by speech recognition, semantic understanding, and action tags, allowing real‑time interruption and action insertion.

Customization features such as face swapping and editing enable rapid creation of new avatars while mitigating copyright risks.

Core advantages of Volcano Engine's 2D digital humans include high visual quality (MOS 3.9, lip‑sync accuracy 98.6%), high concurrency (10 streams on a single T4 GPU at 1080p/25 fps), comprehensive functionality (interruptions, SSML‑driven actions, background replacement, voice switching, multilingual support), and low data cost (5 minutes of data for basic customization).

The 3D digital human pipeline mirrors the 2D process but adds 3D head modeling, facial capture, body capture, advanced motion systems, and rendering features such as off‑screen rendering, clothing, accessories, scene effects, and camera control.

Applications span digital human management, video creation, and industry use cases like financial verification and e‑commerce live streaming.

Future directions aim to improve expressiveness (large‑pose facial synthesis, richer body motion), enhance perception (environment sensing, liveness detection, face recognition), strengthen customization (more facial tools, 3D lighting, role‑specific tailoring), and lower data requirements through large‑scale models and transfer learning.