One‑Click AI Digital Human for Live Commerce: LLM, Lip Sync & Real‑Time Tech

We present a comprehensive practice summary of building intelligent digital humans for live commerce, focusing on six core components: LLM‑based content generation, LLM‑driven interaction, TTS voice synthesis, image‑driven avatar rendering, real‑time audio‑video engineering, and a stable backend service platform.

Digital Human Overview

A digital human (Digital Human) is a virtual entity generated by computer graphics, AI, and machine learning that mimics human appearance, expressions, actions, and even cognitive and emotional abilities, enabling natural interaction with real users.

Based on visual style, digital humans can be 2D real‑person, 2D cartoon, 3D cartoon, 3D stylized, 3D realistic, or 3D hyper‑realistic. According to application scenarios they are divided into media avatars (virtual idols, hosts, celebrity replicas), service avatars (intelligent customer service, e‑commerce sales), and industry avatars (healthcare, education, manufacturing).

Challenges in Live‑Commerce Digital Humans

High production cost and reliance on high‑quality recorded material limit adoption by small and medium merchants.

Existing solutions often require complex material preparation, manual review, and long deployment cycles (3‑5 days).

Manual evaluation is subjective, slow, and provides vague feedback, hindering large‑scale quality control.

Low‑quality avatars lead to poor user engagement and reduced conversion rates.

Solution Roadmap

Phase 1 – Simplified material upload and zero‑shot head‑swap & lip‑sync, reducing time‑to‑launch to less than one day.

Phase 2 – Automated quality inspection, lightweight model training (≤4 h) and inference (≤4 GFlops), cutting total latency to ~6 h.

Phase 3 – Automated evaluation and fine‑grained ecological governance to continuously improve avatar performance.

Phase 4 – Full‑stack, one‑click managed live streaming for digital humans.

Technical Implementation

Head‑Swap & Driving

We employ a head‑swap pipeline combined with a V2V driving model to transfer fine‑grained facial expressions from a source video to a target avatar while preserving eye direction, facial structure, and high‑quality synthesis.

General Lip Sync

A real‑time lip‑sync model based on a UNet backbone predicts facial keypoints from audio and then inpaints the mouth region, achieving low latency and speaker‑independent performance.

Model Architecture

The pipeline consists of a data layer, model layer, and SDK layer. The model layer integrates a UNet‑based inpainting network, a speech‑to‑keypoint predictor, and a reference network to maintain identity consistency.

Results

Our full‑version and lightweight single‑person models achieve comparable visual quality, while the lightweight version reduces computation by 90 % and reaches >110 fps on a RTX 4070, enabling up to nine concurrent streams.

Conclusion and Future Work

We have built a scalable digital‑human pipeline that bridges LLM, TTS, avatar generation, and real‑time rendering, achieving a one‑click launch workflow and significant latency reductions. Future work will focus on eliminating the need for user‑provided material, further improving model efficiency for mobile deployment, and expanding high‑performance avatar capabilities across more domains.

References

ISC Article

IDC Report

Bilibili Presentation