Music‑Driven Digital Human: Algorithms, System Architecture, and Practical Applications

Introduction – The focus of this article is the algorithms and practice of music‑driven digital humans.

Music XR Maker System – Originating from Tencent Music’s Tianqin Lab, Music XR Maker combines music‑driven AI with image rendering and video technologies. It occupies three roles in the digital‑human stack: (1) image construction (modeling, facial capture, clothing generation), (2) character driving (human‑driven and AI‑driven speech, singing, and motion generation), and (3) visual rendering for user‑facing applications such as virtual idols, live streaming, and interactive entertainment.

R&D Framework – The framework includes four components: data sources (motion capture, facial capture, gesture capture, and rich music feature extraction), AI generation (end‑to‑end models for classification, prediction, and generation, plus AI orchestration with recall, ranking, and re‑ranking stages), 3D rendering (using engines like Unity/UE and formats such as SMPL, GLB, FBX, with tools like Blender and Maya), and product applications (interactive entertainment platforms like QQ Music, cloud‑dance live streams, K‑song services, and virtual‑idol productions).

Music‑Generated Digital Human Dance – Three generation methods are described: (1) multi‑camera motion‑capture studios for high‑quality cinematic results, (2) video‑replay (single‑camera) for lower‑cost, rapid production, and (3) pure algorithmic generation based on music features, which enables batch production despite data acquisition challenges. Commercial solutions consider visual quality, rhythm alignment, and style consistency, using feature extraction, retrieval, sorting, and smoothing algorithms to assemble coherent dance sequences.

TME Tianqin Solution – The pipeline slices music into frames, extracts melodic and rhythmic features, retrieves matching dance fragments, re‑ranks them according to tempo and style, and smooths transitions. While effective, the approach may lack diversity due to limited imagination. An alternative generative method maps audio to dance via learned correspondences, aiming for broader expressive range.

Voice‑Driven Lip‑Sync – Two approaches are presented: a professional facial‑capture pipeline delivering high‑fidelity expressions, and a consumer‑grade optical‑camera solution offering acceptable results for typical virtual‑human scenarios. Data collection leverages user‑generated karaoke videos paired with clean vocal tracks, processed through monocular capture to obtain synchronized lip‑sync datasets.

TME Lip‑Sync Model – The model fuses clean vocal audio and aligned lyric timestamps via encoders, combines them with a facial‑matching decoder, and predicts per‑frame facial parameters for the entire song, producing realistic lip movements.

Real‑Time Solution – Offline pre‑computed BlendShapes are generated from high‑quality audio and data; during live use, real‑time vocal input is analyzed, blended with pre‑computed shapes, and rendered instantly, enabling live lip‑sync in QQ Show’s karaoke experience.

Expression Driving – Expression data is captured from performance videos using facial, motion, and hand capture, annotated, and stored in an expression library. During singing, lyric analysis determines the emotional tone, and appropriate expressions are selected from the library to match the music.

Summary and Outlook – Virtual avatars are becoming ubiquitous across entertainment, commerce, and user‑generated content. Challenges include cost, management, and the balance between human‑driven and AI‑driven personas. AI technologies for image creation, visual driving, and audio synthesis are rapidly advancing, and TME will continue to expand music‑driven digital‑human capabilities.

Q&A – The article concludes with a brief Q&A covering data redirection issues, subjective evaluation of generated dance, focus on cartoon‑style avatars, and technical details of segment stitching.