Music-Driven Digital Human: Algorithms and Practices

The piece introduces the Music XR Maker initiative from Tencent Music's Tianqin Lab, a research platform focused on AI-driven audio‑visual experiences, and outlines its two main research tracks: the Music XR Maker system and broader video‑related technologies such as video understanding and quality enhancement.

It explains the role of music‑driven technology within the digital‑human stack, dividing it into three layers: (1) avatar construction, covering model creation, facial capture, and clothing generation; (2) character driving, which includes both human‑in‑the‑loop (real‑person voice) and AI‑driven approaches for speech, singing, facial expression, and motion capture; and (3) visual rendering, enabling the final output to be viewed on platforms like virtual‑idol videos, live streams, and interactive entertainment.

The Music XR Maker development pipeline is described in four parts: data sources (motion‑capture, facial‑capture, and gesture data, plus rich musical feature extraction), AI generation (end‑to‑end models for classification, prediction, and generation, as well as AI‑orchestrated retrieval‑ranking‑re‑ranking stages), 3D rendering (using engines such as Unity or Unreal and formats like SMPL, GLB, FBX, with tools like Blender and Maya), and product applications (interactive entertainment, virtual‑idol videos, live virtual broadcasts, and large‑scale commercial deployments).

The article then details four major research topics: (1) the Music‑driven system architecture, (2) music‑generated digital‑human dance, (3) singing‑driven lip‑sync for digital humans, and (4) singing‑driven expressive facial animation.

For digital‑human dance generation, three production methods are compared: high‑cost multi‑camera motion capture studios, lower‑cost single‑camera video re‑creation, and pure algorithmic generation based on music features, each with its own trade‑offs in quality, scalability, and applicability.

The industry‑level solutions are categorized into generative approaches, codebook‑enhanced methods, and choreography‑based pipelines, with discussion of how commercial dance generation must align visual style, rhythm, and emotional expression with the underlying music.

In the lip‑sync domain, two pipelines are presented: a professional facial‑capture solution offering the highest fidelity, and a consumer‑grade optical camera approach suitable for most virtual‑human scenarios. Data collection leverages user‑generated karaoke videos paired with clean vocal tracks, feeding a model that aligns phoneme timing with audio features.

The TME lip‑sync model combines dry vocal recordings and aligned lyric timestamps, encodes them, and fuses the result with a facial‑matching decoder to predict per‑frame facial parameters for realistic singing animation.

Real‑time performance is achieved by pre‑computing BlendShape presets offline and blending them with live audio analysis during user interaction, enabling responsive virtual‑human singing experiences in products like QQ Show.

The concluding sections reflect on the rapid proliferation of virtual avatars across entertainment, the growing challenges for human performers, and the accelerating pace of AI‑driven technologies in avatar creation, visual driving, and audio synthesis, emphasizing that the future of digital humans is fundamentally technology‑centric.