# JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing

arXiv: https://arxiv.org/abs/2501.01798 · HTML: https://mlsanity.com/paper/2501.01798/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [Bind-Your-Avatar: Multi-Talking-Character Video Generation with Dynamic 3D-mask-based Embedding Router](https://mlsanity.com/paper/2506.19833/) (2025)
- [RASA: Replace Anyone, Say Anything -- A Training-Free Framework for Audio-Driven and Universal Portrait Video Editing](https://mlsanity.com/paper/2503.11571/) (2025)
- [JOLT3D: Joint Learning of Talking Heads and 3DMM Parameters with Application to Lip-Sync](https://mlsanity.com/paper/2507.20452/) (2025)

## Abstract (arXiv)

Significant progress has been made in talking-face video generation research; however, precise lip-audio synchronization and high visual quality remain challenging in editing lip shapes based on input audio. This paper introduces JoyGen, a novel two-stage framework for talking-face generation, comprising audio-driven lip motion generation and visual appearance synthesis. In the first stage, a 3D reconstruction model and an audio2motion model predict identity and expression coefficients respectively. Next, by integrating audio features with a facial depth map, we provide comprehensive supervision for precise lip-audio synchronization in facial generation. Additionally, we constructed a Chinese talking-face dataset containing 130 hours of high-quality video. JoyGen is trained on the open-source HDTF dataset and our curated dataset. Experimental results demonstrate superior lip-audio synchronization and visual quality achieved by our method.