Instruct-NeuralTalker: Editing Audio-Driven Talking Radiance Fields with Instructions

Understand

Recent neural talking radiance field methods have shown great success in photorealistic audio-driven talking face synthesis.

• In this paper, we propose a novel interactive framework that utilizes human instructions to edit such implicit neural representations to achieve real-time personalized talking face generation.
• Given a short speech video, we first build an efficient talking radiance field, and then apply the latest conditional diffusion model for image editing based on the given instructions and guiding implicit representation optimization towards the editing target.
• To ensure audio-lip synchronization during the editing process, we propose an iterative dataset updating strategy and utilize a lip-edge loss to constrain changes in the lip region.