SegTalker: Segmentation-based Talking Face Generation with Mask-guided Local Editing

TL;DR

SegTalker introduces a segmentation-based framework for talking face generation that preserves textures, enables local editing, and maintains lip synchronization, improving over existing methods in texture detail and temporal consistency.

Contribution

The paper proposes a novel segmentation-based approach with mask-guided local editing for talking face generation, enhancing texture preservation and editing capabilities.

Findings

Effective preservation of texture details.

High temporal consistency in generated videos.

Superior performance in lip synchronization.

Abstract

Audio-driven talking face generation aims to synthesize video with lip movements synchronized to input audio. However, current generative techniques face challenges in preserving intricate regional textures (skin, teeth). To address the aforementioned challenges, we propose a novel framework called SegTalker to decouple lip movements and image textures by introducing segmentation as intermediate representation. Specifically, given the mask of image employed by a parsing network, we first leverage the speech to drive the mask and generate talking segmentation. Then we disentangle semantic regions of image into style codes using a mask-guided encoder. Ultimately, we inject the previously generated talking segmentation and style codes into a mask-guided StyleGAN to synthesize video frame. In this way, most of textures are fully preserved. Moreover, our approach can inherently achieve background separation and facilitate mask-guided facial local editing. In particular, by editing the mask and swapping the region textures from a given reference image (e.g. hair, lip, eyebrows), our approach enables facial editing seamlessly when generating talking face video. Experiments demonstrate that our proposed approach can effectively preserve texture details and generate temporally consistent video while remaining competitive in lip synchronization. Quantitative and qualitative results on the HDTF and MEAD datasets illustrate the superior performance of our method over existing methods.