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arXiv · 2601.22501

MIRRORTALK: Forging Personalized Avatars Via Disentangled Style and Hierarchical Motion Control

Renjie Lu·Xulong Zhang·Xiaoyang Qu·Jianzong Wang·Shangfei Wang
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Abstract

Synthesizing personalized talking faces that uphold and highlight a speaker's unique style while maintaining lip-sync accuracy remains a significant challenge. A primary limitation of existing approaches is the intrinsic confounding of speaker-specific talking style and semantic content within facial motions, which prevents the faithful transfer of a speaker's unique persona to arbitrary speech. In this paper, we propose MirrorTalk, a generative framework based on a conditional diffusion model, combined with a Semantically-Disentangled Style Encoder (SDSE) that can distill pure style representations from a brief reference video. To effectively utilize this representation, we further introduce a hierarchical modulation strategy within the diffusion process. This mechanism guides the synthesis by dynamically balancing the contributions of audio and style features across distinct facial regions, ensuring both precise lip-sync accuracy and expressive full-face dynamics. Extensive experiments demonstrate that MirrorTalk achieves significant improvements over state-of-the-art methods in terms of lip-sync accuracy and personalization preservation.

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Renjie Lu, Xulong Zhang, Xiaoyang Qu, Jianzong Wang, Shangfei Wang. 2026-01-30. MIRRORTALK: Forging Personalized Avatars Via Disentangled Style and Hierarchical Motion Control. https://arxiv.org/abs/2601.22501

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