DiTalker: A Unified DiT-based Framework for High-Quality and Speaking Styles Controllable Portrait Animation

• URL: http://arxiv.org/abs/2508.06511v1
• Date: Tue, 29 Jul 2025 08:23:56 GMT
• Title: DiTalker: A Unified DiT-based Framework for High-Quality and Speaking Styles Controllable Portrait Animation
• Authors: He Feng, Yongjia Ma, Donglin Di, Lei Fan, Tonghua Su, Xiangqian Wu,
• Abstract summary: DiTalker is a unified DiT-based framework for speaking style-controllable portrait animation.<n>We introduce an Audio-Style Fusion Module that decouples audio and speaking styles via two parallel cross-attention layers.<n>Experiments demonstrate the superiority of DiTalker in terms of lip synchronization and speaking style controllability.
• Score: 13.089363781114477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Portrait animation aims to synthesize talking videos from a static reference face, conditioned on audio and style frame cues (e.g., emotion and head poses), while ensuring precise lip synchronization and faithful reproduction of speaking styles. Existing diffusion-based portrait animation methods primarily focus on lip synchronization or static emotion transformation, often overlooking dynamic styles such as head movements. Moreover, most of these methods rely on a dual U-Net architecture, which preserves identity consistency but incurs additional computational overhead. To this end, we propose DiTalker, a unified DiT-based framework for speaking style-controllable portrait animation. We design a Style-Emotion Encoding Module that employs two separate branches: a style branch extracting identity-specific style information (e.g., head poses and movements), and an emotion branch extracting identity-agnostic emotion features. We further introduce an Audio-Style Fusion Module that decouples audio and speaking styles via two parallel cross-attention layers, using these features to guide the animation process. To enhance the quality of results, we adopt and modify two optimization constraints: one to improve lip synchronization and the other to preserve fine-grained identity and background details. Extensive experiments demonstrate the superiority of DiTalker in terms of lip synchronization and speaking style controllability. Project Page: https://thenameishope.github.io/DiTalker/

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