MindFlow：调和认知语义与声学动态的对话面部动画生成
MindFlow: Harmonizing Cognitive Semantics and Acoustic Dynamics for Facial Animation Generation in Dyadic Conversations
AI总结：
提出双路径生成框架MindFlow，受神经科学腹侧-背侧通路模型启发，通过Chunk-State方法建模上下文感知情感状态链，结合条件自回归流匹配网络与选择性声学注入器，实现高保真面部动画，在语义适切性和运动自然度上超越现有方法。
摘要：
生成逼真的对话面部动画需要调和高级认知意图与精确的低级运动反射，但现有方法在对话上下文的语义理解和精确动态控制方面存在不足。本文提出MindFlow，一种受神经科学腹侧-背侧通路模型启发的双路径生成框架，将生成解耦为两个协作流，从而协调深层语义推理与细粒度控制。在腹侧模块中，我们将传统的句子-动作方法转变为新颖的块-状态方法，将原始声学流建模为上下文感知的、不断演变的情感状态链，捕捉句子级建模遗漏的微妙副语言细微差别和话语中情感转变。背侧模块包含一个条件自回归流匹配网络，用于高保真面部运动，由高频声学线索驱动并由情感状态调制，外加一个选择性声学注入器用于自适应音频门控，确保在听-说动态中无干扰的鲁棒性。大量实验表明，与最先进的基线相比，MindFlow在语义适切性和运动自然度上均表现优越。
英文摘要：
Generating lifelike facial animation for dyadic conversations requires reconciling high-level cognitive intent with precise low-level motor reflexes, yet existing methods fall short in the semantic understanding of dialogue context and in precise dynamic control. In this paper, we propose MindFlow, a dual-pathway generative framework inspired by the Ventral-Dorsal pathway model in neuroscience, which decouples generation into two collaborative streams, thereby harmonizing deep semantic reasoning with fine-grained control. In the Ventral module, we transform the conventional Sentence-Action approach into a novel Chunk-State approach that models raw acoustic streams as a context-aware, evolving emotional state chain, capturing subtle paralinguistic nuances and mid-utterance emotional shifts missed by sentence-level modeling. The Dorsal module features a conditional autoregressive flow matching network for high-fidelity facial motion, driven by high-frequency acoustic cues and modulated by emotion states, plus a Selective Acoustic Injector for adaptive audio gating to ensure robustness in talking-and-listening dynamics without interference. Extensive experiments demonstrate that MindFlow achieves superior semantic appropriateness and motion naturalness compared to state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
