先思后绘：分解情感语义与细粒度可控的富有表现力的说话头生成
Think-Before-Draw: Decomposing Emotion Semantics & Fine-Grained Controllable Expressive Talking Head Generation
AI总结：
本文提出Think-Before-Draw框架，通过思维链将情感标签解析为面部肌肉运动描述，并结合渐进式引导去噪策略实现细粒度可控的情感说话头生成，在MEAD和HDTF上取得最优性能。
摘要：
情感说话头生成已成为计算机视觉与多模态人工智能交叉领域的关键研究方向，其核心价值在于通过沉浸式和共情式交互增强人机交互体验。随着多模态大语言模型的发展，情感说话头生成的驱动信号已从音频和视频转向更灵活的文本。然而，当前的文本驱动方法依赖预定义的离散情感标签文本，过度简化了真实面部肌肉运动的动态复杂性，因而无法实现自然的情感表现力。本研究提出Think-Before-Draw框架以应对两个关键挑战：（1）情感的深度语义解析——通过创新性地引入思维链（Chain-of-Thought，CoT），将抽象的情感标签转化为具有生理学依据的面部肌肉运动描述，从而实现从高层语义到可执行运动特征的映射；（2）细粒度表现力优化——受艺术家肖像绘画过程的启发，提出一种渐进式引导去噪策略，采用“全局情感定位—局部肌肉控制”机制来细化生成视频中的微表情动态。我们的实验表明，该方法在广泛使用的基准数据集MEAD和HDTF上取得了最先进的性能。此外，我们收集了一组肖像图像，以评估模型的零样本生成能力。
英文摘要：
Emotional talking-head generation has emerged as a pivotal research area at the intersection of computer vision and multimodal artificial intelligence, with its core value lying in enhancing human-computer interaction through immersive and empathetic engagement.With the advancement of multimodal large language models, the driving signals for emotional talking-head generation has shifted from audio and video to more flexible text. However, current text-driven methods rely on predefined discrete emotion label texts, oversimplifying the dynamic complexity of real facial muscle movements and thus failing to achieve natural emotional expressiveness.This study proposes the Think-Before-Draw framework to address two key challenges: (1) In-depth semantic parsing of emotions--by innovatively introducing Chain-of-Thought (CoT), abstract emotion labels are transformed into physiologically grounded facial muscle movement descriptions, enabling the mapping from high-level semantics to actionable motion features; and (2) Fine-grained expressiveness optimization--inspired by artists' portrait painting process, a progressive guidance denoising strategy is proposed, employing a "global emotion localization--local muscle control" mechanism to refine micro-expression dynamics in generated videos.Our experiments demonstrate that our approach achieves state-of-the-art performance on widely-used benchmarks, including MEAD and HDTF. Additionally, we collected a set of portrait images to evaluate our model's zero-shot generation capability.
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。
