KeyframeFace: 通过语义关键帧实现语言驱动的面部动画
KeyframeFace: Language-Driven Facial Animation via Semantic Keyframes
AI总结：
本文提出KeyframeFace框架，通过语义关键帧实现语言驱动的面部动画，提升面部表情的精确控制与可解释性。
摘要：
面部动画是计算机图形（CG）行业创建数字角色的核心组成部分。典型的制作流程依赖于稀疏且具有语义意义的关键帧来精确控制面部表情。直接从自然语言描述生成此类动画可以显著提高内容创作的效率和可及性。然而，大多数现有方法采用文本到连续帧的范式，直接从语言回归密集的面部运动轨迹。这种范式将高层语义意图与低层运动纠缠在一起，缺乏明确的语义控制结构，并限制了精确编辑和可解释性。受动画制作中关键帧范式启发，我们提出KeyframeFace，一种通过可解释的关键帧实现语言驱动的面部动画的框架。与预测密集运动轨迹不同，我们的方法将动画表示为一系列具有语义意义的关键帧，这些关键帧在基于ARKit的可解释面部控制空间中按序列排列。一个语言驱动的模型利用大型语言模型（LLM）先验知识，生成与上下文文本描述和情绪线索对齐的关键帧。为了支持这一范式，我们构建了一个包含2100个表情脚本及其单目视频、每帧ARKit系数和手动标注的语义关键帧的多模态数据集。实验表明，结合语义关键帧监督和语言先验知识，相比不使用面部动作语义的方法，显著提高了表情保真度和语义对齐性。
英文摘要：
Facial animation is a core component for creating digital characters in Computer Graphics (CG) industry. A typical production workflow relies on sparse, semantically meaningful keyframes to precisely control facial expressions. Enabling such animation directly from natural-language descriptions could significantly improve content creation efficiency and accessibility. However, most existing methods adopt a text-to-continuous-frames paradigm, directly regressing dense facial motion trajectories from language. This formulation entangles high-level semantic intent with low-level motion, lacks explicit semantic control structure, and limits precise editing and interpretability. Inspired by the keyframe paradigm in animation production, we propose KeyframeFace, a framework for semantic facial animation from language via interpretable keyframes. Instead of predicting dense motion trajectories, our method represents animation as a sequence of semantically meaningful keyframes in an interpretable ARKit-based facial control space. A language-driven model leverages large language model (LLM) priors to generate keyframes that align with contextual text descriptions and emotion cues. To support this formulation, we construct a multimodal dataset comprising 2,100 expression scripts paired with monocular videos, per-frame ARKit coefficients, and manually annotated semantic keyframes. Experiments show that incorporating semantic keyframe supervision and language priors significantly improves expression fidelity and semantic alignment compared to methods that do not use facial action semantics.
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。
