用AI实时驱动3D卡通角色表情，情绪更自然可信。
MienCap: Realtime Performance-Based Facial Animation with Live Mood Dynamics
- 结合传统绑定与多模型学习，实现几何一致的表情动画。
- 相比商用软件Faceware，表情识别度、强度和吸引力评分更高。
- 适合影视动画师快速生成精准可控的角色表情。
本研究旨在提升基于表演的动画效果，使3D风格化角色的表情更具感知真实感。通过融合传统混合形状动画技术与多个机器学习模型，提出非实时与实时两种解决方案，确保角色表情在几何上一致且感知上合理。非实时系统采用3D情绪迁移网络，利用2D人像生成风格化3D绑定参数；实时系统则引入混合形状自适应网络，生成具有几何一致性与时间稳定性的角色绑定参数运动。通过与商用软件Faceware对比验证，本系统生成的表情在识别度、表现强度和吸引力方面均获得更高统计评分。结果表明，该系统可直接集成至动画流程，帮助动画师更快速、准确地创建所需表情。
原文摘要 · Abstract (English)
Our purpose is to improve performance-based animation which can drive believable 3D stylized characters that are truly perceptual. By combining traditional blendshape animation techniques with multiple machine learning models, we present both non-real time and real time solutions which drive character expressions in a geometrically consistent and perceptually valid way. For the non-real time system, we propose a 3D emotion transfer network makes use of a 2D human image to generate a stylized 3D rig parameters. For the real time system, we propose a blendshape adaption network which generates the character rig parameter motions with geometric consistency and temporally stability. We demonstrate the effectiveness of our system by comparing to a commercial product Faceware. Results reveal that ratings of the recognition, intensity, and attractiveness of expressions depicted for animated characters via our systems are statistically higher than Faceware. Our results may be implemented into the animation pipeline, and provide animators with a system for creating the expressions they wish to use more quickly and accurately.
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