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arXiv 2407.12501cs.CVcs.GR

EmoFace:音频驱动的情感化3D面部动画

EmoFace: Audio-driven Emotional 3D Face Animation

Shanghai Jiao Tong University(上海交通大学);Tencent Games(腾讯游戏)

Chang Liu, Qunfen Lin, Zijiao Zeng, Ye Pan

AI总结:

本文提出EmoFace音频驱动框架,通过独立语音与情绪编码器及后处理生成多情感、唇音同步且眼神自然的MetaHuman三维面部动画,并发布情感音视频数据集。

摘要:

音频驱动的情感化3D面部动画旨在生成具有情感表现力且唇部动作同步的说话头像。然而,以往研究往往忽视了多种情绪对面部表情的影响,或被证明不适合驱动MetaHuman模型。针对这一不足,我们提出EmoFace,这是一种新颖的音频驱动方法,用于创建具有生动情感动态的面部动画。我们的方法能够生成带有多种情绪的面部表情,并能够生成随机却自然的眨眼和眼球运动,同时保持准确的唇部同步。我们提出独立的语音编码器和情绪编码器,以学习音频、情绪与相应面部控制器绑定之间的关系,并最终映射为控制器值序列。此外,我们引入两种后处理技术,专门用于增强动画的真实性,尤其是在眨眼和眼球运动方面。进一步地,认识到适合MetaHuman模型操控的情感音视频数据稀缺,我们贡献了一个情感音视频数据集,并为每一帧导出控制参数。我们提出的方法可应用于制作电子游戏中不可玩角色(NPC)的对话动画,以及驱动虚拟现实环境中的化身。我们进一步的定量和定性实验,以及与现有研究相比的用户研究表明,我们的方法在驱动3D面部模型方面展现出更优结果。代码和示例数据可在https://github.com/SJTU-Lucy/EmoFace获取。

英文摘要:

Audio-driven emotional 3D face animation aims to generate emotionally expressive talking heads with synchronized lip movements. However, previous research has often overlooked the influence of diverse emotions on facial expressions or proved unsuitable for driving MetaHuman models. In response to this deficiency, we introduce EmoFace, a novel audio-driven methodology for creating facial animations with vivid emotional dynamics. Our approach can generate facial expressions with multiple emotions, and has the ability to generate random yet natural blinks and eye movements, while maintaining accurate lip synchronization. We propose independent speech encoders and emotion encoders to learn the relationship between audio, emotion and corresponding facial controller rigs, and finally map into the sequence of controller values. Additionally, we introduce two post-processing techniques dedicated to enhancing the authenticity of the animation, particularly in blinks and eye movements. Furthermore, recognizing the scarcity of emotional audio-visual data suitable for MetaHuman model manipulation, we contribute an emotional audio-visual dataset and derive control parameters for each frames. Our proposed methodology can be applied in producing dialogues animations of non-playable characters (NPCs) in video games, and driving avatars in virtual reality environments. Our further quantitative and qualitative experiments, as well as an user study comparing with existing researches show that our approach demonstrates superior results in driving 3D facial models. The code and sample data are available at https://github.com/SJTU-Lucy/EmoFace.

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。

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