用局部贴片实现夸张角色面部动作的语义保真迁移
Deep Learning Based Facial Retargeting Using Local Patches
- 基于局部贴片提取与重演,分块处理面部动画
- 在脸型差异大的角色间保持表情语义一致性
- 适合动画师快速迁移表演数据至风格化角色
在数字动画时代,为虚拟角色生成逼真的面部动画催生了多种动作迁移方法。尽管对形状相似的角色迁移已很成功,但在面对显著偏离人类面部结构的风格化或夸张3D角色时仍存在挑战。此时需考虑目标角色的面部结构和运动范围,以保留原始表情的语义。为此,我们提出一种基于局部贴片的迁移方法,将源性能视频中的面部动作迁移到目标风格化3D角色。该方法包含三个模块:自动贴片提取模块从源视频帧中提取局部贴片;重演模块生成对应的目标贴片;权重估计模块在每帧计算目标角色的动画参数,构建完整动画序列。大量实验表明,该方法能有效将源表情的语义迁移到面部比例差异显著的角色上。
原文摘要 · Abstract (English)
In the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch-based retargeting method that transfers facial animations captured in a source performance video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source video frame. These patches are processed through the Reenactment Module to generate correspondingly re-enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to stylized characters with considerable variations in facial feature proportion.
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