通过面部关键点变换实现保持身份的姿态引导角色动画
Identity-Preserving Pose-Guided Character Animation via Facial Landmarks Transformation
AI总结：
针对姿态引导角色动画中面部身份难以保持的问题，提出基于三维可变形模型的FLT关键点变换对齐方法，显著提升生成视频的身份一致性。
摘要：
在创建逼真的姿态引导图像到视频角色动画的同时保持面部身份，仍然具有挑战性，尤其是在舞蹈等复杂动态场景中，精确的身份一致性至关重要。现有方法往往难以保持面部连贯性，原因在于从提供头部姿态和表情线索的驱动视频中提取的面部关键点，与参考图像的面部几何结构之间存在错位。为解决这一局限，我们提出了面部关键点变换（Facial Landmarks Transformation，FLT）方法，该方法利用三维可变形模型（3D Morphable Model）来应对该问题。FLT将二维关键点转换为三维面部模型，调整该三维面部模型以与参考身份对齐，然后再将其变换回二维关键点，用于引导图像到视频生成过程。该方法确保与参考面部几何结构精确对齐，增强生成视频与参考图像之间的一致性。实验结果表明，FLT能够有效保持面部身份，并显著改进姿态引导的角色动画模型。
英文摘要：
Creating realistic pose-guided image-to-video character animations while preserving facial identity remains challenging, especially in complex and dynamic scenarios such as dancing, where precise identity consistency is crucial. Existing methods frequently encounter difficulties maintaining facial coherence due to misalignments between facial landmarks extracted from driving videos that provide head pose and expression cues and the facial geometry of the reference images. To address this limitation, we introduce the Facial Landmarks Transformation (FLT) method, which leverages a 3D Morphable Model to address this limitation. FLT converts 2D landmarks into a 3D face model, adjusts the 3D face model to align with the reference identity, and then transforms them back into 2D landmarks to guide the image-to-video generation process. This approach ensures accurate alignment with the reference facial geometry, enhancing the consistency between generated videos and reference images. Experimental results demonstrate that FLT effectively preserves facial identity, significantly improving pose-guided character animation models.
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