标题： CanonSwap：通过规范空间调制实现高保真且一致的视频人脸交换 显示英文标题

标题： CanonSwap: High-Fidelity and Consistent Video Face Swapping via Canonical Space Modulation

摘要： 视频人脸交换旨在解决两个主要挑战：有效地将源身份转移到目标视频中，并准确保留目标面部的动态属性，例如头部姿态、面部表情、唇形同步，\etc 。 现有方法主要专注于实现高质量的身份转移，但往往在保持目标面部的动态属性方面表现不足，导致结果不一致。 我们将这一问题归因于视频中面部外观和运动的固有耦合性。 为了解决这个问题，我们提出了CanonSwap，一种新的视频人脸交换框架，该框架能够将运动信息与外观信息解耦。 具体而言， CanonSwap首先消除与运动相关的信息，使得在统一的标准空间内进行身份修改成为可能。 随后，交换后的特征被重新整合到原始视频空间中，确保目标面部动态属性的保留。 为了进一步实现精确的身份转移，同时减少伪影并增强真实感，我们设计了一个部分身份调制模块，该模块使用空间掩码自适应地整合源身份特征，以限制对面部区域的修改。 此外，我们引入了几种细粒度的同步度量标准，以全面评估视频人脸交换方法的性能。 大量实验表明，我们的方法在视觉质量、时间一致性和身份保留方面显著优于现有方法。 我们的项目页面可在 https://luoxyhappy.github.io/CanonSwap/ 公开访问。 显示英文摘要

摘要： Video face swapping aims to address two primary challenges: effectively transferring the source identity to the target video and accurately preserving the dynamic attributes of the target face, such as head poses, facial expressions, lip-sync, \etc. Existing methods mainly focus on achieving high-quality identity transfer but often fall short in maintaining the dynamic attributes of the target face, leading to inconsistent results. We attribute this issue to the inherent coupling of facial appearance and motion in videos. To address this, we propose CanonSwap, a novel video face-swapping framework that decouples motion information from appearance information. Specifically, CanonSwap first eliminates motion-related information, enabling identity modification within a unified canonical space. Subsequently, the swapped feature is reintegrated into the original video space, ensuring the preservation of the target face's dynamic attributes. To further achieve precise identity transfer with minimal artifacts and enhanced realism, we design a Partial Identity Modulation module that adaptively integrates source identity features using a spatial mask to restrict modifications to facial regions. Additionally, we introduce several fine-grained synchronization metrics to comprehensively evaluate the performance of video face swapping methods. Extensive experiments demonstrate that our method significantly outperforms existing approaches in terms of visual quality, temporal consistency, and identity preservation. Our project page are publicly available at https://luoxyhappy.github.io/CanonSwap/.