Artistic video portrait generation is a significant and sought-after task in the fields of computer graphics and vision. While various methods have been developed that integrate NeRFs or StyleGANs with instructional editing models for creating and editing drivable portraits, these approaches face several challenges. They often rely heavily on large datasets, require extensive customization processes, and frequently result in reduced image quality. To address the above problems, we propose the Efficient Monotonic Video Style Avatar (Emo-Avatar) through deferred neural rendering that enhances StyleGAN's capacity for producing dynamic, drivable portrait videos. We proposed a two-stage deferred neural rendering pipeline. In the first stage, we utilize few-shot PTI initialization to initialize the StyleGAN generator through several extreme poses sampled from the video to capture the consistent representation of aligned faces from the target portrait. In the second stage, we propose a Laplacian pyramid for high-frequency texture sampling from UV maps deformed by dynamic flow of expression for motion-aware texture prior integration to provide torso features to enhance StyleGAN's ability to generate complete and upper body for portrait video rendering. Emo-Avatar reduces style customization time from hours to merely 5 minutes compared with existing methods. In addition, Emo-Avatar requires only a single reference image for editing and employs region-aware contrastive learning with semantic invariant CLIP guidance, ensuring consistent high-resolution output and identity preservation. Through both quantitative and qualitative assessments, Emo-Avatar demonstrates superior performance over existing methods in terms of training efficiency, rendering quality and editability in self- and cross-reenactment.

艺术视频肖像生成是计算机图形学和视觉领域中一个重要且受追捧的任务。针对现有方法所面临的大样本数据集、定制过程繁琐和图像质量下降等挑战，我们提出了一种名为Emo-Avatar的高效单调视频风格化头像生成方法，利用延迟神经渲染技术增强StyleGAN生成动态的可驾驶肖像视频的能力。该方法通过两阶段的延迟神经渲染流程，结合PIT初始化和拉普拉斯金字塔纹理采样等技术，实现了对风格化肖像视频的快速生成和编辑，具有比现有方法更高的训练效率、渲染质量和可编辑性。

Emo-Avatar: 通过纹理渲染实现高效的单目视频风格化头像