DynamicAvatars：基于扩散模型的精确动态面部头像重建与精细编辑
DynamicAvatars: Accurate Dynamic Facial Avatars Reconstruction and Precise Editing with Diffusion Models
AI总结：
DynamicAvatars提出基于高斯泼溅和扩散模型的动态3D头像生成框架，结合LLM引导参数与提示编辑模型，实现从视频生成逼真头像并进行精确、稳定的动态编辑。
摘要：
生成和编辑动态3D头部头像在虚拟现实和电影制作中是关键任务。然而，现有方法常常存在面部失真、头部运动不准确以及细粒度编辑能力有限等问题。为解决这些挑战，我们提出了DynamicAvatars，这是一种动态模型，能够从视频片段以及与面部位置和表情相关的参数中生成逼真的、可运动的3D头部头像。我们的方法通过一种新颖的基于提示的编辑模型实现精确编辑，该模型将用户提供的提示与从大语言模型（LLMs）中推导出的引导参数相结合。为实现这一目标，我们提出了一种基于高斯泼溅（Gaussian Splatting）的双重跟踪框架，并引入了一个提示预处理模块以增强编辑稳定性。通过引入专门的GAN算法并将其连接到我们的控制模块（该模块从LLMs生成精确的引导参数），我们成功解决了现有方法的局限性。此外，我们开发了一种动态编辑策略，该策略有选择地利用特定的训练数据集，以提高模型在动态编辑任务中的效率和适应性。
英文摘要：
Generating and editing dynamic 3D head avatars are crucial tasks in virtual reality and film production. However, existing methods often suffer from facial distortions, inaccurate head movements, and limited fine-grained editing capabilities. To address these challenges, we present DynamicAvatars, a dynamic model that generates photorealistic, moving 3D head avatars from video clips and parameters associated with facial positions and expressions. Our approach enables precise editing through a novel prompt-based editing model, which integrates user-provided prompts with guiding parameters derived from large language models (LLMs). To achieve this, we propose a dual-tracking framework based on Gaussian Splatting and introduce a prompt preprocessing module to enhance editing stability. By incorporating a specialized GAN algorithm and connecting it to our control module, which generates precise guiding parameters from LLMs, we successfully address the limitations of existing methods. Additionally, we develop a dynamic editing strategy that selectively utilizes specific training datasets to improve the efficiency and adaptability of the model for dynamic editing tasks.
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。
