TEDRA：基于文本的动态与逼真角色编辑
TEDRA: Text-based Editing of Dynamic and Photoreal Actors
AI总结：
提出TEDRA，首个基于文本编辑动态3D虚拟形象的方法，通过个性化扩散模型和PNA-SDS技术实现高保真、时空连贯的服装风格编辑，支持骨骼姿态与视角控制。
摘要：
过去几年，仅从真实人类的视频中创建逼真且可驱动的3D虚拟形象取得了显著进展。然而，一个核心的剩余挑战是通过文本描述对服装风格进行细粒度且用户友好的编辑。为此，我们提出了TEDRA，这是首个允许对虚拟形象进行基于文本编辑的方法，该方法保持了虚拟形象的高保真度、时空连贯性以及动态特性，并支持骨骼姿态和视角控制。我们首先训练一个模型，以创建真实演员的可控且高保真的数字副本。接下来，我们通过在不同相机角度捕获的真实角色的各种帧上微调预训练的生成扩散模型来实现个性化，确保数字表示忠实地捕捉真实人物的动态和运动。这一两阶段过程为我们的动态人类虚拟形象编辑方法奠定了基础。利用这个个性化扩散模型，我们在基于模型的引导框架内，使用我们的个性化法线对齐分数蒸馏采样（PNA-SDS）根据提供的文本提示修改动态虚拟形象。此外，我们提出了一种时间步退火策略以确保高质量的编辑。我们的结果表明，在功能和视觉质量上，相比先前的工作有明显的改进。
英文摘要：
Over the past years, significant progress has been made in creating photorealistic and drivable 3D avatars solely from videos of real humans. However, a core remaining challenge is the fine-grained and user-friendly editing of clothing styles by means of textual descriptions. To this end, we present TEDRA, the first method allowing text-based edits of an avatar, which maintains the avatar's high fidelity, space-time coherency, as well as dynamics, and enables skeletal pose and view control. We begin by training a model to create a controllable and high-fidelity digital replica of the real actor. Next, we personalize a pretrained generative diffusion model by fine-tuning it on various frames of the real character captured from different camera angles, ensuring the digital representation faithfully captures the dynamics and movements of the real person. This two-stage process lays the foundation for our approach to dynamic human avatar editing. Utilizing this personalized diffusion model, we modify the dynamic avatar based on a provided text prompt using our Personalized Normal Aligned Score Distillation Sampling (PNA-SDS) within a model-based guidance framework. Additionally, we propose a time step annealing strategy to ensure high-quality edits. Our results demonstrate a clear improvement over prior work in functionality and visual quality.
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。
