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arXiv · 2408.15995

TEDRA: Text-based Editing of Dynamic and Photoreal Actors

Basavaraj Sunagad·Heming Zhu·Mohit Mendiratta·Adam Kortylewski·Christian Theobalt·Marc Habermann
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Abstract

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.

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Basavaraj Sunagad, Heming Zhu, Mohit Mendiratta, Adam Kortylewski, Christian Theobalt, Marc Habermann. 2024-08-28. TEDRA: Text-based Editing of Dynamic and Photoreal Actors. https://arxiv.org/abs/2408.15995

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