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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.