TEDRA: Text-based Editing of Dynamic and Photoreal Actors
<|reference_start|>TEDRA: Text-based Editing of Dynamic and Photoreal Actors: 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.<|reference_end|>
@article{sunagad2024tedra:,
    title={TEDRA: Text-based Editing of Dynamic and Photoreal Actors},
    author={Basavaraj Sunagad, Heming Zhu, Mohit Mendiratta, Adam Kortylewski,
  Christian Theobalt, Marc Habermann},
    journal={arXiv preprint arXiv:2408.15995},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15995},
    primaryClass={cs.CV}
}