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.
66cfde5570b7b22757960c9e
Basavaraj Sunagad
admin_assigned
2024-08-29T07:39:38.468Z
656df2b453703dd78fcd8072
/avatars/1a7d76bb345804821146d064be1de203.svg
Basavaraj Sunagad
user
brsunagad
66cfde5570b7b22757960c9f
Heming Zhu
admin_assigned
2024-08-29T07:39:45.146Z
64a41b3da3f7783f6c13bced
https://cdn-avatars.huggingface.co/v1/production/uploads/64a41b3da3f7783f6c13bced/M-w8PUAs64hG2IRguZuf7.jpeg
Heming  Zhu
user
kv2000
66cfde5570b7b22757960ca0
Mohit Mendiratta
admin_assigned
2024-08-29T07:39:51.254Z
6363ae61ff4b318d1b77a337
/avatars/6a15518c49c66fef2e4f8b4eb2f56936.svg
Mohit Mendiratta
user
Mohit10051994
66cfde5570b7b22757960ca1
Adam Kortylewski
66cfde5570b7b22757960ca2
Christian Theobalt
66cfde5570b7b22757960ca3
Marc Habermann
claimed_verified
2024-08-29T08:06:56.051Z
66d02ac970b7b22757b2c631
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/-71YM7txHBu67bfS_Re8-.jpeg
Marc Habermann
user
MarcHabermann
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.
TEDRA allows text-based editing of 3D avatars while maintaining high fidelity, space-time coherence, and dynamics by personalizing a generative diffusion model with a novel scoring-distillation sampling method.
generative diffusion model fine-tuning Personalized Normal Aligned Score Distillation Sampling (PNA-SDS) model-based guidance framework time step annealing 3D avatars high fidelity space-time coherence dynamics skeletal pose view control