OmniCreator: Self-Supervised Unified Generation with Universal Editing
Authors: name: Haodong Chen, name: Lan Wang, name: Harry Yang, name: Ser-Nam Lim

We introduce OmniCreator, a novel framework that can conduct text-prompted
unified (image+video) generation as well as editing all in one place.
OmniCreator acquires generative and universal editing capabilities in a
self-supervised manner, taking original text-video pairs as conditions while
utilizing the same video as a denoising target to learn the semantic
correspondence between video and text. During inference, when presented with a
text prompt and a video, OmniCreator is capable of generating a target that is
faithful to both, achieving a universal editing effect that is unconstrained as
opposed to existing editing work that primarily focuses on certain editing
types or relies on additional controls (e.g., structural conditions, attention
features, or DDIM inversion). On the other hand, when presented with a text
prompt only, OmniCreator becomes generative, producing high-quality video as a
result of the semantic correspondence learned. Importantly, we found that the
same capabilities extend to images as is, making OmniCreator a truly unified
framework. Further, due to the lack of existing generative video editing
benchmarks, we introduce the OmniBench-99 dataset, designed to evaluate the
performance of generative video editing models comprehensively. Extensive
experiments demonstrate that OmniCreator exhibits substantial superiority over
all other models.