Video-Motion-Customization
VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models (CVPR 2024) (by HyeonHo99)
TokenFlow
Official Pytorch Implementation for "TokenFlow: Consistent Diffusion Features for Consistent Video Editing" presenting "TokenFlow" (ICLR 2024) (by omerbt)
Video-Motion-Customization | TokenFlow | |
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3 | 1 | |
117 | 1,468 | |
- | - | |
7.2 | 6.2 | |
about 1 month ago | 4 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Video-Motion-Customization
Posts with mentions or reviews of Video-Motion-Customization.
We have used some of these posts to build our list of alternatives
and similar projects.
- Code for video motion customization has been released!
-
VMC: Video Motion Customization
Text-to-video diffusion models have advanced video generation significantly. However, customizing these models to generate videos with tailored motions presents a substantial challenge. In specific, they encounter hurdles in (a) accurately reproducing motion from a target video, and (b) creating diverse visual variations. For example, straightforward extensions of static image customization methods to video often lead to intricate entanglements of appearance and motion data. To tackle this, here we present the Video Motion Customization (VMC) framework, a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive frames as a motion reference. The diffusion process then preserves low-frequency motion trajectories while mitigating high-frequency motion-unrelated noise in image space. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts. Our codes, data and the project demo can be found at https://video-motion-customization.github.io/
Code: https://github.com/HyeonHo99/Video-Motion-Customization
TokenFlow
Posts with mentions or reviews of TokenFlow.
We have used some of these posts to build our list of alternatives
and similar projects.
-
TokenFlow has been Released
Code: https://github.com/omerbt/TokenFlow
What are some alternatives?
When comparing Video-Motion-Customization and TokenFlow you can also consider the following projects:
MotionDirector - MotionDirector: Motion Customization of Text-to-Video Diffusion Models.
LAMP - Official implement code of LAMP: Learn a Motion Pattern by Few-Shot Tuning a Text-to-Image Diffusion Model (Few-shot-based text-to-video diffusion)