Abstract
Generating video background that tailors to foreground subject motion is an important problem for the movie industry and visual effects community. This task involves synthesizing background that aligns with the motion and appearance of the foreground subject, while also complies with the artist's creative intention. We introduce ActAnywhere, a generative model that automates this process which traditionally requires tedious manual efforts. Our model leverages the power of large-scale video diffusion models, and is specifically tailored for this task. ActAnywhere takes a sequence of foreground subject segmentation as input and an image that describes the desired scene as condition, to produce a coherent video with realistic foreground-background interactions while adhering to the condition frame. We train our model on a large-scale dataset of human-scene interaction videos. Extensive evaluations demonstrate the superior performance of our model, significantly outperforming baselines. Moreover, we show that ActAnywhere generalizes to diverse out-of-distribution samples, including non-human subjects. Please visit our project webpage at https://actanywhere.github.io.
Community
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models (2023)
- VASE: Object-Centric Appearance and Shape Manipulation of Real Videos (2024)
- VideoBooth: Diffusion-based Video Generation with Image Prompts (2023)
- Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer (2023)
- Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation (2023)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
Very impressive work. So, can the open-source code for this work be found?
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper