

HOI Swap
Overview :
HOI-Swap is a diffusion model-based video editing framework specializing in tackling the complexities of hand-object interactions in video editing. This model, trained through self-supervision, enables seamless object swapping within a single frame. It also learns to adjust hand interaction patterns based on object attribute changes, such as grip style. The second stage extends single-frame editing to an entire video sequence, achieving high-quality video editing through motion alignment and video generation.
Target Users :
HOI-Swap is suitable for professional video editors and researchers who require precise handling of hand-object interactions, particularly in scenarios involving complex human-object interactions. This includes video creators, post-production film editors, and virtual reality content developers.
Use Cases
Video creators use HOI-Swap to replace objects in videos to create more realistic scenes.
Post-production film editors utilize HOI-Swap to adjust hand movements in videos to match the replaced objects.
Virtual reality content developers use HOI-Swap to implement more natural hand-object interactions within virtual environments.
Features
Precise Object Swapping: Seamlessly replace objects in a video using a reference image provided by the user.
Hand-Object Interaction Awareness: The model adjusts hand movements based on object shape and function changes.
Self-Supervised Training: Learns using self-generated training data without requiring external labeled data.
Motion Alignment: Achieves motion consistency between the new video sequence and the original video by using sampled motion points and optical flow techniques.
Video Reconstruction: Reconstructs a complete video sequence from a deformed one.
High-Quality Video Output: Generates high-quality video edit results with realistic hand-object interactions.
How to Use
1. Choose a video you want to edit and have a reference image of the object to replace.
2. Use the first-stage model of HOI-Swap to perform single-frame object replacement in the video.
3. Adjust the hand interaction movements according to the object attribute changes to ensure natural interaction with the new object.
4. Utilize the second-stage model to extend single-frame editing to the entire video sequence.
5. Achieve motion consistency between the new video sequence and the original video by using sampled motion points and optical flow techniques.
6. Use the video diffusion model to reconstruct a complete video sequence from the deformed one.
7. Review the generated video editing results, ensuring the realism of the hand-object interaction and overall video quality.
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