VACE-Annotators
| Property | Value |
|---|---|
| Author | ali-vilab |
| License | Apache-2.0 |
| Paper | arXiv:2503.07598 |
| Release Date | March 2025 |
What is VACE-Annotators?
VACE-Annotators is a crucial preprocessing component of the VACE (Video Creation and Editing) framework, designed to prepare input data for various video manipulation tasks. It serves as the foundation for VACE's all-in-one video creation and editing capabilities, enabling tasks such as reference-to-video generation (R2V), video-to-video editing (V2V), and masked video-to-video editing (MV2V).
Implementation Details
The annotator module is implemented in Python and requires specific dependencies including PyTorch 2.5.1 and CUDA 12.4. It processes input videos and generates necessary annotations for tasks like depth estimation, inpainting, and other video manipulation operations.
- Supports multiple preprocessing tasks through a unified interface
- Generates masks, depth maps, and other annotations required for video editing
- Integrates with both Wan2.1 and LTX-Video base models
- Compatible with various video resolutions and formats
Core Capabilities
- Video depth estimation preprocessing
- Inpainting mask generation
- Bbox-based region selection
- Support for Move-Anything, Swap-Anything, and Animate-Anything features
- Flexible task configuration through JSON configs
Frequently Asked Questions
Q: What makes this model unique?
VACE-Annotators is unique in its ability to provide comprehensive preprocessing capabilities for a wide range of video editing tasks, serving as an essential component in the VACE pipeline that enables seamless integration between different video manipulation operations.
Q: What are the recommended use cases?
The model is particularly useful for developers and researchers working on video editing applications, supporting tasks such as depth-aware editing, object removal through inpainting, and reference-based video generation. It's designed to work within the VACE framework for both research and practical applications.
