Track-Anything
sam-clip
| Track-Anything | sam-clip | |
|---|---|---|
| 16 | 1 | |
| 6,113 | 20 | |
| - | - | |
| 8.1 | 5.4 | |
| 3 months ago | 4 months ago | |
| Python | Python | |
| MIT License | MIT License |
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Track-Anything
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Keying/masking person on a footage
I was doing rotoscoping for a silhouette of a girl dancing in front of a building, then I saw this amazing tool: https://github.com/gaomingqi/Track-Anything
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Advice for multi-animal tracking for scientific research?
My question is, how can we modernize this pipeline? We've experimented a bit with the new SAM-based track-anything tool, and it seems promising, but we actually don't want to "track anything", we only want to track fishes. What would you do in 2023, to extract tracks of one specific class of object from long video datasets? I'm hoping for any advice at all.
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[D] Which open source models can replicate wonder dynamics's drag'n'drop cg characters?
The Track-Anything tool already implements this
- Tutorial for Track-Anything, an interactive tool to segment, track, and inpaint anything in videos.
- Github for Track Anything
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Segment Anything for Video - Track Anything! 🤖
Segment Anything for Video - Track Anything! 🤖 With this tool, you can automatically isolate objects, make edits using inpainting, and track objects with precision. It's a game-changer for creative projects. Even though it does not work well with the shadows yet, we expect a rapid evolution of these technologies. Github : https://github.com/gaomingqi/Track-Anything)
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How to adapt an existing Python project to my specific use case without bringing in unnecessary dependencies or reinventing the wheel
I would like to know if there are any guidelines to follow when adapting an existing Python project for my own use-case. Specifically, I want to customize the output of the Track-Anything project by incorporating my own processing steps. However, I do not want to import the entire codebase. Rather, I only want to import the minimum amount of code necessary to produce the same output with object tracking, without having to reimplement functions that are already available.
- Track-Anything should get implemented in kdelive
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SUSTech VIP Lab Proposes Track Anything Model (TAM) That Achieves High-Performance Interactive Tracking and Segmentation in Videos
Here is the GitHub: https://github.com/gaomingqi/track-anything
sam-clip
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
autodistill - Images to inference with no labeling (use foundation models to train supervised models).
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
autodistill-metaclip - MetaCLIP module for use with Autodistill.
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
anylabeling - Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!!
sd-webui-segment-anything - Segment Anything for Stable Diffusion WebUI
Instruct2Act - Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model
SegmentAnythingin3D - Segment Anything in 3D with NeRFs (NeurIPS 2023)