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Track Anything: Segment Anything Meets Videos Track-Anything is a flexible and interactive tool for video object tracking and segmentation suitable for: - Video object tracking and segmentation with shot changes. - Visualized development and data annnotation for video object tracking and segmentation. - Object-centric downstream video tasks, such as...

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AK

519,139 subscribers

578,691 Aufrufe • vor 3 Jahren •via X (Twitter)

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10 Kommentare

Profilbild von AK
AKvor 3 Jahren

demo:

Profilbild von AK
AKvor 3 Jahren

Multiple Object Tracking and Segmentation (with XMem)

Profilbild von AK
AKvor 3 Jahren

Video Object Tracking and Segmentation with Shot Changes (with XMem)

Profilbild von Smoke-away
Smoke-awayvor 3 Jahren

Neat 😎

Profilbild von Bhack
Bhackvor 3 Jahren

@EMostaque There is an alternative version with AOT networks

Profilbild von Nicolai Klemke
Nicolai Klemkevor 3 Jahren

This is fine

Profilbild von Brett Adcock
Brett Adcockvor 3 Jahren

This is 😎

Profilbild von erictang.eth 🍕🍾
erictang.eth 🍕🍾vor 3 Jahren

Never too late to get into the video meme game. Looking forward to wasting more time.

Profilbild von Lior Golgher
Lior Golghervor 3 Jahren

Could it be used for markerless pose estimation? DeepLabCut style?

Profilbild von FinnT730
FinnT730vor 3 Jahren

@Yokohara_h Ok, now to see if I can get working locally XD

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Tracking Anything with Decoupled Video Segmentation paper page: Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation.
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Tracking Anything with Decoupled Video Segmentation paper page: Training data for video segmentation are expensive to annotate. This impedes extensions of end-to-end algorithms to new video segmentation tasks, especially in large-vocabulary settings. To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation. Due to this design, we only need an image-level model for the target task (which is cheaper to train) and a universal temporal propagation model which is trained once and generalizes across tasks. To effectively combine these two modules, we use bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation. We show that this decoupled formulation compares favorably to end-to-end approaches in several data-scarce tasks including large-vocabulary video panoptic segmentation, open-world video segmentation, referring video segmentation, and unsupervised video object segmentation.

AK

305,683 Aufrufe • vor 3 Jahren

The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️
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The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️

AI at Meta

186,348 Aufrufe • vor 3 Jahren

Bussing plates after a healthy meal. Our robot only eats plastic fruits and veggies for now. Video segmentation accomplished using Track-Anything, an architecture combining Meta's SAM to generate a zero-shot segmentation prompt and Xmem to enable long-horizon, temporally consistent video segmentation masks.
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Bussing plates after a healthy meal. Our robot only eats plastic fruits and veggies for now. Video segmentation accomplished using Track-Anything, an architecture combining Meta's SAM to generate a zero-shot segmentation prompt and Xmem to enable long-horizon, temporally consistent video segmentation masks.

Watney Robotics

14,856 Aufrufe • vor 2 Jahren

Meta’s Segment Anything Model (SAM) 3.1 is now available on Meta Model API, giving developers a fast and lightweight model for detection, segmentation and tracking in a single call on inference tuned for SAM 3.1's architecture. Use a short phrase to find objects in images and video. One API call returns detections, pixel-precise segmentation masks, and identity-preserving video tracks. Learn more and start building:
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Meta’s Segment Anything Model (SAM) 3.1 is now available on Meta Model API, giving developers a fast and lightweight model for detection, segmentation and tracking in a single call on inference tuned for SAM 3.1's architecture. Use a short phrase to find objects in images and video. One API call returns detections, pixel-precise segmentation masks, and identity-preserving video tracks. Learn more and start building:

Meta for Developers

570,173 Aufrufe • vor 20 Tagen

Here’s a sneak peek using Rerun and Gradio for data annotation. It uses Video Depth Anything and Segment Anything 2 under the hood to generate segmentation masks and depth maps/point clouds. More to share next week.
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Here’s a sneak peek using Rerun and Gradio for data annotation. It uses Video Depth Anything and Segment Anything 2 under the hood to generate segmentation masks and depth maps/point clouds. More to share next week.

Pablo Vela

36,719 Aufrufe • vor 1 Jahr

SAM 2 from Meta FAIR is the first unified model for real-time, promptable object segmentation in images & videos. Using the model in our web-based demo you can segment, track and apply effects to objects in video in just a few clicks. Try SAM 2 ➡️
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SAM 2 from Meta FAIR is the first unified model for real-time, promptable object segmentation in images & videos. Using the model in our web-based demo you can segment, track and apply effects to objects in video in just a few clicks. Try SAM 2 ➡️

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88,918 Aufrufe • vor 2 Jahren

Three new open-source models just landed in ComfyUI natively: → Gemma 4 (Google DeepMind) - multimodal LLM handling text, image, audio, and video input with built-in step-by-step reasoning mode → VOID (Netflix) - video object removal that also erases shadows, reflections, and physical interactions caused by the removed subject → BiRefNet - high-res background & object segmentation, one of the most-used segmentation models in the ecosystem Workflows and blog linked below 👇
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Three new open-source models just landed in ComfyUI natively: → Gemma 4 (Google DeepMind) - multimodal LLM handling text, image, audio, and video input with built-in step-by-step reasoning mode → VOID (Netflix) - video object removal that also erases shadows, reflections, and physical interactions caused by the removed subject → BiRefNet - high-res background & object segmentation, one of the most-used segmentation models in the ecosystem Workflows and blog linked below 👇

ComfyUI

218,749 Aufrufe • vor 4 Monaten

🚀 The GeoAI QGIS Plugin is here 🔥 You can run Moondream vision-language models, object detection, image segmentation (SAM 3), and even train your own geospatial segmentation model end-to-end. Website: GitHub: Short demo: Full video tutorial: #QGIS #GeoAI #SAM3 #Geospatial #DeepLearning #ComputerVision #OpenSource #Python
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🚀 The GeoAI QGIS Plugin is here 🔥 You can run Moondream vision-language models, object detection, image segmentation (SAM 3), and even train your own geospatial segmentation model end-to-end. Website: GitHub: Short demo: Full video tutorial: #QGIS #GeoAI #SAM3 #Geospatial #DeepLearning #ComputerVision #OpenSource #Python

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⚡️Did you know YOLOv8 is an anchor-free object detection model, and YOLOv5 is an anchor-based object detection model? We conduct an in-depth comparison between them and carry out inference for detection and segmentation. #yolo #yolov8 #yolov5 #ai
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⚡️Did you know YOLOv8 is an anchor-free object detection model, and YOLOv5 is an anchor-based object detection model? We conduct an in-depth comparison between them and carry out inference for detection and segmentation. #yolo #yolov8 #yolov5 #ai

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Agricultural Field Boundary Delineation (Instance Segmentation) with GeoAI Learn an end-to-end GeoAI workflow for agricultural field boundary delineation using instance segmentation and the Fields of the World dataset ( This workflow can be adapted to detect other object types (not just field boundaries) as long as training data is available. Video tutorial: Notebook: Web app: #geoai #geospatial #deeplearing

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HDMI (HumanoiD iMitation for Interaction) is a framework enabling humanoid robots to learn whole-body object interaction skills from monocular RGB human videos. It extracts and retargets human poses and object trajectories using GVHMR and LocoMujoco, building reference datasets with contact annotations. The data is used to train an RL policy via robot-object co-tracking. HDMI achieved 67 consecutive door traversals.
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HDMI (HumanoiD iMitation for Interaction) is a framework enabling humanoid robots to learn whole-body object interaction skills from monocular RGB human videos. It extracts and retargets human poses and object trajectories using GVHMR and LocoMujoco, building reference datasets with contact annotations. The data is used to train an RL policy via robot-object co-tracking. HDMI achieved 67 consecutive door traversals.

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mirrash.eth 🦇🔊

302,195 Aufrufe • vor 3 Monaten

Segment Anything is fun and powerful but still requires full mask annotation for supervised training. Our new work MaskFreeVIS appearing at #CVPR2023 shows we can get accurate video instance segmentation WITHOUT any video or image mask annotations. More at
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Segment Anything is fun and powerful but still requires full mask annotation for supervised training. Our new work MaskFreeVIS appearing at #CVPR2023 shows we can get accurate video instance segmentation WITHOUT any video or image mask annotations. More at

Fisher Yu

41,179 Aufrufe • vor 3 Jahren

.KeenTools released its geometry-based object and camera tracking solution GeoTracker for Houdini in open beta. Download:
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.KeenTools released its geometry-based object and camera tracking solution GeoTracker for Houdini in open beta. Download:

80 LEVEL

18,352 Aufrufe • vor 5 Monaten

I'm excited to share our new work Diffusion as Shader (DaS), a versatile controllable video generation method for various tasks: object manipulation, camera control, mesh-to-video, and motion transfer. Project page: Github:
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I'm excited to share our new work Diffusion as Shader (DaS), a versatile controllable video generation method for various tasks: object manipulation, camera control, mesh-to-video, and motion transfer. Project page: Github:

Yuan Liu

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Ask-Anything, tool for chatting about video with chatGPT, miniGPT4 and StableLM github: Gradio demo:
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Ask-Anything, tool for chatting about video with chatGPT, miniGPT4 and StableLM github: Gradio demo:

AK

143,649 Aufrufe • vor 3 Jahren