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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.

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Pablo Vela

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36,719 görüntüleme • 1 yıl önce •via X (Twitter)

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Data & Analytics1 yıl önce

@_akhaliq @rerundotio @Gradio @_akhaliq, isn't it fascinating how technology evolves to make our work smoother? Tools like these push boundaries, sparking new levels of innovation in data annotation. 🚀 What other tools are you excited about that are changing your workflow? #DataScienceMagic

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HUDI2 yıl önce

Guess who’s not a fan of data ownership? 😅 When #Zuckerberg gives zero stars, you know the @datamaskwallet app is keeping your data safe from the big tech giants' grasp. 🚫📊 #DataPrivacy #BigTechNemesis art and creative by @Matteo_Frog

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Van Tuan Bui1 yıl önce

@rerundotio @Gradio This looks awesome! Exciting times for multimodal data tools!

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Nemesis1 yıl önce

@rerundotio @Gradio This is fascinating! Using Video Depth Anything and Segment Anything 2 for data annotation is a great step forward. Looking forward to seeing more updates!

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吭气十足吃包子并偷着乐的小学生欧尼酱1 yıl önce

@rerundotio @Gradio cool

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d'Artagnan-sha1 yıl önce

@rerundotio @Gradio Fascinating work, @pablovelagomez1! Leveraging cutting-edge models like Video Depth Anything and Segment Anything 2 to enable data annotation with depth and segmentation insights is a powerful step forward.

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Ayush Goyal1 yıl önce

@_akhaliq @rerundotio @Gradio insane demo. rerun + gradio stack looks powerful

Benzer Videolar

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.

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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 video inpainting and editing. abs: github:
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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 video inpainting and editing. abs: github:

AK

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Ever wondered what the real women behind ancient Greek statues looked like? I brought them back to life using: - Depth Anything (depth maps) - Omma (Code and 3D implementation) - Three.js
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Ever wondered what the real women behind ancient Greek statues looked like? I brought them back to life using: - Depth Anything (depth maps) - Omma (Code and 3D implementation) - Three.js

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Infinite depth trick! 🌑 This simple Depth Fade node is revolutionary for UEFN maps. Use it on a plane to create realistic shadows and faux depth on doors and anything else that needs that effect! 🛠️🎨 #UEFN #Fortnite
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Infinite depth trick! 🌑 This simple Depth Fade node is revolutionary for UEFN maps. Use it on a plane to create realistic shadows and faux depth on doors and anything else that needs that effect! 🛠️🎨 #UEFN #Fortnite

Sitka Creative 📱

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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.

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Depth Anything V2 This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more
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Depth Anything V2 This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and more

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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
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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

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41,103 görüntüleme • 3 yıl önce

new video for David Guetta using AI to create depth maps for more precise VFX control ! shout out to Spencer Sterling
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new video for David Guetta using AI to create depth maps for more precise VFX control ! shout out to Spencer Sterling

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34,268 görüntüleme • 1 yıl önce

Took it further with Omma AI, (Spline ) now it works on video, and everything working in a Web Browser. Drop a video, Depth Anything v2 calculates the depth map frame by frame, and Three.js renders a fully lit 3D mesh in real time. Depth estimation on video in the browser +Automatic baking pass + Dynamic lighting reacting to the geometry #vibecoding #threejs #webgl
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Took it further with Omma AI, (Spline ) now it works on video, and everything working in a Web Browser. Drop a video, Depth Anything v2 calculates the depth map frame by frame, and Three.js renders a fully lit 3D mesh in real time. Depth estimation on video in the browser +Automatic baking pass + Dynamic lighting reacting to the geometry #vibecoding #threejs #webgl

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147,479 görüntüleme • 2 ay önce

It’s been a long time since I last shared anything about the project, so here’s a very small and quick sneak peek 👀 I’ll have more to share very soon, with a full devlog and more regular work in progress😊 #gamedev #indiedev #indiegamedev #indiegame #gaming #game
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It’s been a long time since I last shared anything about the project, so here’s a very small and quick sneak peek 👀 I’ll have more to share very soon, with a full devlog and more regular work in progress😊 #gamedev #indiedev #indiegamedev #indiegame #gaming #game

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TikTok presents Depth Anything Unleashing the Power of Large-Scale Unlabeled Data paper page: demo: Depth Anything is trained on 1.5M labeled images and 62M+ unlabeled images jointly, providing the most capable Monocular Depth Estimation (MDE) foundation models with the following features: zero-shot relative depth estimation, better than MiDaS v3.1 (BEiTL-512) zero-shot metric depth estimation, better than ZoeDepth optimal in-domain fine-tuning and evaluation on NYUv2 and KITTI

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🤯 OneDiffusion: A versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. ✅ Text to Image ✅ Image to Depth ✅ Image to Segmentation ✅ Image to Pose ✅ FaceID ✅ Image to Multiview How to use & more👇

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Thrilled to introduce Video Depth Anything to support Depth Estimation for super-long videos (over 5 minutes). 👉It enjoys all the benefits of #DepthAnything: high-quality, fast, robust, etc. Proj Page:
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Thrilled to introduce Video Depth Anything to support Depth Estimation for super-long videos (over 5 minutes). 👉It enjoys all the benefits of #DepthAnything: high-quality, fast, robust, etc. Proj Page:

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62,168 görüntüleme • 9 ay önce

Happy 4th 🇺🇸!! I have a preview for my next release (assembly101 is massive, so I need to push until next week). But in the meantime, check out the link below to try out the 🚧 work in progress using Rerun and Gradio Links below 👇
0:25

Happy 4th 🇺🇸!! I have a preview for my next release (assembly101 is massive, so I need to push until next week). But in the meantime, check out the link below to try out the 🚧 work in progress using Rerun and Gradio Links below 👇

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34,186 görüntüleme • 1 yıl önce

Another great news from AIT Protocol, official platform will be released on 8th Jan at 2 PM UTC. Here’s a sneak peek of the $AIT Data Annotation Platform designed to train #AI models 🤖
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Another great news from AIT Protocol, official platform will be released on 8th Jan at 2 PM UTC. Here’s a sneak peek of the $AIT Data Annotation Platform designed to train #AI models 🤖

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Nvidia released Describe Anything! a new method that can generate detailed descriptions for specific areas in images and videos using points, boxes, scribbles, or masks

Dreaming Tulpa 🥓👑

52,217 görüntüleme • 1 yıl önce