正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

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

2,490 subscribers

36,719 次观看 • 1 年前 •via X (Twitter)

教育艺术科学技术

Anya Rossi• Live Now

Private livecam show

7 条评论

Data & Analytics 的头像

Data & Analytics1 年前

@_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

HUDI 的头像

HUDI2 年前

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

Van Tuan Bui 的头像

Van Tuan Bui1 年前

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

Nemesis 的头像

Nemesis1 年前

@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!

吭气十足吃包子并偷着乐的小学生欧尼酱的头像

吭气十足吃包子并偷着乐的小学生欧尼酱1 年前

@rerundotio @Gradio cool

d'Artagnan-sha 的头像

d'Artagnan-sha1 年前

@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.

Ayush Goyal 的头像

Ayush Goyal1 年前

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

相关视频

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.

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 次观看 • 2 年前

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:

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

578,619 次观看 • 3 年前

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

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

Joseph Azar

74,370 次观看 • 1 个月前

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

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 📱

119,243 次观看 • 4 个月前

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.

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

305,633 次观看 • 2 年前

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

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

AK

114,537 次观看 • 2 年前

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

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

41,103 次观看 • 3 年前

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

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

JAK BANNON

34,268 次观看 • 1 年前

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

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

Joseph Azar

147,479 次观看 • 2 个月前

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

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

A Silent Desolation

15,267 次观看 • 23 天前

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

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

AK

600,018 次观看 • 2 年前

🤯 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👇

🤯 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👇

Gradio

11,820 次观看 • 1 年前

Announcing our new and stable release of Segment Anything for Microscopy! We introduce new features and improve the tool to segment microscopy data with a few clicks, like the mitochondria in the video, first segmented automatically and then corrected interactively.

Announcing our new and stable release of Segment Anything for Microscopy! We introduce new features and improve the tool to segment microscopy data with a few clicks, like the mitochondria in the video, first segmented automatically and then corrected interactively.

Constantin Pape

45,374 次观看 • 2 年前

🚀 Excited to share a new tutorial on how to detect buildings 🏘️using the Segment Anything Model 2 (SAM 2) with Segment-Geospatial! You can choose interactive segmentation or utilize a GeoJSON file with building centroids or bounding boxes to do automated building detection. 📹 Check out the video tutorial: 📓 Explore the notebook: #Geospatial #OpenSource #AI #MachineLearning

🚀 Excited to share a new tutorial on how to detect buildings 🏘️using the Segment Anything Model 2 (SAM 2) with Segment-Geospatial! You can choose interactive segmentation or utilize a GeoJSON file with building centroids or bounding boxes to do automated building detection. 📹 Check out the video tutorial: 📓 Explore the notebook: #Geospatial #OpenSource #AI #MachineLearning

Qiusheng Wu

72,345 次观看 • 1 年前

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:

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:

Bingyi Kang

25,464 次观看 • 1 年前

Here’s a sneak peek at the new Meta Ray-Ban smart display glasses and the apps it uses such as Spotify, Instagram and WhatsApp.

Here’s a sneak peek at the new Meta Ray-Ban smart display glasses and the apps it uses such as Spotify, Instagram and WhatsApp.

Nathie 🔜 AWE

62,168 次观看 • 9 个月前

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 👇

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 👇

Pablo Vela

34,186 次观看 • 1 年前

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 🤖

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 🤖

IBCIG

44,821 次观看 • 2 年前

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

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 次观看 • 1 年前