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Google just revealed an ABSOLUTE depth estimation model 🤯 As opposed to recent depth models (Marigold, PatchFusion) which aim for maximum details, DMD aims to estimate the ABSOLUTE depth (in meters) within the image More details below ⬇️⬇️

Alex Carlier

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199,532 görüntüleme • 2 yıl önce •via X (Twitter)

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PatchFusion was just released. Compared to ZoeDepth, it predicts depth maps with much finer details, just look at the comparison below! 🤯 Its main contribution is a new Global-to-Local module and Consistency-Aware Training. More examples below (with code) ⬇️⬇️

PatchFusion was just released. Compared to ZoeDepth, it predicts depth maps with much finer details, just look at the comparison below! 🤯 Its main contribution is a new Global-to-Local module and Consistency-Aware Training. More examples below (with code) ⬇️⬇️

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Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

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Introducing 🛹 RollingDepth 🛹 — a universal monocular depth estimator for arbitrarily long videos! Our paper, “Video Depth without Video Models,” delivers exactly that, setting new standards in temporal consistency. Check out more details in the thread 🧵

Introducing 🛹 RollingDepth 🛹 — a universal monocular depth estimator for arbitrarily long videos! Our paper, “Video Depth without Video Models,” delivers exactly that, setting new standards in temporal consistency. Check out more details in the thread 🧵

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A close-up only works when depth feels real. In this shot, we pushed blur and shading to shape the depth of field (DoF), not just for realism, but to deepen the emotion within the frame. Produced by Otsu Labs & Studio Munbou

A close-up only works when depth feels real. In this shot, we pushed blur and shading to shape the depth of field (DoF), not just for realism, but to deepen the emotion within the frame. Produced by Otsu Labs & Studio Munbou

Otsu Labs

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It's also important to verify the token To safeguard against scams, rug pulls, and honeypots, it’s essential to confirm the token's contract Just enter the contract address to access in-depth details about the token’s security:

It's also important to verify the token To safeguard against scams, rug pulls, and honeypots, it’s essential to confirm the token's contract Just enter the contract address to access in-depth details about the token’s security:

Leshka.eth ⛩

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Scientists at University of Western Australia set new record for deepest fish ever filmed at a depth of 8,336 meters. During an expedition that took place in August 2022, the research vessel DSSV Pressure Drop embarked on a two-month journey to investigate trenches in the Pacific Ocean near Japan. The primary objective of the expedition was to examine the populations of deep-sea fish. While conducting filming activities, the research team unexpectedly encountered a previously unknown species of snailfish at an astounding depth of 8,336 meters, equivalent to over 27,000 feet, specifically in the Izu-Ogasawara Trench. At a depth of 8,022 meters, scientists successfully obtained two fish from traps. This achievement is noteworthy because it includes the capture of a snailfish species known as Pseudoliparis belyaevi. Notably, this is the first time a fish has been collected from depths exceeding 8,000 meters, as these snailfish were previously observed solely at a shallower depth of 7,703 meters.

Scientists at University of Western Australia set new record for deepest fish ever filmed at a depth of 8,336 meters. During an expedition that took place in August 2022, the research vessel DSSV Pressure Drop embarked on a two-month journey to investigate trenches in the Pacific Ocean near Japan. The primary objective of the expedition was to examine the populations of deep-sea fish. While conducting filming activities, the research team unexpectedly encountered a previously unknown species of snailfish at an astounding depth of 8,336 meters, equivalent to over 27,000 feet, specifically in the Izu-Ogasawara Trench. At a depth of 8,022 meters, scientists successfully obtained two fish from traps. This achievement is noteworthy because it includes the capture of a snailfish species known as Pseudoliparis belyaevi. Notably, this is the first time a fish has been collected from depths exceeding 8,000 meters, as these snailfish were previously observed solely at a shallower depth of 7,703 meters.

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Upscale-A-Video was just released and it's so good! 🤯 It's a temporal-consistent Diffusion Model for video Super-Resolution, and has some of the best results I've ever seen, look at how sharp those lines become! More details below ⬇️⬇️

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Issue 4 of Interfaces just released! This issue is about building an animated sign-in dialog. It covers everything that goes into building it, from the animations to the small but important details. It's one of the most in-depth issues yet, full of code snippets and demos.

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I just want to show you what an explosion of 1000 kg of explosives looks like at a depth of 20-30 meters. The video shows the destruction of a German bottom mine in the waters of the Feodosiysky Bay in the Crimea.

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We just revamped session key management for the Abstract Global Wallet. You can now see in-depth details on what session keys and permissions are active for apps that you've used. This will make wallet management more secure and transparent for all users going forward.

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We’re thrilled to announce our Innovation Showcase programme! From 28-29 September, we’ll be joined by inspirational leaders in the creative industries for thought-provoking panels, in-depth conversations and special performances ⬇️

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“What more can we really do?” 🗣️ Just over 15,000 people attended this afternoon’s home tie against Watford - a similar number to last week’s game. 🫣 In episode 8 of the Only Swans Podcast, Carwyn and Oli discussed this issue in-depth and questioned what more could really be done in an already exciting period for the club. 🦢 ⬇️ Listen In Full ⬇️ 🔴 YouTube: 🟢 Spotify: 🟣 Apple Podcasts:

Only Swans

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I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant

I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant

Chubby♨️

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名画を立体的に鑑賞する方法 Nano Banana Pro (深度/法線マップ生成)→ WebGL(Three.js) スマホでも体験できます👉 【プロンプト】（法線マップ用） Generate a surface normal map from the input artwork image. Output one RGB image where each pixel encodes the surface normal direction in standard tangent-space format: •R = X direction •G = Y direction •B = Z direction Requirements: •Maintain the same resolution and aspect ratio as the input image. •Normal directions must follow the local geometry implied by brush strokes, shading, and contours. •Do NOT add new artistic elements or repaint the scene. •Only infer plausible micro-geometry and surface orientation. •Ensure smooth, consistent normal transitions without noise. （深度マップ用） Generate a depth map from the input artwork image. Output a single grayscale depth map where: •White = near to the viewer •Black = far from the viewer Requirements: •Maintain the same resolution and aspect ratio as the input image. •Depth must be smooth, continuous, and physically plausible. •Infer only geometric depth that could reasonably exist in the original artwork. •Do NOT add new objects or details. •Do NOT stylize, repaint, or reinterpret the scene. •Avoid texture noise; focus solely on depth structure.

名画を立体的に鑑賞する方法 Nano Banana Pro (深度/法線マップ生成)→ WebGL(Three.js) スマホでも体験できます👉 【プロンプト】（法線マップ用） Generate a surface normal map from the input artwork image. Output one RGB image where each pixel encodes the surface normal direction in standard tangent-space format: •R = X direction •G = Y direction •B = Z direction Requirements: •Maintain the same resolution and aspect ratio as the input image. •Normal directions must follow the local geometry implied by brush strokes, shading, and contours. •Do NOT add new artistic elements or repaint the scene. •Only infer plausible micro-geometry and surface orientation. •Ensure smooth, consistent normal transitions without noise. （深度マップ用） Generate a depth map from the input artwork image. Output a single grayscale depth map where: •White = near to the viewer •Black = far from the viewer Requirements: •Maintain the same resolution and aspect ratio as the input image. •Depth must be smooth, continuous, and physically plausible. •Infer only geometric depth that could reasonably exist in the original artwork. •Do NOT add new objects or details. •Do NOT stylize, repaint, or reinterpret the scene. •Avoid texture noise; focus solely on depth structure.

ハシモトタクマ / FunTech

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Ecom Brands I spent more than 100 hours making an entire masterclass about Advertising on Meta and Google. The most in depth content I have ever released for FREE. RT & Comment "Masterclass" and I will send it to you. (Must be following)

Ecom Brands I spent more than 100 hours making an entire masterclass about Advertising on Meta and Google. The most in depth content I have ever released for FREE. RT & Comment "Masterclass" and I will send it to you. (Must be following)

Luke Bielby

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Meet Cosmos—a young loggerhead sea turtle tracked with the Connecta IoT Network.🐢In partnership with DEKAMER the specially designed device, linked to the Connecta IoT Network, sends position, maximum depth, surface time, and depth profile data from sea to space, giving scientists the insights they need to protect migration routes and habitats. And we’re just getting started. Soon, we’ll be tracking and protecting even more species, both on land and at sea. 🐫🐘🐅🐄 #WeArePlanS #PlanS #ConnectaIoT #DEKAMER

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The UFC is back in Rio de Janeiro, and we're taking you with us! 🇧🇷 Trade $BLOCK on CUBE for a chance to win an all-expenses-paid trip to Brazil to attend #UFC301, which includes: 🎟️ Tickets to the #UFC301 event. 🤝 Meet & greet with UFC athletes. 🏟️ An exclusive UFC octagon tour. Details below. ⬇️

The UFC is back in Rio de Janeiro, and we're taking you with us! 🇧🇷 Trade $BLOCK on CUBE for a chance to win an all-expenses-paid trip to Brazil to attend #UFC301, which includes: 🎟️ Tickets to the #UFC301 event. 🤝 Meet & greet with UFC athletes. 🏟️ An exclusive UFC octagon tour. Details below. ⬇️

Blockasset

19,251 görüntüleme • 2 yıl önce

I did this test render earlier as i realized its a nice behind the scenes way to see the full volume and depth of the models and gas clouds. I thought in the future i might make more of these as Supporter exclusive little bonuses for renders i made. what do you all think?

I did this test render earlier as i realized its a nice behind the scenes way to see the full volume and depth of the models and gas clouds. I thought in the future i might make more of these as Supporter exclusive little bonuses for renders i made. what do you all think?

ToxicSkunksune Commissions Open

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