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Are we done with object detection? What about tiny objects beyond 200 meters? 🔎 Telescope 🔭 addresses long-range perception by explicitly tackling extreme scale imbalance ⚖️ in images. It hinges on a learnable hyperbolic foveation transform from a low-resolution image, magnifying distant regions 🔍 while compressing nearby ones -... show more

Felix Heide

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188,663 views • 4 months ago •via X (Twitter)

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Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,849 views • 1 year ago

Today, let’s talk about the possibilities of We’re living in a generation where IP is exploding. Web2 and Web3 are all about IP and everything is tied to digital identity. Be it a PFP or a brand, it carries personality and value. So let’s use NFTs as an example. NFTs are built around IPs. Characters that build the community and the world of a project. (I’ve been in the NFT space for 3-4 years. So yes, I’ve seen the cycle.) Some questions : - Beyond the image, how do we bring utility to every character? - What stories do they hold? - What personalities do they have? - Can each one have a digital identity beyond just being a PFP? - And most importantly : how can it talk to millions of people without the owner replying to every message? That’s just difficult to scale. Exactly the reason why we are building To bring IPs to life. Every character deserves a story. Deserves to connect emotionally anytime, with anyone. (Using daniel matsunaga 💎👋🏻💎 space io in this example)

Clement | Imaginary Ones | Bubio.ai

10,326 views • 1 year ago

Impossible Nature. My goal here was to generate images with a low quality aesthetic that are slightly overexposed and then apply handheld camera motion to it with focal shifts, quick zoom in/out + tracking. I just wanted to see how far I could push realism from an alternate universe. Nano Banana Pro let's you construct animals out of practically anything, it's wild. What do you think? Does it look believable? Images generated with Google's Nano Banana Pro. Image to video using Kling AI 2.5 1 - A crab made entirely out of seashells captured on the beach at night.

Impossible Nature. My goal here was to generate images with a low quality aesthetic that are slightly overexposed and then apply handheld camera motion to it with focal shifts, quick zoom in/out + tracking. I just wanted to see how far I could push realism from an alternate universe. Nano Banana Pro let's you construct animals out of practically anything, it's wild. What do you think? Does it look believable? Images generated with Google's Nano Banana Pro. Image to video using Kling AI 2.5 1 - A crab made entirely out of seashells captured on the beach at night.

Travis Davids

16,750 views • 9 months ago

This is some quietly impressive work on making video world models actually controllable in 4D space. VerseCrafter lets you take an input image, use something like Blender to animate the 3D camera path and object trajectories, then uses that to condition generation. Scribbling in 2D feels so crude in comparison. The authors represent everything in a shared 4D world state - static background as a point cloud, moving objects as 3D gaussian trajectories. The gaussians are an interesting choice because they capture position, shape, and orientation probabilistically rather than forcing rigid bounding boxes or category specific models like SMPL-X for human bodies. They bolt this onto frozen Wan2.1 with a lightweight adapter, so they get a strong video prior. They also built a pipeline to auto extract 4D annotations from real world videos to train this puppy. It doesn't look sexy yet, but IMO this is the interface video world models need - actual 3D authoring tools to exert control rather than crude scribbles and prompt incantations.

This is some quietly impressive work on making video world models actually controllable in 4D space. VerseCrafter lets you take an input image, use something like Blender to animate the 3D camera path and object trajectories, then uses that to condition generation. Scribbling in 2D feels so crude in comparison. The authors represent everything in a shared 4D world state - static background as a point cloud, moving objects as 3D gaussian trajectories. The gaussians are an interesting choice because they capture position, shape, and orientation probabilistically rather than forcing rigid bounding boxes or category specific models like SMPL-X for human bodies. They bolt this onto frozen Wan2.1 with a lightweight adapter, so they get a strong video prior. They also built a pipeline to auto extract 4D annotations from real world videos to train this puppy. It doesn't look sexy yet, but IMO this is the interface video world models need - actual 3D authoring tools to exert control rather than crude scribbles and prompt incantations.

Bilawal Sidhu

26,017 views • 7 months ago

Pi0 vs. ACT with BBox conditioning 🟦 Not many know you can push ACT to *almost-Pi0* generalisation by conditioning on bounding boxes (BBoxes). How is the training data collected? • Generate BBoxes for all pick-and-place objects in the scene.(I used Gemini) • Pick-and-place targets are selected randomly. • Add the BBox coordinates to the robot’s state. • Overlay the BBoxes in the visualisation so you know what to grab and where to drop. During inference: • Generate BBoxes for every object again. • Click the object you want to pick and its target spot; those BBoxes get added to the robot state. • Let the robot do the work for you 😃 Setup: - Trained ACT for 100k steps and fine-tuned Pi0 for only 20k. - Training data is 60 episodes and had *only* LEGO bricks. - Using single front camera (Laptop in this case) Got the idea from xun in LeRobot discord. Here’s ACT vs Pi0 on a toy car that isn’t in the dataset. 1/3

Pi0 vs. ACT with BBox conditioning 🟦 Not many know you can push ACT to almost-Pi0 generalisation by conditioning on bounding boxes (BBoxes). How is the training data collected? • Generate BBoxes for all pick-and-place objects in the scene.(I used Gemini) • Pick-and-place targets are selected randomly. • Add the BBox coordinates to the robot’s state. • Overlay the BBoxes in the visualisation so you know what to grab and where to drop. During inference: • Generate BBoxes for every object again. • Click the object you want to pick and its target spot; those BBoxes get added to the robot state. • Let the robot do the work for you 😃 Setup: - Trained ACT for 100k steps and fine-tuned Pi0 for only 20k. - Training data is 60 episodes and had only LEGO bricks. - Using single front camera (Laptop in this case) Got the idea from xun in LeRobot discord. Here’s ACT vs Pi0 on a toy car that isn’t in the dataset. 1/3

Shreyas Gite

34,863 views • 1 year ago

🚨 MASSIVE ASTEROID ALERT 🚨 😱 5 ASTEROIDS TO STRIKE EARTH ON JANUARY 4! ⚠️On January 4, something unusual appeared on NASA’s tracking systems. Not one. Not two. But five separate space objects entered Earth’s monitored region of space — all on the same day. 🛰️ WHAT WAS DETECTED NASA’s Near-Earth Object monitoring network identified five asteroids moving at extreme speeds, each following its own calculated path around our planet. • Classified as Near-Earth Asteroids (NEAs) • Traveling at tens of thousands of km/h • Detected days in advance • Tracked continuously as they approached and passed. Their distances varied, but all were close enough to activate automatic observation protocols. 🔭 WHY THIS STOOD OUT Asteroids pass Earth often — but multiple objects on the same date always draw attention. Every trajectory was recalculated. Every data point rechecked. Ground-based telescopes and automated systems stayed locked in. No public countdown. No dramatic warning. Just silent monitoring. 🌑 WHAT THE SYSTEMS SAW • Stable orbits • No sudden course changes • No fragmentation • No interaction with Earth’s atmosphere. One by one, the objects passed Earth’s vicinity and moved back into deep space. 🌍 THE RESULT No impact. No damage. No visible sign in the sky. To most people, January 4 felt completely normal. But above our heads, space traffic moved quietly — and was watched closely. 🛰️ THE BIGGER PICTURE Earth travels through a solar system filled with ancient debris left over from planet formation. Most objects remain distant. Some pass close. A few demand attention. This was one of those moments. The universe didn’t slow down. Earth didn’t notice. NASA kept watching. 🌌 Five asteroids came and went. The planet remained untouched. And space moved on. #Asteroids #NASA #SpaceUpdate #NearEarthObjects #CosmicWatch #Astronomy #Universe

🚨 MASSIVE ASTEROID ALERT 🚨 😱 5 ASTEROIDS TO STRIKE EARTH ON JANUARY 4! ⚠️On January 4, something unusual appeared on NASA’s tracking systems. Not one. Not two. But five separate space objects entered Earth’s monitored region of space — all on the same day. 🛰️ WHAT WAS DETECTED NASA’s Near-Earth Object monitoring network identified five asteroids moving at extreme speeds, each following its own calculated path around our planet. • Classified as Near-Earth Asteroids (NEAs) • Traveling at tens of thousands of km/h • Detected days in advance • Tracked continuously as they approached and passed. Their distances varied, but all were close enough to activate automatic observation protocols. 🔭 WHY THIS STOOD OUT Asteroids pass Earth often — but multiple objects on the same date always draw attention. Every trajectory was recalculated. Every data point rechecked. Ground-based telescopes and automated systems stayed locked in. No public countdown. No dramatic warning. Just silent monitoring. 🌑 WHAT THE SYSTEMS SAW • Stable orbits • No sudden course changes • No fragmentation • No interaction with Earth’s atmosphere. One by one, the objects passed Earth’s vicinity and moved back into deep space. 🌍 THE RESULT No impact. No damage. No visible sign in the sky. To most people, January 4 felt completely normal. But above our heads, space traffic moved quietly — and was watched closely. 🛰️ THE BIGGER PICTURE Earth travels through a solar system filled with ancient debris left over from planet formation. Most objects remain distant. Some pass close. A few demand attention. This was one of those moments. The universe didn’t slow down. Earth didn’t notice. NASA kept watching. 🌌 Five asteroids came and went. The planet remained untouched. And space moved on. #Asteroids #NASA #SpaceUpdate #NearEarthObjects #CosmicWatch #Astronomy #Universe

UNIVERSE Now

23,575 views • 8 months ago

In case you were not aware, most UFOs are "nuclear powered" by compact-fusion reactions. I know the physics, they showed us in crops, but have done only six months of preliminary work using argon gas. Basically we need 40 to 50 kV of DC power, and a big AC electromagnet. Deuterium gas gets ionized, then rushes back and forward like clothes in a washing machine. This creates many head-on collisions with less heat, than in "standard" fusion reactors. What Avi Loeb said concerning 3I/2025 is speculative but also reasonable. I am hoping to see better images of 3I/2025 soon from the James Webb telescope.

In case you were not aware, most UFOs are "nuclear powered" by compact-fusion reactions. I know the physics, they showed us in crops, but have done only six months of preliminary work using argon gas. Basically we need 40 to 50 kV of DC power, and a big AC electromagnet. Deuterium gas gets ionized, then rushes back and forward like clothes in a washing machine. This creates many head-on collisions with less heat, than in "standard" fusion reactors. What Avi Loeb said concerning 3I/2025 is speculative but also reasonable. I am hoping to see better images of 3I/2025 soon from the James Webb telescope.

Red Collie (Dr. Horace Drew) scientist/inventor

286,362 views • 1 year ago

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

19,931 views • 2 months ago

🧪 My GEN-3 Prompting Process I get a lot of questions on how I find my prompts when using Image-2-Video in Runway. Here is a quick breakdown of the general thought process. If you have any further questions, let's chat in the comments below. 1️⃣ I always start by doing 3x generations without any prompts and additional settings. 2️⃣ I analyze those 3x generations and identify patterns. What did the model always do well, where did it fail. 3️⃣ I then use prompting and the different controllability features to eliminate where the model struggled on its own. --- General Tips --- *️⃣ There are some tokens which work universally. "Muted colors, low contrast" are great to preserve the colors of the original input image. "Static camera, natural movement" works fantastically to get cinematic shots. *️⃣ My I2V prompts are on the shorter side. It's usually a sentence describing the scene and then individual modifiers like the ones mentioned above to fix certain camera/lighting/movement artifacts. *️⃣ Start small and prompt engineer in steps. This is very much an iterative process which rewards you for understanding model behavior and knowing how to craft a visual architecture with words. --- Disclaimer --- Please note that this approach is more suitable for a professional workflow. Therefore, I recommend it for users on the unlimited plan who don't need to worry about credits.

🧪 My GEN-3 Prompting Process I get a lot of questions on how I find my prompts when using Image-2-Video in Runway. Here is a quick breakdown of the general thought process. If you have any further questions, let's chat in the comments below. 1️⃣ I always start by doing 3x generations without any prompts and additional settings. 2️⃣ I analyze those 3x generations and identify patterns. What did the model always do well, where did it fail. 3️⃣ I then use prompting and the different controllability features to eliminate where the model struggled on its own. --- General Tips --- ️⃣ There are some tokens which work universally. "Muted colors, low contrast" are great to preserve the colors of the original input image. "Static camera, natural movement" works fantastically to get cinematic shots. ️⃣ My I2V prompts are on the shorter side. It's usually a sentence describing the scene and then individual modifiers like the ones mentioned above to fix certain camera/lighting/movement artifacts. *️⃣ Start small and prompt engineer in steps. This is very much an iterative process which rewards you for understanding model behavior and knowing how to craft a visual architecture with words. --- Disclaimer --- Please note that this approach is more suitable for a professional workflow. Therefore, I recommend it for users on the unlimited plan who don't need to worry about credits.

Nicolas Neubert

46,240 views • 2 years ago

Nanobanana Pro Higgsfield AI 🧩 なるほどこれは便利！！いろんなショットを一発で出して、そこから選ぶ感じ見事にアップスケールしてくれるプロンプトはリプ欄の元投稿を使わせていただきました ””” Analyze the entire composition of the input image. Identify ALL key subjects present (whether it's a single person, a group/couple, a vehicle, or a specific object) and their spatial relationship/interaction. Generate a cohesive 3x3 grid "Cinematic Contact Sheet" featuring 9 distinct camera shots of exactly these subjects in the same environment. You must adapt the standard cinematic shot types to fit the content (e.g., if a group, keep the group together; if an object, frame the whole object): Row 1 (Establishing Context): 1. Extreme Long Shot (ELS): The subject(s) are seen small within the vast environment. 2. Long Shot (LS): The complete subject(s) or group is visible from top to bottom (head to toe / wheels to roof). 3. Medium Long Shot (American/3-4): Framed from knees up (for people) or a 3/4 view (for objects). Row 2 (The Core Coverage): 4. Medium Shot (MS): Framed from the waist up (or the central core of the object). Focus on interaction/action. 5. Medium Close-Up (MCU): Framed from chest up. Intimate framing of the main subject(s). 6. Close-Up (CU): Tight framing on the face(s) or the "front" of the object. Row 3 (Details & Angles): 7. Extreme Close-Up (ECU): Macro detail focusing intensely on a key feature (eyes, hands, logo, texture). 8. Low Angle Shot (Worm's Eye): Looking up at the subject(s) from the ground (imposing/heroic). 9. High Angle Shot (Bird's Eye): Looking down on the subject(s) from above. Ensure strict consistency: The same people/objects, same clothes, and same lighting across all 9 panels. The depth of field should shift realistically (bokeh in close-ups). A professional 3x3 cinematic storyboard grid containing 9 panels. The grid showcases the specific subjects/scene from the input image in a comprehensive range of focal lengths. Top Row: Wide environmental shot, Full view, 3/4 cut. Middle Row: Waist-up view, Chest-up view, Face/Front close-up. Bottom Row: Macro detail, Low Angle, High Angle. All frames feature photorealistic textures, consistent cinematic color grading, and correct framing for the specific number of subjects or objects analyzed. """

yachimat - AI Short Anime

115,713 views • 9 months ago

🚨 CHINESE SCIENTISTS JUST INVENTED 3D PRINTING THAT CREATES OBJECTS IN 0.6 SECONDS USING ONLY LIGHT. Researchers at Tsinghua University have developed a new method called DISH (Digital Incoherent Synthesis of Holographic light fields) that can print complex millimeter-scale objects almost instantly. Instead of slowly building layer by layer, the system fires thousands of precisely patterned light images from multiple angles into a still vat of liquid resin. Where the light overlaps, the resin instantly hardens into a solid 3D object. The entire process takes just 0.6 seconds. Why this matters: • It’s currently the fastest volumetric 3D printing method ever demonstrated • Achieves extremely fine detail features thinner than a human hair • The resin stays completely still, so there’s no vibration or distortion • It can work with watery (low-viscosity) resins, making it suitable for biological applications • The team has already printed complex structures like blood vessel-like tubes and even a tiny bust of a historical figure The deeper implication: Traditional 3D printing has always been limited by speed and the need to move either the print head or the resin. This approach removes both constraints by using light itself as the sculptor. Because it can print directly into still liquid (and potentially onto living tissue), it opens new possibilities in bioprinting, medical devices, and rapid manufacturing. If the technology can be scaled beyond millimeter sizes, it could fundamentally change how we think about making physical objects turning “print” from a slow process into something closer to instantaneous fabrication. We’re moving from “layer by layer” to “all at once.” How do you think instant volumetric 3D printing like this could change medicine, manufacturing, or everyday life if it becomes widely available? Follow for more frontier manufacturing and materials science breakthroughs.

🚨 CHINESE SCIENTISTS JUST INVENTED 3D PRINTING THAT CREATES OBJECTS IN 0.6 SECONDS USING ONLY LIGHT. Researchers at Tsinghua University have developed a new method called DISH (Digital Incoherent Synthesis of Holographic light fields) that can print complex millimeter-scale objects almost instantly. Instead of slowly building layer by layer, the system fires thousands of precisely patterned light images from multiple angles into a still vat of liquid resin. Where the light overlaps, the resin instantly hardens into a solid 3D object. The entire process takes just 0.6 seconds. Why this matters: • It’s currently the fastest volumetric 3D printing method ever demonstrated • Achieves extremely fine detail features thinner than a human hair • The resin stays completely still, so there’s no vibration or distortion • It can work with watery (low-viscosity) resins, making it suitable for biological applications • The team has already printed complex structures like blood vessel-like tubes and even a tiny bust of a historical figure The deeper implication: Traditional 3D printing has always been limited by speed and the need to move either the print head or the resin. This approach removes both constraints by using light itself as the sculptor. Because it can print directly into still liquid (and potentially onto living tissue), it opens new possibilities in bioprinting, medical devices, and rapid manufacturing. If the technology can be scaled beyond millimeter sizes, it could fundamentally change how we think about making physical objects turning “print” from a slow process into something closer to instantaneous fabrication. We’re moving from “layer by layer” to “all at once.” How do you think instant volumetric 3D printing like this could change medicine, manufacturing, or everyday life if it becomes widely available? Follow for more frontier manufacturing and materials science breakthroughs.

TheNewPhysics

347,458 views • 2 months ago

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️

Bilawal Sidhu

68,515 views • 1 year ago

We recently introduced Gemini Omni Flash, our first model in the new Omni family. With Omni, you can easily create and edit high-quality videos from text, image, video or audio references. We recently gave developers access to it, and since then, we’ve seen builders all over the world use Omni to create a range of personal and professional projects. Here are some of our favorite ways we’ve seen builders use Omni so far ↓ 📽️ Switch angles and perspectives You can change camera angles, switch environments, and apply cinematic zooms — all without losing the thread of your original scene. Builder Leon Lin took full advantage of this capability, capturing a woman standing in the middle of a city from about 20 different perspectives. You see her from different angles: up close and far away, head on and in profile, from above, and from below. Some shots zoom in, while others hold still. And the background shifts, too.

Google

488,369 views • 26 days ago

Project Hail Mary opened last week. Great film. But nobody is talking about the credits. They should be. A guy with a telescope spent hundreds of hours collecting light from objects so distant that the photons hitting his sensor left their source before Rome was founded. His name is Rod Prazeres. His images ended up on 70-foot IMAX screens worldwide. Look at what he captured. The Rosette Nebula is a cloud of gas 5,000 light-years away that has arranged itself into the shape of a human eye, ringed by fire. The Vela filaments are a stellar explosion still spreading outward through space – blue threads so fine they look like frost on glass. The dust pillar in the Pelican Nebula is manufacturing new suns right now. While you read this. None of it was rendered. All of it is real. Weir spent years getting the science right. The filmmakers felt the same way about the sky. When they needed something beautiful enough to close the film, they went looking for something that actually exists. They found it. 5,000 light-years out. Gandalv / Gandalv

Project Hail Mary opened last week. Great film. But nobody is talking about the credits. They should be. A guy with a telescope spent hundreds of hours collecting light from objects so distant that the photons hitting his sensor left their source before Rome was founded. His name is Rod Prazeres. His images ended up on 70-foot IMAX screens worldwide. Look at what he captured. The Rosette Nebula is a cloud of gas 5,000 light-years away that has arranged itself into the shape of a human eye, ringed by fire. The Vela filaments are a stellar explosion still spreading outward through space – blue threads so fine they look like frost on glass. The dust pillar in the Pelican Nebula is manufacturing new suns right now. While you read this. None of it was rendered. All of it is real. Weir spent years getting the science right. The filmmakers felt the same way about the sky. When they needed something beautiful enough to close the film, they went looking for something that actually exists. They found it. 5,000 light-years out. Gandalv / Gandalv

Gandalv

1,159,541 views • 5 months ago

Dear Tarun Chitra 1. We are the original creators of DeSci back in 2016. What DeSci has become today is largely unrelated with its original model of producing rigorous peer-reviewed scientific studies published in reputable medical journals. The model we introduced. We are tirelessly fighting against pseudoscience, and we are showing the world that people can understand the difference between legit science and pseudoscience with the success of $INNBCV. Yes, meritocracy is possible in crypto. Even against all odds. 2. We are the only project in the entire crypto space that ever funded, performed, and published highly innovative HIV cure research ( We are the project that produced the first peer-reviewed study on blockchain-based biomedical data storage in the world’s most reputable scientific network, Springer Nature ( $INNBCV is not for the privileged few; it is for the many. We resisted all the pressure from those who wanted us to provide big allocations to VIPs of other DAOs “because it is good for the marketing” and put our users first, ensuring a fair launch, a launch for the people, and they turned $70k into $2,000,000. $INNBCV shows that you can have a sustainable model, provided you are backed by actual science. And thanks to the amazing guys at daos.fun baoskee and Solana community. Behind our project there is the sweat and blood of years of work to produce publications in the most reputable medical journals. Just to put things into perspective, it took us 3 years to publish our latest work in Springer Nature. 3. Unlike many other projects, we had no ICO/VCs, meaning we had to prove ourselves every single day because we are only supported by our community. If we deliver products, we survive; it is either publish or perish for us, and that’s why we have such a close connection to our community. $INNBCV is a struggler, $INNBCV is a survivor, $INNBCV is not for the privilege of the few but for the people. Our community makes it possible by supporting us. You guys are the real heroes.

Dear Tarun Chitra 1. We are the original creators of DeSci back in 2016. What DeSci has become today is largely unrelated with its original model of producing rigorous peer-reviewed scientific studies published in reputable medical journals. The model we introduced. We are tirelessly fighting against pseudoscience, and we are showing the world that people can understand the difference between legit science and pseudoscience with the success of $INNBCV. Yes, meritocracy is possible in crypto. Even against all odds. 2. We are the only project in the entire crypto space that ever funded, performed, and published highly innovative HIV cure research ( We are the project that produced the first peer-reviewed study on blockchain-based biomedical data storage in the world’s most reputable scientific network, Springer Nature ( $INNBCV is not for the privileged few; it is for the many. We resisted all the pressure from those who wanted us to provide big allocations to VIPs of other DAOs “because it is good for the marketing” and put our users first, ensuring a fair launch, a launch for the people, and they turned $70k into $2,000,000. $INNBCV shows that you can have a sustainable model, provided you are backed by actual science. And thanks to the amazing guys at daos.fun baoskee and Solana community. Behind our project there is the sweat and blood of years of work to produce publications in the most reputable medical journals. Just to put things into perspective, it took us 3 years to publish our latest work in Springer Nature. 3. Unlike many other projects, we had no ICO/VCs, meaning we had to prove ourselves every single day because we are only supported by our community. If we deliver products, we survive; it is either publish or perish for us, and that’s why we have such a close connection to our community. $INNBCV is a struggler, $INNBCV is a survivor, $INNBCV is not for the privilege of the few but for the people. Our community makes it possible by supporting us. You guys are the real heroes.

InnovativeBioresearch🇮🇹

10,867 views • 1 year ago

Tried this viral prompt idea on BudgetPixel AI using GPT Image 2 + Seedance 2.0 Prompt remove the arrows immediately while starting the video. The camera generates footage in a first-person, ultra-high-speed perspective, faithfully following the exact path of the red line marked on the reference image. Cinematic presentation. Low-angle ground-level shot racing across the lush green meadow filled with wildflowers and tall grass, following the exact curving path shown in the reference image. Pass smoothly right beside the large fluffy long-haired cat sitting alert on the left foreground, its fur detailed and catching warm sunlight. A second cat lies playfully on its back in the grass nearby. Scattered throughout the vibrant field are numerous hens and chickens grazing and moving naturally in the same positions as the reference photo. A rustic wooden farmhouse sits nestled in the midground among the greenery. The camera then continues the smooth ascent following the curving path upward along the gentle stream area, soaring through the dense trees toward the towering rocky mountain peaks. It dramatically circles the central mountain before pulling back into a breathtaking bird’s-eye panoramic view of the entire valley, showcasing the full scale of the river, forests, fields, farmhouse, and animals below. One continuous fluid cinematic shot with no cuts. Photorealistic, ultra-detailed textures on the cats’ fur, chicken feathers, vegetation, water reflections, and rocks. Warm golden hour lighting with soft volumetric god rays, rich atmospheric depth, vibrant natural colors, National Geographic level realism, masterpiece --ar 16:9 --stylize 25 --v 6

Tried this viral prompt idea on BudgetPixel AI using GPT Image 2 + Seedance 2.0 Prompt remove the arrows immediately while starting the video. The camera generates footage in a first-person, ultra-high-speed perspective, faithfully following the exact path of the red line marked on the reference image. Cinematic presentation. Low-angle ground-level shot racing across the lush green meadow filled with wildflowers and tall grass, following the exact curving path shown in the reference image. Pass smoothly right beside the large fluffy long-haired cat sitting alert on the left foreground, its fur detailed and catching warm sunlight. A second cat lies playfully on its back in the grass nearby. Scattered throughout the vibrant field are numerous hens and chickens grazing and moving naturally in the same positions as the reference photo. A rustic wooden farmhouse sits nestled in the midground among the greenery. The camera then continues the smooth ascent following the curving path upward along the gentle stream area, soaring through the dense trees toward the towering rocky mountain peaks. It dramatically circles the central mountain before pulling back into a breathtaking bird’s-eye panoramic view of the entire valley, showcasing the full scale of the river, forests, fields, farmhouse, and animals below. One continuous fluid cinematic shot with no cuts. Photorealistic, ultra-detailed textures on the cats’ fur, chicken feathers, vegetation, water reflections, and rocks. Warm golden hour lighting with soft volumetric god rays, rich atmospheric depth, vibrant natural colors, National Geographic level realism, masterpiece --ar 16:9 --stylize 25 --v 6

Aaliya

14,611 views • 3 months ago

Kitten update #8: The kitties are back home! Everyone is getting very strong and active now. I forgot to take their weights this morning so I'll have to do it before the next feeding, but I'm confident everyone is continuing to gain. We started adding in some wet food today. I mixed up a slurry with some of the formula and stuck it in a syringe with a nipple on it. Nobody took to it right away except gray tuxedo, who loved it, but everyone else got some squirted in while they were chewing on the milk nipple and then they loved it. Well, except for dark tortie, which is a little ironic since she is by far the biggest little monster of the litter, but I guess she's done so well on the formula she's not interested in making a change yet. Butts are all looking SO much better now that I stopped using the wet wipes. However, we have now started to have some concerns about little to no pooping. Dark tortie hasn't pooped for 24 hours so I eliminated the yogurt from the formula and am crossing my fingers for some action before the end of the day or back to the vet she goes. I don't know how long they'll let me play with their tummies like this but it sure is fun for now!

Kitten update #8: The kitties are back home! Everyone is getting very strong and active now. I forgot to take their weights this morning so I'll have to do it before the next feeding, but I'm confident everyone is continuing to gain. We started adding in some wet food today. I mixed up a slurry with some of the formula and stuck it in a syringe with a nipple on it. Nobody took to it right away except gray tuxedo, who loved it, but everyone else got some squirted in while they were chewing on the milk nipple and then they loved it. Well, except for dark tortie, which is a little ironic since she is by far the biggest little monster of the litter, but I guess she's done so well on the formula she's not interested in making a change yet. Butts are all looking SO much better now that I stopped using the wet wipes. However, we have now started to have some concerns about little to no pooping. Dark tortie hasn't pooped for 24 hours so I eliminated the yogurt from the formula and am crossing my fingers for some action before the end of the day or back to the vet she goes. I don't know how long they'll let me play with their tummies like this but it sure is fun for now!

Andrea Burkhart 🐟🐟🐟🐟🏴‍☠️

27,195 views • 1 year ago

Alistair Campbell. You looked in the mirror this morning and saw a moral authority. We looked at your hands and saw the blood of 179 British servicemen. You tweeted yesterday. Mocking Farage. Mocking Lowe. Mocking Musk. Mocking Robinson. Demanding they comment on police deaths and domestic violence before they comment on migration. You set yourself the judge of who may speak and who must stay silent. You are not a judge. You are an ex-communication in a suit, put out to pasture. You thought the deletion worked. The BBC drama suppressed for twenty years. The dossier buried. The Kelly affair whitewashed. The Portland contracts with Qatar and Kazakhstan hidden behind NDAs. You thought we got tired. You thought the six inquiries with zero prosecutions meant the file was closed. You are running now. We can smell it. We do not close files. We keep them open. We keep them sharp. You sit in your studio with Rory Stewart, collecting Chernin Group dollars and Tony Blair's Oracle money, attacking Farage and Musk and Robinson like a protection racket enforcer. "Nasty Nigel." The weekly column. Kevin Maguire at the Mirror still taking your calls, still writing your lines, still pretending he is a journalist when we have the Smeargate receipts. Did you think we filed those away, Alistair? Did you think the hands were clean, Alistair? They think the curtains are drawn. They think we cannot see them shaking. Poor things. We see the whole playground. You lecture Musk about megalomania from a studio funded by Oracle blood money. You sneer at Farage and Lowe from a sofa paid for by Qatar's missing millions. You mock Robinson while your own son's fraud investigation sits in a Metropolitan Police file. The glass house is cracking, Alistair. The stones are coming back. Here is what you need to understand, Alistair. We are the ones you do not know. The silent ones. The file-keepers. The ones who remember Dr Kelly while you remember your podcast download numbers. The ones who track the Portland contracts while you track your monthly £100,000. The ones who know about the son's fraud investigation while you know about the next GMB booking. You are not the hunter anymore. You are the hunted. Burnham will not save you. The podcast will not save you. The mental health shield will not save you. Your day in court is not a threat. It is a schedule. The laws that exempt you were written by your friends. Those friends are leaving. The new government will not have your back. They will have your file. Sleep tight, Alistair. The night is long. The file is thick. And we do not get tired. We get closer.

Alistair Campbell. You looked in the mirror this morning and saw a moral authority. We looked at your hands and saw the blood of 179 British servicemen. You tweeted yesterday. Mocking Farage. Mocking Lowe. Mocking Musk. Mocking Robinson. Demanding they comment on police deaths and domestic violence before they comment on migration. You set yourself the judge of who may speak and who must stay silent. You are not a judge. You are an ex-communication in a suit, put out to pasture. You thought the deletion worked. The BBC drama suppressed for twenty years. The dossier buried. The Kelly affair whitewashed. The Portland contracts with Qatar and Kazakhstan hidden behind NDAs. You thought we got tired. You thought the six inquiries with zero prosecutions meant the file was closed. You are running now. We can smell it. We do not close files. We keep them open. We keep them sharp. You sit in your studio with Rory Stewart, collecting Chernin Group dollars and Tony Blair's Oracle money, attacking Farage and Musk and Robinson like a protection racket enforcer. "Nasty Nigel." The weekly column. Kevin Maguire at the Mirror still taking your calls, still writing your lines, still pretending he is a journalist when we have the Smeargate receipts. Did you think we filed those away, Alistair? Did you think the hands were clean, Alistair? They think the curtains are drawn. They think we cannot see them shaking. Poor things. We see the whole playground. You lecture Musk about megalomania from a studio funded by Oracle blood money. You sneer at Farage and Lowe from a sofa paid for by Qatar's missing millions. You mock Robinson while your own son's fraud investigation sits in a Metropolitan Police file. The glass house is cracking, Alistair. The stones are coming back. Here is what you need to understand, Alistair. We are the ones you do not know. The silent ones. The file-keepers. The ones who remember Dr Kelly while you remember your podcast download numbers. The ones who track the Portland contracts while you track your monthly £100,000. The ones who know about the son's fraud investigation while you know about the next GMB booking. You are not the hunter anymore. You are the hunted. Burnham will not save you. The podcast will not save you. The mental health shield will not save you. Your day in court is not a threat. It is a schedule. The laws that exempt you were written by your friends. Those friends are leaving. The new government will not have your back. They will have your file. Sleep tight, Alistair. The night is long. The file is thick. And we do not get tired. We get closer.

Christian

198,772 views • 9 days ago

THE DUCK SPINNING IN YOUR SCREEN ISN'T CHANGING DIRECTION. YOUR BRAIN IS CHANGING THE WAY IT SEES IT. Here's why it happens: What you're experiencing is called bistable perception, and it exposes something unsettling about how you actually see the world. Vision doesn't work the way we assume. Photons hit your retina, sure, but the raw signal reaching your visual cortex is fragmentary, ambiguous, and full of gaps. Your brain fills those gaps by placing a bet like a best guess about what's most likely out there. Perception behaves more like a prediction engine than a camera. When an image gives your brain two equally valid interpretations, the prediction engine can't settle. It oscillates between them. Neuroscientists have traced this exact flip. When the duck appears to change direction, activity in your right inferior frontal gyrus and parts of your parietal cortex spikes. Those are the regions that have nothing to do with detecting motion. They handle attention, disambiguation, and internal decision-making. The switch isn't happening in your eyes. A small vote is being cast deep inside your prefrontal machinery. Underneath sits a mechanism called neural adaptation. Whichever direction you're currently perceiving, the neurons committing to that interpretation gradually fatigue. Their firing weakens. The competing interpretation, previously suppressed, gains ground. Eventually the suppressed signal breaks through and overtakes your conscious experience. The duck "changes direction" the moment one neural coalition loses to another. This is more than an internet illusion. People with autism tend to switch more slowly on bistable images. People with schizophrenia switch faster. Your perceptual stability is essentially a fingerprint of your neurology. Every stable object you see today like your desk, your hands, the road, etc are the outcome of billions of these silent votes happening beneath awareness. The duck was never spinning both ways. You were.

THE DUCK SPINNING IN YOUR SCREEN ISN'T CHANGING DIRECTION. YOUR BRAIN IS CHANGING THE WAY IT SEES IT. Here's why it happens: What you're experiencing is called bistable perception, and it exposes something unsettling about how you actually see the world. Vision doesn't work the way we assume. Photons hit your retina, sure, but the raw signal reaching your visual cortex is fragmentary, ambiguous, and full of gaps. Your brain fills those gaps by placing a bet like a best guess about what's most likely out there. Perception behaves more like a prediction engine than a camera. When an image gives your brain two equally valid interpretations, the prediction engine can't settle. It oscillates between them. Neuroscientists have traced this exact flip. When the duck appears to change direction, activity in your right inferior frontal gyrus and parts of your parietal cortex spikes. Those are the regions that have nothing to do with detecting motion. They handle attention, disambiguation, and internal decision-making. The switch isn't happening in your eyes. A small vote is being cast deep inside your prefrontal machinery. Underneath sits a mechanism called neural adaptation. Whichever direction you're currently perceiving, the neurons committing to that interpretation gradually fatigue. Their firing weakens. The competing interpretation, previously suppressed, gains ground. Eventually the suppressed signal breaks through and overtakes your conscious experience. The duck "changes direction" the moment one neural coalition loses to another. This is more than an internet illusion. People with autism tend to switch more slowly on bistable images. People with schizophrenia switch faster. Your perceptual stability is essentially a fingerprint of your neurology. Every stable object you see today like your desk, your hands, the road, etc are the outcome of billions of these silent votes happening beneath awareness. The duck was never spinning both ways. You were.

The Curious Tales

279,309 views • 3 days ago

🚨 ANOTHER BRAND NEW VIDEO DIRECT FROM IRAN: The call to be armed has evolved. This is no longer about small-scale tactics or asking for pistols to target low-level regime forces; it is the deployment of a next-level strategy. A highly sophisticated, organized force inside Iran is preparing for a decisive action to finish the matter. These individuals are sending videos directly to me to break through the silence and project a completely new reality on the ground. This latest footage reveals a group operating east of Tehran near Pardis and Jajrud, confidently displaying their exact GPS location before setting up a fleet of FPV and surveillance drones. Their message is absolute: the era of disorganized vigilantes playing around with guns is over. They are operating with modern tools and calculated precision. On the audio track, a mechanically disguised voice delivers a clear warning directly to the state: "This time you are in the center of our target. We are not waiting for the help of others, and we are waiting for you where you do not expect it." The message ends with the declaration "Long live Iran, Javid Shah" (Long Live the King). While they are maintaining strict operational security by keeping their exact targets and timelines secret, they promise the results will be seen soon. For the millions they represent, peaceful protest against live ammunition has proven impossible. They aren't asking for foreign boots on the ground. They are demonstrating that if Crown Prince Reza Pahlavi has a force at his command, that force is intelligent, advanced, and ready. And let’s be clear: the only viable path to effectively arming and coordinating this movement is through direct alignment with the Prince and his team. They risked their lives to deliver this proof straight from the ground. More people need to amplify their call. Do not let the regime bury their bravery.

Armin Navabi

37,271 views • 2 months ago