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MLLMs are great at understanding videos, but struggle with spatial reasoning—like estimating distances or tracking objects across time. the bottleneck? getting precise 3D spatial annotations on real videos is expensive and error-prone. introducing SIMS-V 🤖 [1/n]

Ellis Brown

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16,480 просмотров • 10 месяцев назад •via X (Twitter)

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Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or AI-driven spatial intelligence. Now, imagine a map so detailed it captures the world at sub-5cm accuracy. That’s OVRMaps. 🌐 OVER 3D maps are built with 400-1,000 images per 300 sqm, taken from multiple angles at pedestrian level. The result? A new era of hyper-precise localization that changes everything. Why does this matter? ↳ VPS that actually works – Real-world AR anchoring, digital twins, and AI-powered spatial computing with pinpoint accuracy. ↳ Next-gen precision – Essential for ride-sharing, robotics, smart cities, and asset tracking—where even a small error makes a huge difference. ↳ AI-Driven Spatial Intelligence – Our maps fuel Large Geo-Spatial Models (LGMs), the AI revolution enabling machines to understand, navigate, and interact with the real world. ↳ The Spatial Computing revolution – Merging immersive experiences with real-world precision, unlocking applications beyond what Google’s dataset can support. OVRMaps are built for the future. 🌍 Start mapping today:

Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or AI-driven spatial intelligence. Now, imagine a map so detailed it captures the world at sub-5cm accuracy. That’s OVRMaps. 🌐 OVER 3D maps are built with 400-1,000 images per 300 sqm, taken from multiple angles at pedestrian level. The result? A new era of hyper-precise localization that changes everything. Why does this matter? ↳ VPS that actually works – Real-world AR anchoring, digital twins, and AI-powered spatial computing with pinpoint accuracy. ↳ Next-gen precision – Essential for ride-sharing, robotics, smart cities, and asset tracking—where even a small error makes a huge difference. ↳ AI-Driven Spatial Intelligence – Our maps fuel Large Geo-Spatial Models (LGMs), the AI revolution enabling machines to understand, navigate, and interact with the real world. ↳ The Spatial Computing revolution – Merging immersive experiences with real-world precision, unlocking applications beyond what Google’s dataset can support. OVRMaps are built for the future. 🌍 Start mapping today:

Over the Reality 🌐

1,331,567 просмотров • 1 год назад

I can't believe that Nvidia looked at this "AI on top of games filter" and said to themselves this is the future of gaming. Like it or not, this is where Nvidia is heading and they're calling it neural rendering with DLSS 5. The examples they've showed reminded me a lot of those AI generated filter videos on top of GTA V, except that now it is supposed to run in real-time. Honestly, I don't like this current look at all

I can't believe that Nvidia looked at this "AI on top of games filter" and said to themselves this is the future of gaming. Like it or not, this is where Nvidia is heading and they're calling it neural rendering with DLSS 5. The examples they've showed reminded me a lot of those AI generated filter videos on top of GTA V, except that now it is supposed to run in real-time. Honestly, I don't like this current look at all

NikTek

3,335,022 просмотров • 6 месяцев назад

Selling candles on Etsy, Amazon, Shopify, or eBay? Then you know the struggle — product videos are expensive and a huge time sink. I tried Pollo AI's "Photo to Video Ads" feature right inside the Mobile APP with just one candle photo. A few minutes later, that single image turned into a cinematic, cozy video ad that looked ready to go live. No studio. No crew. No editing skills. All from my phone. At this point, Pollo Agent honestly feels like my creative team. AI-generated product ads are getting scary good.

Selling candles on Etsy, Amazon, Shopify, or eBay? Then you know the struggle — product videos are expensive and a huge time sink. I tried Pollo AI's "Photo to Video Ads" feature right inside the Mobile APP with just one candle photo. A few minutes later, that single image turned into a cinematic, cozy video ad that looked ready to go live. No studio. No crew. No editing skills. All from my phone. At this point, Pollo Agent honestly feels like my creative team. AI-generated product ads are getting scary good.

Aurelia Vance

33,828 просмотров • 3 месяцев назад

Today, we released Lyra 2.0, a framework for generating persistent, explorable 3D worlds at scale, from NVIDIA Research. Generating large-scale, complex environments is difficult for AI models. Current models often “forget” what spaces look like and lose track of movement over time, causing objects to shift, blur, or appear inconsistent. This prevents them from creating the reliable 3D environments required for downstream simulations. Lyra 2.0 solves these issues by: ✅ Maintaining per-frame 3D geometry to retrieve past frames and establish spatial correspondences ✅ Using self-augmented training to correct its own temporal drifting. Lyra 2.0 turns an image into a 3D world you can walk through, look back, and drop a robot into for real-time rendering, simulation, and immersive applications. ➡️ Learn more: 📄 Read the paper:

Today, we released Lyra 2.0, a framework for generating persistent, explorable 3D worlds at scale, from NVIDIA Research. Generating large-scale, complex environments is difficult for AI models. Current models often “forget” what spaces look like and lose track of movement over time, causing objects to shift, blur, or appear inconsistent. This prevents them from creating the reliable 3D environments required for downstream simulations. Lyra 2.0 solves these issues by: ✅ Maintaining per-frame 3D geometry to retrieve past frames and establish spatial correspondences ✅ Using self-augmented training to correct its own temporal drifting. Lyra 2.0 turns an image into a 3D world you can walk through, look back, and drop a robot into for real-time rendering, simulation, and immersive applications. ➡️ Learn more: 📄 Read the paper:

NVIDIA AI Developer

438,052 просмотров • 5 месяцев назад

Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

Ilir Aliu

53,992 просмотров • 5 месяцев назад

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 просмотров • 1 год назад

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 просмотров • 8 месяцев назад

Next up, something I haven't REALLY managed to get working for a long time is synchronized, uncalibrated videos for person tracking. Whenever I used something like vggt, there was just enough residual error that it didn't work and gave crap results. I have a version now in Rerun that takes time-synced, but uncalibrated sparse videos -> calibrated, gravity-aligned videos within a reasonable error. Yellow cameras are basically the naive vggt cameras (actually g3t in this case), and blue ones are after running the optimization post-processing pipeline. Kornia has been shipping some cool stuff recently; in particular, they have an easy-to-use bundle adjustment pipeline in rust with kornia-rs. I was trying to use pycolmap but I kept having issues with it so it was a really nice alternative. I'm going to be pumping out more huggingface Gradio spaces soon so this will be the next one 🫡 Fable has gotten really good at making images. its a TLDR on how it works

Next up, something I haven't REALLY managed to get working for a long time is synchronized, uncalibrated videos for person tracking. Whenever I used something like vggt, there was just enough residual error that it didn't work and gave crap results. I have a version now in Rerun that takes time-synced, but uncalibrated sparse videos -> calibrated, gravity-aligned videos within a reasonable error. Yellow cameras are basically the naive vggt cameras (actually g3t in this case), and blue ones are after running the optimization post-processing pipeline. Kornia has been shipping some cool stuff recently; in particular, they have an easy-to-use bundle adjustment pipeline in rust with kornia-rs. I was trying to use pycolmap but I kept having issues with it so it was a really nice alternative. I'm going to be pumping out more huggingface Gradio spaces soon so this will be the next one 🫡 Fable has gotten really good at making images. its a TLDR on how it works

Pablo Vela

16,480 просмотров • 14 дней назад

MOST PEOPLE DON’T KNOW THIS IS EVEN POSSIBLE. You can swap your face live on camera in real-time using just ONE image. OPEN SOURCE. FREE TO INSTALL. No expensive tools, studio setup or complex pipelines. Real-time face swap. Live webcam. VIDEO DEEPFAKE. Here is what is going on. Most creators struggle with content production. You need editing, VFX tools, time, and skills to create something engaging. The problem has always been this: AI face swap tools are slow, offline, or require heavy editing workflows. Upload → wait → render → export → repeat. Someone just killed that entire workflow. It is called Deep Live Cam. You connect a single image to your webcam, and it swaps your face live - instantly. No rendering. No waiting. No editing. And then it acts in real time. swaps faces live on webcam or video supports multiple faces at once keeps natural expressions with mouth masking works with CPU, GPU, or Mac lets you stream, record, or create content instantly You can literally: go live as anyone create viral memes in seconds run live shows with different identities replace actors in videos in real time prank, entertain, or build content at scale Professional studios spend thousands on VFX pipelines to do this. This does it in seconds. Someone built a real-time deepfake engine… and put it on the internet for free.

MOST PEOPLE DON’T KNOW THIS IS EVEN POSSIBLE. You can swap your face live on camera in real-time using just ONE image. OPEN SOURCE. FREE TO INSTALL. No expensive tools, studio setup or complex pipelines. Real-time face swap. Live webcam. VIDEO DEEPFAKE. Here is what is going on. Most creators struggle with content production. You need editing, VFX tools, time, and skills to create something engaging. The problem has always been this: AI face swap tools are slow, offline, or require heavy editing workflows. Upload → wait → render → export → repeat. Someone just killed that entire workflow. It is called Deep Live Cam. You connect a single image to your webcam, and it swaps your face live - instantly. No rendering. No waiting. No editing. And then it acts in real time. swaps faces live on webcam or video supports multiple faces at once keeps natural expressions with mouth masking works with CPU, GPU, or Mac lets you stream, record, or create content instantly You can literally: go live as anyone create viral memes in seconds run live shows with different identities replace actors in videos in real time prank, entertain, or build content at scale Professional studios spend thousands on VFX pipelines to do this. This does it in seconds. Someone built a real-time deepfake engine… and put it on the internet for free.

Kanika

17,835 просмотров • 5 месяцев назад

Create a 3D model from a single image, set of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”

Create a 3D model from a single image, set of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”

Bilawal Sidhu

92,898 просмотров • 2 лет назад

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.

MrNeRF

27,428 просмотров • 1 год назад

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Xuhui Zhou

143,100 просмотров • 3 месяцев назад

Streaming iPhone data in real-time directly to Rerun 🚀 The collection process is one of the most frustrating parts of building imitation-learning datasets. I’ve got a little army of sensors—📱 iPhone, iPad, Quest 3—but getting them temporally aligned, spatially aligned, AND seeing real-time feedback while recording is tough. I stumbled on a great library from Cake Lab (WPI) called ARFlow. It’s a thin client built on Unity’s ARFoundation that connects over gRPC to a server running Rerun for live data logging. I forked it to: - Log the SLAM translation poses, and - Upgrade rerun to v0.23 for my use case. So far, it works well, but there are still a few hitches: 1. Right now, it’s solid on iPhone and iPad; my Quest 3 client is still slow and not super reliable. 2. I’m using an older ARFlow branch focused on real-time streaming only—no spatial or temporal sync yet. Unity builds for iOS keep failing. 🛠️ 3. Nothing is saved locally to the client, so packet loss is a risk on shaky networks. There’s huge potential in tapping the ubiquitous sensors we carry around every day, and ARFlow is a big step toward making that easy

Streaming iPhone data in real-time directly to Rerun 🚀 The collection process is one of the most frustrating parts of building imitation-learning datasets. I’ve got a little army of sensors—📱 iPhone, iPad, Quest 3—but getting them temporally aligned, spatially aligned, AND seeing real-time feedback while recording is tough. I stumbled on a great library from Cake Lab (WPI) called ARFlow. It’s a thin client built on Unity’s ARFoundation that connects over gRPC to a server running Rerun for live data logging. I forked it to: - Log the SLAM translation poses, and - Upgrade rerun to v0.23 for my use case. So far, it works well, but there are still a few hitches: 1. Right now, it’s solid on iPhone and iPad; my Quest 3 client is still slow and not super reliable. 2. I’m using an older ARFlow branch focused on real-time streaming only—no spatial or temporal sync yet. Unity builds for iOS keep failing. 🛠️ 3. Nothing is saved locally to the client, so packet loss is a risk on shaky networks. There’s huge potential in tapping the ubiquitous sensors we carry around every day, and ARFlow is a big step toward making that easy

Pablo Vela

27,433 просмотров • 1 год назад

I see a lot of people commenting without even reading the caption or checking who I am. I’m a professional stunt rider, a movie stunt double and a content creator. I play characters, I create staged scenes because that is literally my job. Everything is planned, supervised and controlled. But even with all that, some people still rush into the comments to throw insults without understanding what they’re actually watching. Before getting angry, judging or making accusations, take a few seconds to look into what you’re seeing. It avoids a lot of unfairness and it shows respect for the people who spend time and energy creating entertaining content. And everything here is real work. It’s real riding, real effort and hours of preparation. There is never any artificial intelligence in my videos. I don’t blame anyone, but I kindly remind you to be careful with what you think you’re seeing before judging, because reality is not always what it looks like.

I see a lot of people commenting without even reading the caption or checking who I am. I’m a professional stunt rider, a movie stunt double and a content creator. I play characters, I create staged scenes because that is literally my job. Everything is planned, supervised and controlled. But even with all that, some people still rush into the comments to throw insults without understanding what they’re actually watching. Before getting angry, judging or making accusations, take a few seconds to look into what you’re seeing. It avoids a lot of unfairness and it shows respect for the people who spend time and energy creating entertaining content. And everything here is real work. It’s real riding, real effort and hours of preparation. There is never any artificial intelligence in my videos. I don’t blame anyone, but I kindly remind you to be careful with what you think you’re seeing before judging, because reality is not always what it looks like.

Sarah Lezito

43,317 просмотров • 10 месяцев назад

I've been researching Agents for the past 6 months and collected 40+ materials on the most capable architectures & implementations. The intent was to publish a comprehensive overview, like I did on RAG techniques, but been too busy with so sharing it here. There are some great intro lectures by Andrew Ng to start with. The following types of Agentic architectures are covered: 🤖 Chain of thought (Plan & Execute agent) 🤖 Tooling operators (An agent upon a set of tools, routing to them) - good for connecting external data storage & APIs, pretty fast and robust 🤖 ReAct (Thought - Action - Observation) - capable of iteratively executing complex tasks or answering complex queries 🤖 Self-Reflection - (Action - Observation / Evaluation - Reflection - Planning) - adds some quality and reasoning clarity compared to the ReAct scheme, might be slower 🤖 Agent upon agents (A multiagent scheme) - a quite complex setting, slow, but capable of executing very complex multistep tasks, not super robust as loops are a frequent issue. Most successful projects: AutoGPT, AgentGPT, MemGPT, GPT-Researcher, CrewAI, MetaGPT. There are also some arXiv papers & blog posts on the most important architectures. 🔗 All the materials are here: 🧠 The best part is there is a co-pilot to chat with all this knowledge! If you’d like to add some valuable publications on Agents to this collection - just share a link in the comments 👇

I've been researching Agents for the past 6 months and collected 40+ materials on the most capable architectures & implementations. The intent was to publish a comprehensive overview, like I did on RAG techniques, but been too busy with so sharing it here. There are some great intro lectures by Andrew Ng to start with. The following types of Agentic architectures are covered: 🤖 Chain of thought (Plan & Execute agent) 🤖 Tooling operators (An agent upon a set of tools, routing to them) - good for connecting external data storage & APIs, pretty fast and robust 🤖 ReAct (Thought - Action - Observation) - capable of iteratively executing complex tasks or answering complex queries 🤖 Self-Reflection - (Action - Observation / Evaluation - Reflection - Planning) - adds some quality and reasoning clarity compared to the ReAct scheme, might be slower 🤖 Agent upon agents (A multiagent scheme) - a quite complex setting, slow, but capable of executing very complex multistep tasks, not super robust as loops are a frequent issue. Most successful projects: AutoGPT, AgentGPT, MemGPT, GPT-Researcher, CrewAI, MetaGPT. There are also some arXiv papers & blog posts on the most important architectures. 🔗 All the materials are here: 🧠 The best part is there is a co-pilot to chat with all this knowledge! If you’d like to add some valuable publications on Agents to this collection - just share a link in the comments 👇

IVAN ILIN

113,984 просмотров • 2 лет назад

MARVEL JUST PLUCKED $5,000,000 OUT OF THEIR VFX BUDGET TO BUY OUT A 20-YEAR-OLD WHO REBUILT SPIDER-MAN IN 120 HOURS Disney spent $250,000,000 and forced 400 animators into crunch to render a masked, miserable hero. A broke student booted a single neural engine and built a maskless, overjoyed superhero in 5 days with zero render software. Marvel panicked, realized his pipeline beat their internal renders, and dropped a $5M buyout. Here is the exact technical stack that forced a $150B studio to write the check: > ZERO MOCAP HARDWARE - Driven by open-source spatial pose estimators. No physical tracking suits or clean plates required. > MID-AIR FACIAL LOCK - Locked consistent face features across high-speed lighting using a hyper-focused ControlNet depth harness. > LIQUIDATED CGI PIPELINES - Replaced 3D environments, manual keyframes, and render farms with real-time latent frame interpolation.

MARVEL JUST PLUCKED $5,000,000 OUT OF THEIR VFX BUDGET TO BUY OUT A 20-YEAR-OLD WHO REBUILT SPIDER-MAN IN 120 HOURS Disney spent $250,000,000 and forced 400 animators into crunch to render a masked, miserable hero. A broke student booted a single neural engine and built a maskless, overjoyed superhero in 5 days with zero render software. Marvel panicked, realized his pipeline beat their internal renders, and dropped a $5M buyout. Here is the exact technical stack that forced a $150B studio to write the check: > ZERO MOCAP HARDWARE - Driven by open-source spatial pose estimators. No physical tracking suits or clean plates required. > MID-AIR FACIAL LOCK - Locked consistent face features across high-speed lighting using a hyper-focused ControlNet depth harness. > LIQUIDATED CGI PIPELINES - Replaced 3D environments, manual keyframes, and render farms with real-time latent frame interpolation.

Shadow Nick

186,374 просмотров • 1 месяц назад

AI VIDEOS ARE BECOMING SCARILY NORMAL At first, this looks like a simple clip from a sunny day on campus. A girl is sitting on the grass with a notebook, people are relaxing around her, and the whole scene feels like something someone casually filmed on their phone. Nothing about it instantly feels fake. And that’s exactly the point. AI video is no longer only about creating robots, fantasy worlds or crazy impossible scenes. It’s getting better at copying the most normal parts of real life: sunlight, background movement, random people, small gestures and natural-looking camera shots. Not long ago, AI clips were easy to expose. The face would feel off, the hands would glitch, or the motion would look weird after a few seconds. Now those signs are becoming harder to catch. The real breakthrough isn’t that AI can make something shocking. It’s that it can make an ordinary everyday moment look so real that most people would scroll past without questioning it.

AI VIDEOS ARE BECOMING SCARILY NORMAL At first, this looks like a simple clip from a sunny day on campus. A girl is sitting on the grass with a notebook, people are relaxing around her, and the whole scene feels like something someone casually filmed on their phone. Nothing about it instantly feels fake. And that’s exactly the point. AI video is no longer only about creating robots, fantasy worlds or crazy impossible scenes. It’s getting better at copying the most normal parts of real life: sunlight, background movement, random people, small gestures and natural-looking camera shots. Not long ago, AI clips were easy to expose. The face would feel off, the hands would glitch, or the motion would look weird after a few seconds. Now those signs are becoming harder to catch. The real breakthrough isn’t that AI can make something shocking. It’s that it can make an ordinary everyday moment look so real that most people would scroll past without questioning it.

Inspector

4,001,255 просмотров • 9 дней назад

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Shikun Liu

22,464 просмотров • 11 месяцев назад