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GEN-1 still works with lights off, and generalizes under harsh lighting conditions. The model uses raw video pixels to make decisions, so strong lighting changes can drastically alter its input distribution. Yet performance still holds. Why? GEN-1 was pre-trained on a massive, diverse dataset of different lighting conditions—everywhere from... show more

Generalist

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18,101 次观看 • 2 个月前 •via X (Twitter)

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GEN-1 plays the 🐚 shell game, trained on just 1 hr of robot data. It also generalizes to unseen objects, like berkay 's car keys. Physical AI models should be capable of benchmark tasks like this one. It's interesting for the all the reasons Rhoda AI calls out -- requires visual memory, and the model must track the cups from the very start, at high frame rates. Interestingly, GEN-1 appears to exhibit a degree of "active perception." It's subtle; the hands can sometimes appear to "follow" the cups, using its own movements to help attend to where it thinks the object should be. Read more about GEN-1 in our blog post in the comments below ↓

GEN-1 plays the 🐚 shell game, trained on just 1 hr of robot data. It also generalizes to unseen objects, like berkay 's car keys. Physical AI models should be capable of benchmark tasks like this one. It's interesting for the all the reasons Rhoda AI calls out -- requires visual memory, and the model must track the cups from the very start, at high frame rates. Interestingly, GEN-1 appears to exhibit a degree of "active perception." It's subtle; the hands can sometimes appear to "follow" the cups, using its own movements to help attend to where it thinks the object should be. Read more about GEN-1 in our blog post in the comments below ↓

Generalist

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GEN-1 delicately arranges potato chips, and lifts a heavy bag of potatoes — from a gentle touch to a strong grip. Read more about Gen-1 in our blog posts in the comments below ↓

GEN-1 delicately arranges potato chips, and lifts a heavy bag of potatoes — from a gentle touch to a strong grip. Read more about Gen-1 in our blog posts in the comments below ↓

Generalist

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MaterialFusion Enhancing Inverse Rendering with Material Diffusion Priors discuss: Recent works in inverse rendering have shown promise in using multi-view images of an object to recover shape, albedo, and materials. However, the recovered components often fail to render accurately under new lighting conditions due to the intrinsic challenge of disentangling albedo and material properties from input images. To address this challenge, we introduce MaterialFusion, an enhanced conventional 3D inverse rendering pipeline that incorporates a 2D prior on texture and material properties. We present StableMaterial, a 2D diffusion model prior that refines multi-lit data to estimate the most likely albedo and material from given input appearances. This model is trained on albedo, material, and relit image data derived from a curated dataset of approximately ~12K artist-designed synthetic Blender objects called BlenderVault. we incorporate this diffusion prior with an inverse rendering framework where we use score distillation sampling (SDS) to guide the optimization of the albedo and materials, improving relighting performance in comparison with previous work. We validate MaterialFusion's relighting performance on 4 datasets of synthetic and real objects under diverse illumination conditions, showing our diffusion-aided approach significantly improves the appearance of reconstructed objects under novel lighting conditions. We intend to publicly release our BlenderVault dataset to support further research in this field.

MaterialFusion Enhancing Inverse Rendering with Material Diffusion Priors discuss: Recent works in inverse rendering have shown promise in using multi-view images of an object to recover shape, albedo, and materials. However, the recovered components often fail to render accurately under new lighting conditions due to the intrinsic challenge of disentangling albedo and material properties from input images. To address this challenge, we introduce MaterialFusion, an enhanced conventional 3D inverse rendering pipeline that incorporates a 2D prior on texture and material properties. We present StableMaterial, a 2D diffusion model prior that refines multi-lit data to estimate the most likely albedo and material from given input appearances. This model is trained on albedo, material, and relit image data derived from a curated dataset of approximately ~12K artist-designed synthetic Blender objects called BlenderVault. we incorporate this diffusion prior with an inverse rendering framework where we use score distillation sampling (SDS) to guide the optimization of the albedo and materials, improving relighting performance in comparison with previous work. We validate MaterialFusion's relighting performance on 4 datasets of synthetic and real objects under diverse illumination conditions, showing our diffusion-aided approach significantly improves the appearance of reconstructed objects under novel lighting conditions. We intend to publicly release our BlenderVault dataset to support further research in this field.

AK

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GEN-1 cleans white board Read more about GEN-1 in our blog post in the comments below ↓

GEN-1 cleans white board Read more about GEN-1 in our blog post in the comments below ↓

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Everyday for the past 2 weeks, we've been sharing something new from GEN-1, our latest milestone in scaling robot learning. This has never been done before. Going from ideas to skills in days (or faster) is what physical AI models should deliver. More coming. Stay tuned. Read more about it in our blog post in the comments below ↓

Everyday for the past 2 weeks, we've been sharing something new from GEN-1, our latest milestone in scaling robot learning. This has never been done before. Going from ideas to skills in days (or faster) is what physical AI models should deliver. More coming. Stay tuned. Read more about it in our blog post in the comments below ↓

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Our vision is for AI that uses world models to adapt in new and dynamic environments and efficiently learn new skills. We’re sharing V-JEPA 2, a new world model with state-of-the-art performance in visual understanding and prediction. V-JEPA 2 is a 1.2 billion-parameter model, trained on video, that can enable zero-shot planning in robots—allowing them to plan and execute tasks in unfamiliar environments. Learn more about V-JEPA 2 ➡️ As we continue working toward our goal of achieving advanced machine intelligence (AMI), we’re also releasing three new benchmarks for evaluating how well existing models can reason about the physical world from video. Learn more and download the new benchmarks ➡️

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Cartoon style post processing I recently made. The best part is that it runs entirely in real time, and works well with my models. It's still a bit limited, but it works well for the basics of detecting silhouettes and subtle lighting tweaks.

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junk lighting is killing us in this study, changing lighting from blue lights to full spectrum eliminated ADHD and learning disabilities this boy was absolutely tweaking under fake lighting but when under full spectrum he learned to read and started fully participating in class.

junk lighting is killing us in this study, changing lighting from blue lights to full spectrum eliminated ADHD and learning disabilities this boy was absolutely tweaking under fake lighting but when under full spectrum he learned to read and started fully participating in class.

Carnivore Aurelius ©🥩 ☀️🦙

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🧵1/3 I partnered with Kling to make a promo for their new 3.0 model. I came up with the concept, created it and delivered it all on my own in 3 days of early access, I wanted to make something that showed how Kling could be used to tell a diverse range of stories in a diverse range of styles. I've honestly been blown away be how incredible the model is and I haven't even scratched the surface of features like the new 3.0 OMNI model. For me Kling 2.6 was already best in class for most things but 3.0 sees massive improvements to video quality and detail (no need to add grain to mask imperfections) and prompt adherence. Also you can time your generations to be as long as 15 seconds and as short as 3, this is fantastic for pacing your scenes. Read on to find out more about the new Performance and Multi shot features!👇

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The cartel has motorcycle gangs driving around from building to building in Puerto Vallarta lighting them on fire This is Mexico’s version of a 7-Eleven being lit on fire. They’re also lighting grocery stores and other various buildings on fire Democrats want an open border for America

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GEN-1 removes thumbtacks and papers from corkboard. Other tasks GEN-1 can do: Read more about GEN-1, our latest foundation model for the physical world:

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The iconic 7.62mm Degtyaryov light machine gun makes its first appearance in this new #EscapefromTarkov ASMR episode from Battlestate Games. Today, we will service this mid-20th-century machine gun to ensure flawless performance in the harsh conditions of Norvinsk region. Link to the full video in the comments below👇

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Can we enable free-viewpoint renderings of scenes under various lighting conditions? Yes, we can! And just from SINGLE videos. Here are 🌃 night simulations from input videos with hard shadows.

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Gen-1 ties zipties Read more about Gen-1 in our blog posts in the comments below ↓

Gen-1 ties zipties Read more about Gen-1 in our blog posts in the comments below ↓

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Asked by Todd Lewis on what is the biggest challenge in putting together the new PGA Tour schedule, Tiger Woods said this: "I think it's trying to serve literally everyone, from the player side of it, from our media partners, from all of our title sponsors, from the local communities or even changing venues and going to bigger markets. It's what do we need to do from a competitive model to make our tour the best product it can possibly be each and every year and still have room for development. How do we do all of that at the same time."

Asked by Todd Lewis on what is the biggest challenge in putting together the new PGA Tour schedule, Tiger Woods said this: "I think it's trying to serve literally everyone, from the player side of it, from our media partners, from all of our title sponsors, from the local communities or even changing venues and going to bigger markets. It's what do we need to do from a competitive model to make our tour the best product it can possibly be each and every year and still have room for development. How do we do all of that at the same time."

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