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LichtFeld Studio in Action! GPLv3 - no subscriptions, no data scraping, forever free! - Trains in 20 minutes at 4k for 60k steps with 2m Gaussians and MCMC densification. - With enabled bilateral guidance (CUDA version), it totals 60 minutes. - Pretty much floater free. Makes me quite proud...

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MrNeRF

14,022 subscribers

20,299 Aufrufe • vor 10 Monaten •via X (Twitter)

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Code and data are now online for CameraHMR, our state-of-the-art parametric 3D human pose and shape (HPS) estimation method that will appear at hashtag#3DV2025. There are 4 key contributions that make it so accurate and robust: 1. To get accurate 3D shape and pose as well as good alignment to image features, you need to know the focal length of the camera. To solve this, we train HumanFOV to compute the field of view. 2. We introduce CameraHMR, which integrates HumanFOV into HMR2.0 to exploit the estimated focal length. 3. To get accurate pseudo ground truth (pGT) training data, we compute the focal length for images in 4DHumans dataset and modify SMPLify to take this into account. 4. But SMPLify only uses sparse 2D keypoints, which do not capture body shape. So we train a dense surface keypoint detector, DenseKP, on BEDLAM and run it on 4DHumans, resulting in improved body shape. The resulting method is CamSMPLify. We iterate training CameraHMR and running CamSMPLify on the training set initialized with CameraHMR. This results in much improved pGT for 4DHumans and a SOTA single-image HMR method.

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21,696 Aufrufe • vor 1 Jahr

🧵24/34 Inner Misalignment --- Consider this simplified experiment: We want this AI to find the exit of the maze. So we feed it millions of maze variations and reward it when it finds the exit. Please notice that in the worlds of the training data the apples are red and the exit is green. After enough training, our observation is that it has become extremely capable at solving mazes and finding the exit, we feel very confident it is aligned, so then we deploy it to the real world. The real world will be different though, it might have green apples and a red door. The AI geeks call this distributional shift. We expected that the AI will generalise and find the exit again, but in fact we now realise that the AI learned something completely different from what we thought. All the while we thought it learned how to find the exit, it had learned how to go after the green thing. Its behaviour was perfect in training. And most importantly, this AI is not stupid, it is an extremely capable AI that can solve extremely complex mazes. It’s just mis-aligned on the inside. Fishing for Failure modes --- The way to handle the shift between the training and deployment distributions is with methods like adversarial training: feeding it with a lot of generated variations and trying to make it fail so the weakness can be fixed. In this case, we generate an insane amount of maze variations, we discover those for which it fails to find the exit (like the ones with the green apples or the green walls or something), we generate many more similar to that and train it with reinforcement learning until it performs well at those as well. The hope is that we will cover everything it might encounter later when we deploy it in real life. There exist at least 2 basic ways this approach falls apart: First, there will never be any guarantee that we’ll have covered every possible random thing it might encounter later when we deploy it in real life. It’s very likely it will have to deal with stuff outside its training set which it will not know how to handle and will throw it out of balance and break it away from its expected behavioural patterns. The cascade effect of such a broken mind operating in the open world can be immense, and with super-capable runaway rogue agents, self-replicating and recursively self-improving, the phenomenon could grow and spread to an extinction-level event. ...
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🧵24/34 Inner Misalignment --- Consider this simplified experiment: We want this AI to find the exit of the maze. So we feed it millions of maze variations and reward it when it finds the exit. Please notice that in the worlds of the training data the apples are red and the exit is green. After enough training, our observation is that it has become extremely capable at solving mazes and finding the exit, we feel very confident it is aligned, so then we deploy it to the real world. The real world will be different though, it might have green apples and a red door. The AI geeks call this distributional shift. We expected that the AI will generalise and find the exit again, but in fact we now realise that the AI learned something completely different from what we thought. All the while we thought it learned how to find the exit, it had learned how to go after the green thing. Its behaviour was perfect in training. And most importantly, this AI is not stupid, it is an extremely capable AI that can solve extremely complex mazes. It’s just mis-aligned on the inside. Fishing for Failure modes --- The way to handle the shift between the training and deployment distributions is with methods like adversarial training: feeding it with a lot of generated variations and trying to make it fail so the weakness can be fixed. In this case, we generate an insane amount of maze variations, we discover those for which it fails to find the exit (like the ones with the green apples or the green walls or something), we generate many more similar to that and train it with reinforcement learning until it performs well at those as well. The hope is that we will cover everything it might encounter later when we deploy it in real life. There exist at least 2 basic ways this approach falls apart: First, there will never be any guarantee that we’ll have covered every possible random thing it might encounter later when we deploy it in real life. It’s very likely it will have to deal with stuff outside its training set which it will not know how to handle and will throw it out of balance and break it away from its expected behavioural patterns. The cascade effect of such a broken mind operating in the open world can be immense, and with super-capable runaway rogue agents, self-replicating and recursively self-improving, the phenomenon could grow and spread to an extinction-level event. ...

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535,291 Aufrufe • vor 1 Jahr

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Arteta: “You want to be part of the noise or not? If not then go to do something else, be part of another different club. You want to be Arsenal and everybody's been demanding for 10 years, 15 years. We need to go back there. Now we are there and now what? “You don't want noise. Noise is part and the bullets are part of that. Try to deal with them in the right way and achieve what you are looking for.” On Man City having the Premier League title in their hands: “We have to do what we have to do, and nothing is going to change for anybody at the end. You're going to have to win the next match and if you do that, you're going to be in a much stronger position. That's the only thing.”

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FOR THOSE WHO ARE CONFUSED… We experience TIME as a straight line, one moment after another, PAST TO PRESENT TO FUTURE. But that is only how it appears from inside the system. Beings in higher dimensions, what some call “ORACLES” OR “GODS,” do not experience time like this. They do not move through it. They observe it in its entirety. A closer analogy would be CHANNELS ON A SCREEN. What is playing on FOX and what is playing on CBS is not separate in existence, it is only separated by perspective. Both are happening at the same time, fully active, fully real. In the same way, what we call 1950 and what we call 2004 are not distant points moving away from each other. They are both present within the same total field of reality. From that level of awareness, nothing is being “waited on.” There is NO DELAY. There is only ACCESS. A shift of perception is all that is needed, like changing the channel. So if everything exists at once, then your FUTURE is not something that is coming toward you. It is something that already exists. The version of you 20 YEARS FROM NOW is already there. You have already CHOSEN THAT PATH, you have already GONE THROUGH WHAT COMES WITH IT, you have already LIVED THROUGH IT ALL. And what you are doing now is not creating it. You are MOVING THROUGH IT. Slowly remembering why you are there at all.

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1:08

🚨 President Trump just looked into the camera and issued a FINAL ultimatum to the Iranian regime: Make a deal in the next 2-3 weeks or the U.S. military will OBLITERATE every target in sight. “We have ALL the cards, they hold NONE.” TRUMP: “Yet if during this period of time no deal is made, we have our eyes on key targets.” “If there is no deal, we are going to hit each and every one of their electric generating plants very hard and probably simultaneously.” “We have not hit their oil, even though that’s the easiest target of all, because it would not give them even a small chance of survival or rebuilding.” “But we could hit it and it would be gone.” “And there’s not a thing they could do about it.” “They have no anti-aircraft equipment. Their radar is 100% annihilated.” “We are unstoppable as a military force.” “The nuclear sites that we obliterated with the B-2 bombers have been hit so hard that it would take months to get near the nuclear dust.” “And we have it under intense satellite surveillance and control. If we see them make a move, even a move for it will hit them with missiles very hard, again.”

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288,376 Aufrufe • vor 3 Monaten

Carlos about Williams upgrades and expectations “We are not expecting upgrades, I think we've been clear than until the end of summer we are not bringing anything.” “It doesn't worry me, in other tracks we are gonna be faster than in here. Barcelona is the worst track for our car. I hope to fight to be in the top 10 in Canada.” “It has never been a Williams track, even with a perfect weekend we could have only achieve 1 or 2 points so is not like something big has escaped from our hands.” “Now we have to make sure everything is on the right place for Canada” #SpanishGP
0:44

Carlos about Williams upgrades and expectations “We are not expecting upgrades, I think we've been clear than until the end of summer we are not bringing anything.” “It doesn't worry me, in other tracks we are gonna be faster than in here. Barcelona is the worst track for our car. I hope to fight to be in the top 10 in Canada.” “It has never been a Williams track, even with a perfect weekend we could have only achieve 1 or 2 points so is not like something big has escaped from our hands.” “Now we have to make sure everything is on the right place for Canada” #SpanishGP

ari

61,519 Aufrufe • vor 1 Jahr

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.
4:06

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.

🇺🇸RealRobert🇺🇸

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I’m so very very proud present my greatest work yet ❤️🔥🎥 WAR FOREVER • Part One I hope this is inspiration to those just starting, those questioning it, and us veterans to celebrate how far we’ve come in so short of time. We are there. We are at cinema grade, no question. It may have been only a few years but for those of us in the grind everyday this is what we did it for, this is what we stuck in there learning and pushing and struggling for. I am so proud of this and the other work I have coming. Inching ever closer tot he dream to direct a feature film one day ❤️🎥. Thank you to everyone who checks it out and supports me and NAKID Pictures ® + STAGES LFG! 👾🔥
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I’m so very very proud present my greatest work yet ❤️🔥🎥 WAR FOREVER • Part One I hope this is inspiration to those just starting, those questioning it, and us veterans to celebrate how far we’ve come in so short of time. We are there. We are at cinema grade, no question. It may have been only a few years but for those of us in the grind everyday this is what we did it for, this is what we stuck in there learning and pushing and struggling for. I am so proud of this and the other work I have coming. Inching ever closer tot he dream to direct a feature film one day ❤️🎥. Thank you to everyone who checks it out and supports me and NAKID Pictures ® + STAGES LFG! 👾🔥

Dustin Hollywood

14,836 Aufrufe • vor 4 Monaten

What do you do for a living today? AI and intelligent robots will increasingly take over many tasks from humans. And they will do it better, faster and cheaper. Yes, yours too. Many people will work much less or not at all in normal jobs. And earn less or even nothing. What will you live on then? And how will our companies survive when their customers don't have the money to buy their products and services? This is the key question for your financial future! We will take a look at three scenarios for the future of our life and the economy. If AI and robots work for us on a large scale, our future could be great. However, it could also turn out quite differently. We could also wake up in a horror scenario. Because we have a huge and catastrophic flaw in our economic system that could lead to disaster in a world of AI and robotics. Correcting this mistake is the biggest challenge. What the flaw is and how we can fix it, you'll learn in this video.
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What do you do for a living today? AI and intelligent robots will increasingly take over many tasks from humans. And they will do it better, faster and cheaper. Yes, yours too. Many people will work much less or not at all in normal jobs. And earn less or even nothing. What will you live on then? And how will our companies survive when their customers don't have the money to buy their products and services? This is the key question for your financial future! We will take a look at three scenarios for the future of our life and the economy. If AI and robots work for us on a large scale, our future could be great. However, it could also turn out quite differently. We could also wake up in a horror scenario. Because we have a huge and catastrophic flaw in our economic system that could lead to disaster in a world of AI and robotics. Correcting this mistake is the biggest challenge. What the flaw is and how we can fix it, you'll learn in this video.

Dr Pero Micic

12,010,133 Aufrufe • vor 1 Jahr