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We are proud of our auto-redirection algorithm, AI automatically matches the model skeleton, this algorithm is provided either in the webcam version or the free version, no need to adjust the model, just a few minutes to make your model very elegantly kinetically, even if you are a cat.

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CYANPUPPETS

10,107 subscribers

17,469 просмотров • 2 лет назад •via X (Twitter)

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At the end of the day, we are all struggling to be a better version of ourself. A version that we can proud of and make our lobe one proud. Sumpah this vid just make me cry so hard. 🥲
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At the end of the day, we are all struggling to be a better version of ourself. A version that we can proud of and make our lobe one proud. Sumpah this vid just make me cry so hard. 🥲

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Here is an updated version of the mod, it almost completed, it just need a few updates on the model and fixing the teeth clipping.
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Here is an updated version of the mod, it almost completed, it just need a few updates on the model and fixing the teeth clipping.

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Microsoft's Chief of AI just announced that AI will replace almost all white-collar jobs within 18 months. Big Tech used our data to train their AI. Now that same AI is coming for our jobs. And we get nothing in return? If AI replaces us…we should own it. That’s why we built Action Model Not another chatbot. A community-owned AI model that acts. An AI trained by the people. Owned by the people. No billionaires controlling it. No extracting value from users. No “you are the product” model. Over 150,000+ people are already earning ownership in the AI future with Action Model. You should be too.
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Microsoft's Chief of AI just announced that AI will replace almost all white-collar jobs within 18 months. Big Tech used our data to train their AI. Now that same AI is coming for our jobs. And we get nothing in return? If AI replaces us…we should own it. That’s why we built Action Model Not another chatbot. A community-owned AI model that acts. An AI trained by the people. Owned by the people. No billionaires controlling it. No extracting value from users. No “you are the product” model. Over 150,000+ people are already earning ownership in the AI future with Action Model. You should be too.

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We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.
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We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.

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Today, we shared evals for an early version of the next model in our o-model reasoning series: OpenAI o3
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Google presents Still-Moving Customized Video Generation without Customized Video Data Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on "frozen videos" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.

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Palette and I are so honored to make the first 3D customizable vtuber avatar, which is a 3D version of a Live2D customizable model! 🩵 -^u^- We've made a Warudo plugin so that you can swap between our outfits and hair styles, no need for any prior 3D experience or external software to use our avatar!✨

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39,341 просмотров • 8 месяцев назад

I cant believe I rigged all this just to end up using a Tshirt version of this model the whole year. I think I learned a very important lesson on overcomplicating designs, and I hope it will show in the new October model where the goal was to make something simple and effective
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I cant believe I rigged all this just to end up using a Tshirt version of this model the whole year. I think I learned a very important lesson on overcomplicating designs, and I hope it will show in the new October model where the goal was to make something simple and effective

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CYANPUPPETS

58,611 просмотров • 1 год назад