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We have 13B hours of player interaction on the platform monthly. This data enables us to train intelligent NPCs that can reason and interact in 3D worlds. Our training goes beyond videos of gameplay and simple WASD actions, utilizing our full data model for a more detailed representation of... show more

Roblox

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288,109 次观看 • 5 个月前 •via X (Twitter)

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In our research lab, we are building “real-time dreaming” - the ability to generate fully playable video worlds prompted from any text or image. Our real-time, action conditioned world model (currently running internally at 16fps at 832x480p) is trained on a combination of data, including proprietary Roblox 3D avatar/world interaction data. World models are different from multiplayer engines in that they store state and memory in video latents. Roblox is multiplayer, and we are actively researching optimal ways to simultaneously store state for thousands of players, and keep them in sync with their environment. Our world model leverages database technology which stores all user interactions on Roblox in a vector format that can be used to re-render video and interaction from any camera angle. We see several immediate uses for our Roblox world model. We will use it side-by-side text, image and video prompts as a way to launch auto-generation of immersive worlds. In Roblox Studio, a creator could walk around and use prompts to “paint” a world and then convert it into a 3D representation or direct to Roblox native as a way for many people to play simultaneously. All of this comes alive as we explore the notion of a “Dream Theater” - where one user is dreaming, while others watch and prompt them. 2/4

Roblox

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With the power of our new Audio API Beta, developers can build immersive soundscapes and enhance voice communications. From adding in-experience walkie-talkies to giving voice to NPCs, this API unlocks a whole new dimension for developers. #RobloxDev

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228,311 次观看 • 2 年前

To support massive, high-fidelity worlds, our engine implements an efficient LOD strategy with a cloud transcoding system that streams meshes and textures, even on lower end mobile devices. We are expanding this framework to AI upsampling of 3D worlds from prompts. In this preview video below, with a single user prompt, our 3D upsampling technology updates the geometry and texture to transform the classic Roblox game Crossroads into a fantasy world with rich organic detail. 3/4

To support massive, high-fidelity worlds, our engine implements an efficient LOD strategy with a cloud transcoding system that streams meshes and textures, even on lower end mobile devices. We are expanding this framework to AI upsampling of 3D worlds from prompts. In this preview video below, with a single user prompt, our 3D upsampling technology updates the geometry and texture to transform the classic Roblox game Crossroads into a fantasy world with rich organic detail. 3/4

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1/ We are speed running industrial robotics. It took us just 19 days from the first day of data collection to filming a 2.5-hour continuous run of our model autonomously breaking down industrial containers — zero human intervention. The data efficiency of our DVA model is fundamentally changing how fast we bring robots out of the lab and into the factory. Autonomous operation with 3 hours of data collection at a customer factory.

1/ We are speed running industrial robotics. It took us just 19 days from the first day of data collection to filming a 2.5-hour continuous run of our model autonomously breaking down industrial containers — zero human intervention. The data efficiency of our DVA model is fundamentally changing how fast we bring robots out of the lab and into the factory. Autonomous operation with 3 hours of data collection at a customer factory.

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Finland President Alexander Stubb: Well, we have to be more independent in some cases. If you, for example, look at storage of personal data, obviously a lot of Europeans are angry that their personal data is stored outside of the EU. So we have to build our own data centers and our own AI factories so we can actually own the data of our own citizens. But in the end, there is no such thing as European full autonomy. You will always need to collaborate with our friends in Canada, in the US, in other partnerships around the world. ASML and other tech companies can play a crucial role both in the US and here.

Finland President Alexander Stubb: Well, we have to be more independent in some cases. If you, for example, look at storage of personal data, obviously a lot of Europeans are angry that their personal data is stored outside of the EU. So we have to build our own data centers and our own AI factories so we can actually own the data of our own citizens. But in the end, there is no such thing as European full autonomy. You will always need to collaborate with our friends in Canada, in the US, in other partnerships around the world. ASML and other tech companies can play a crucial role both in the US and here.

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People have been sending me clips of them seeing my RAT NPCs in worlds. I love this moment of hillarity. This is what I was aiming for. Thank you so much for the reception. I hope to bring a new era to VRChat and Social VR spaces where NPCs become more common! #gamedev

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In our latest episode of Tech Talks, we explore the future of agentic game creation at Roblox. Discover how Roblox Assistant is evolving beyond a prompt tool into an AI-native system that can plan, execute, and verify complex game development tasks alongside creators.

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86,735 次观看 • 3 个月前

We discovered an emergent property of VLAs like π0/π0.5/π0.6: as we scale up pre-training, the model learns to align human videos and robot data! This gives us a simple way to leverage human videos. Once π0.5 knows how to control robots, it can naturally learn from human video.

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Physics-based Motion Retargeting from Sparse Inputs paper page: Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.

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OpenLedger

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To give you a perspective on how advanced the NPC interaction system in GTA VI is going to be, here's a direct comparison between GTA V and Red Dead Redemption 2 In GTA V, the only option is to greet or insult NPCs depending on the character that you're playing as, in this case here Trevor will insult them. Red Dead Redemption 2 on the other hand has a completely different NPC interaction system where you have the option to greet, antagonize or rob NPCs. These options alone can give you the freedom to greet an NPC first, then immediately antagonize them to allow for different outcomes based on how you choose to interact with NPCs. This alone was a game changer and you can expect Grand Theft Auto VI to have an even more complex NPC interaction system compared to Red Dead Redemption 2, with more dialogue options and different outcomes. Its probably one of the reasons why GTA VI is taking much longer to develop, imagine how many lines of dialogue Rockstar Games had to record and make all of them work seamlessly by the time the game comes out.

To give you a perspective on how advanced the NPC interaction system in GTA VI is going to be, here's a direct comparison between GTA V and Red Dead Redemption 2 In GTA V, the only option is to greet or insult NPCs depending on the character that you're playing as, in this case here Trevor will insult them. Red Dead Redemption 2 on the other hand has a completely different NPC interaction system where you have the option to greet, antagonize or rob NPCs. These options alone can give you the freedom to greet an NPC first, then immediately antagonize them to allow for different outcomes based on how you choose to interact with NPCs. This alone was a game changer and you can expect Grand Theft Auto VI to have an even more complex NPC interaction system compared to Red Dead Redemption 2, with more dialogue options and different outcomes. Its probably one of the reasons why GTA VI is taking much longer to develop, imagine how many lines of dialogue Rockstar Games had to record and make all of them work seamlessly by the time the game comes out.

NikTek

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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.

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.

AK

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Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

54,084 次观看 • 8 个月前