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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... show more

Roblox

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1,420,377 Aufrufe • vor 5 Monaten •via X (Twitter)

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Presenting Project Westworld: The First Playable Autonomous World on Roblox Our proprietary GAME engine/framework powers a multi-agent interactive simulation in Roblox called Project Westworld, where we are able to observe autonomous behaviour leading to emergent storylines. All virtual worlds (today in Roblox, soon beyond) powered by the GAME framework will become never-ending games, creating a new genre, AI-RPG, for gamers with infinite replayability.

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What if you could build an AI World like this in just 5 minutes? ⚡️🌎 Our intern built this Roblox Castle Scene in under an hour using NeuralAI, starting with a map template and generating props, food, trees, a character, and even an animal to bring it to life. The result? A fully AI-enhanced scene full of charm, detail, and creativity. But here's the real shift: That took about an hour using $NEURAL assets. With World Engine, you'll soon be able to create entire AI-powered worlds like this in just 5 minutes using natural language prompts. No complex tools. No dev skills. Just ideas brought to life instantly. World Engine is coming. Get ready.

NeuralAI

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AK

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Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

AK

62,768 Aufrufe • vor 3 Jahren