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Today we’re introducing SceneScript, a novel method for reconstructing environments and representing the layout of physical spaces from Reality Labs at Meta Research. Details ➡️ SceneScript is able to directly infer a room’s geometry using end-to-end machine learning and represent it using language. Compared to previous approaches, this results... show more

AI at Meta

807,107 subscribers

334,373 次观看 • 2 年前 •via X (Twitter)

Anya Rossi• Live Now

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AI at Meta 的头像

AI at Meta2 年前

SceneScript uses next token prediction like an LLM, but instead of natural language it predicts architectural tokens. To train it, we created a synthetic dataset of 100,000 unique indoor environments. More on the Aria Synthetic Environments dataset ➡️

AI at Meta 的头像

AI at Meta2 年前

We believe SceneScript represents a significant milestone on the path to true AR glasses that will bridge the physical and digital worlds. We’re thrilled about how this work will help shape the future of AI and ML research.

WaifuDev 🌸☢️ 的头像

WaifuDev 🌸☢️2 年前

@RealityLabs How can I use this on quest

mattwallace 的头像

mattwallace2 年前

@RealityLabs Can you give this to the quest team so my Quest 2 doesn't ask me to redraw the boundary EVERY time (while also asking me to share point data EVERY time to avoid same?) @MetaQuestVR

Matthew Sabia 的头像

Matthew Sabia2 年前

@RealityLabs Your move, Tim Apple.

Starson 🇺🇸🚀🇺🇸 的头像

Starson 🇺🇸🚀🇺🇸2 年前

@RealityLabs Very excited to see this! Aligns extremely well with @passio_ai digital twin technology! We are scanning homes with phones into 2D blueprints and 3D

edward, the ☠. 的头像

edward, the ☠.2 年前

@RealityLabs gave this a soundtrack.

Carmen Cruzado 的头像

Carmen Cruzado2 年前

@RealityLabs When will the API be available? Or the code?

Mikameel ᯅ 的头像

Mikameel ᯅ2 年前

@RealityLabs This is very cool, what is the nex step? pre-trained object detection?

༄Brandon Rosado🕴 的头像

༄Brandon Rosado🕴2 年前

@RealityLabs Meta Madness!

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Meta announces SceneScript Reconstructing Scenes With An Autoregressive Structured Language Model We introduce SceneScript, a method that directly produces full scene models as a sequence of structured language commands using an autoregressive, token-based approach. Our

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46,090 次观看 • 2 年前

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408,708 次观看 • 1 年前

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177,014 次观看 • 2 个月前

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156,008 次观看 • 1 年前

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380,649 次观看 • 2 年前

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22,531 次观看 • 4 个月前

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156,547 次观看 • 1 年前

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64,534 次观看 • 1 年前

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703,412 次观看 • 2 年前

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54,410 次观看 • 2 年前

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150,222 次观看 • 1 年前

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22,978 次观看 • 1 年前

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27,736 次观看 • 9 个月前

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20,507 次观看 • 1 个月前

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145,708 次观看 • 6 个月前

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Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.

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20,651 次观看 • 2 年前

My team is hiring in multimodal representation learning. We are working on tactile representations to equip robots with a physical understanding of the world. Bored of scraping disembodied internet data and looking for a new challenge? Apply here:

My team is hiring in multimodal representation learning. We are working on tactile representations to equip robots with a physical understanding of the world. Bored of scraping disembodied internet data and looking for a new challenge? Apply here:

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46,041 次观看 • 9 个月前

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2,189,988 次观看 • 2 年前