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Google announces InseRF Text-Driven Generative Object Insertion in Neural 3D Scenes paper page: InseRF generates an object in a 3D scene via a text prompt and one 2D bounding box

205,943 Aufrufe • vor 2 Jahren •via X (Twitter)

10 Kommentare

Profilbild von Georgia Gkioxari
Georgia Gkioxarivor 2 Jahren

The adding of the panettone got me. Panetonnes all year round please in NeRFs and the real world!

Profilbild von Mohamad Shahbazi
Mohamad Shahbazivor 2 Jahren

Thanks a lot for featuring our work @_akhaliq! Here is the project page for more details and results:

Profilbild von DAS
DASvor 2 Jahren

I bet this works as good as most google AI products we’ve seen demo(ed)

Profilbild von The AI Edge
The AI Edgevor 2 Jahren

Existing methods for 3D scene editing are mostly effective for style and appearance changes or removing objects. But generating new objects is a challenge for them. InseRF addresses this by combining advances in NeRFs with advances in generative AI and also shows potential for future improvements in generative 2D and 3D models.

Profilbild von Vaibhav Tulsyan
Vaibhav Tulsyanvor 2 Jahren

@adyaman

Profilbild von Cédric Limousin
Cédric Limousinvor 2 Jahren

If real, that's the future of video. Starting from a blank scene, adding assets, then actors and animating them while being able to move the camera where you want.

Profilbild von Supreme
Supremevor 2 Jahren

what the

Profilbild von Poe Allen
Poe Allenvor 2 Jahren

What is the big deal my bro

Profilbild von Nick Moran
Nick Moranvor 2 Jahren

"Add a panettone on the tray" is an odd prompt if you're already drawing a bounding box over the tray. The figure in the paper makes it look like the actual prompt would just be "a panettone".

Profilbild von 𝑫𝒂𝒏𝒊𝒆𝒍 𝑺𝒄𝒐𝒕𝒕 𝑴𝒂𝒕𝒕𝒉𝒆𝒘𝒔 🇦🇺
𝑫𝒂𝒏𝒊𝒆𝒍 𝑺𝒄𝒐𝒕𝒕 𝑴𝒂𝒕𝒕𝒉𝒆𝒘𝒔 🇦🇺vor 2 Jahren

Nice, but perhaps they need to use the existing scene to do a little bit of global illumination and environment mapping from the scene to the inserted object?

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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.

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