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LERF: Language Embedded Radiance Fields TL;DR: Grounding CLIP vectors volumetrically inside a NeRF allows flexible natural language queries in 3D abs: project page:

251,233 次观看 • 3 年前 •via X (Twitter)

9 条评论

Fabien Benetou 的头像
Fabien Benetou3 年前

Damn...

Allknight 的头像
Allknight2 年前

@zenstyle Now do Sarah Connor >:D

potatoruning 的头像
potatoruning3 年前

looks good

David Asem 的头像
David Asem3 年前

For a sex maybe 🤔 fix the @AR15COM has a fix

Viho 的头像
Viho3 年前

🤩

MoonGotArt 的头像
MoonGotArt3 年前

Will this be open source?

Bill Xue 的头像
Bill Xue3 年前

@SaveToNotion #tweet

Guruprasad Venkataraghavan 的头像
Guruprasad Venkataraghavan3 年前

@memdotai mem it

$maczter 的头像
$maczter2 年前

@icreatelife 🤯🤯🤯

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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.

AK

62,768 次观看 • 3 年前