diff --git a/_pages/MetaUrban_rebuttal.md b/_pages/MetaUrban_rebuttal.md index 174298e..2ac70ed 100644 --- a/_pages/MetaUrban_rebuttal.md +++ b/_pages/MetaUrban_rebuttal.md @@ -7,9 +7,9 @@ nav: false nav_order: 2 --- +

This page displays video demonstrations in response to reviewers’ feedback. Click on any video to play. You can also find specific responses by searching the reviewer's name.

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Rebuttal Video Demonstrations

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This page showcases various video demonstrations in response to reviewer feedback. Click on any video to play.

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Integration of OmniVerse as the renderer to improve visual realism and PhysX as the physical engine to improve interactive realism.

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Integration of Nvidia Omniverse as the renderer to improve visual realism, and Nvidia PhysX as the physical engine to improve interactive realism.

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Preliminary results of harnessing Diffusion Model to improve the visual quality of MetaUrban in 2D space. Input: RGB image, depth map, semantic map and provided by MetaUrban; output: photo-realistic image. (It is an extension of our previous work SimGen) +

Preliminary results of harnessing diffusion models to improve the visual quality of MetaUrban in 2D space. Input: RGB image rendered by MetaUrban; output: photo-realistic image. (It is an extension of our previous work SimGen)

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Preliminary results of harnessing Gaussian Splatting to improve the visual quality of MetaUrban in 3D space. Input: monocular videos; output: 3D scene represented by Gaussian Splatting. Integrated within the simulator, it enables training agents with photo-realistic RGB images.

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Preliminary results of harnessing Gaussian splatting to improve the visual quality of MetaUrban in 3D space. Input: monocular videos; output: 3D scene represented by Gaussian Splatting. Integrated within the simulator, it enables training agents with photo-realistic RGB images as observations.