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White Light Reference for Machine Learning. Meet our inhouse tech Doggo "Rolo". "More Doggo than Doggo" Since July we've been redesigning our scanning pipeline to work with the new 3D Gaussian Splatting for Real-Time Radiance Field Rendering method from Inria. IR's AeonX capture system has been designed to capture...

236,811 次观看 • 2 年前 •via X (Twitter)

11 条评论

Pianaland 🇪🇺 🇺🇦 🇹🇼🇮🇱 的头像
Pianaland 🇪🇺 🇺🇦 🇹🇼🇮🇱2 年前

Is the fur just geometry? Traditional 3d softwares go arround the theme with a huge variety of solutions. Or this just a reference fo then make a proper 3d model (im imagine that what the cam can’t see there are no data / no model)

Infinite-Realities 的头像
Infinite-Realities2 年前

This is more useful for reference as it's an approximation albeit a very good looking one but I bet the folks at Epic could work some interesting magic with it if they had a commercial license to integrate the method into Unreal.

Dan Lowe 的头像
Dan Lowe2 年前

That's crazy! Great test subject: The hair detail and shading really shows this approach off.

Furkan Gözükara 的头像
Furkan Gözükara2 年前

I read the Readme but confusing So you provided photos of this dog trained model and then viewed with viewer? How is the workflow?

Infinite-Realities 的头像
Infinite-Realities2 年前

The images are aligned in either Reality Capture, or Metashape, or Colmap. They are trained with the Guassian Splatting process then viewed in a modified version of Sibr.

Zvezdan Nedeljkovic 的头像
Zvezdan Nedeljkovic2 年前

I am currently trying to figure out how to install everything and make my first model. As I figuren you need colmap to feed it to the gaussin splatting? What do you export from Reality Capture? Great results btw :)

Infinite-Realities 的头像
Infinite-Realities2 年前

Thanks we don't have a defined pipeline that we can share for the moment but I think others have started to document and share their pipeline. I think more resources will come online soon. We plan to create an install write up at some point.

Giddy Kong: The Gaming Ape 的头像
Giddy Kong: The Gaming Ape2 年前

You're showing us that Rolo isn't real... but my brain refuses to listen.

Vlad Erium 🇯🇵 的头像
Vlad Erium 🇯🇵2 年前

Looks cool!

darthgera123 的头像
darthgera1232 年前

Hi sorry I dont understand but are these renderings with multiple light conditions?

Infinite-Realities 的头像
Infinite-Realities2 年前

These are viewed in real-time baked lighting results.

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Nvidia announces GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning paper page: Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.

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