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inverse rendering?

35,456 次观看 • 1 天前 •via X (Twitter)

11 条评论

cognito 的头像
cognito1 天前

You can run the same experiment using colmap + gaussian splat and get arguably better results

neilson 的头像
neilson1 天前

some reasons this is slightly different 1. these rooms aren't real 2. any input view can be changed in realtime to change the entire scene

Marcin Kobylecki 的头像
Marcin Kobylecki1 天前

I gave it a try, and I must admit I actually had quite a bit of fun with it. There is one issue, however: it lacks the production quality required for a film; the realism comes across better than the 3D animation style.

EX3D 的头像
EX3D1 天前

Fucking incredible.

Gio Gargiulo 的头像
Gio Gargiulo1 天前

What am I looking at?

professional biaser 的头像
professional biaser1 天前

absolutely mental. literally alien tech.

Rowdy Burns 的头像
Rowdy Burns1 天前

What software?

tommy 的头像
tommy16 小时前

fuck…

Phil Gara 的头像
Phil Gara1 天前

Amazing!

metamike 的头像
metamike1 天前

Tf is this

Broadstreet 的头像
Broadstreet22 小时前

Excellent

相关视频

MaterialFusion Enhancing Inverse Rendering with Material Diffusion Priors discuss: Recent works in inverse rendering have shown promise in using multi-view images of an object to recover shape, albedo, and materials. However, the recovered components often fail to render accurately under new lighting conditions due to the intrinsic challenge of disentangling albedo and material properties from input images. To address this challenge, we introduce MaterialFusion, an enhanced conventional 3D inverse rendering pipeline that incorporates a 2D prior on texture and material properties. We present StableMaterial, a 2D diffusion model prior that refines multi-lit data to estimate the most likely albedo and material from given input appearances. This model is trained on albedo, material, and relit image data derived from a curated dataset of approximately ~12K artist-designed synthetic Blender objects called BlenderVault. we incorporate this diffusion prior with an inverse rendering framework where we use score distillation sampling (SDS) to guide the optimization of the albedo and materials, improving relighting performance in comparison with previous work. We validate MaterialFusion's relighting performance on 4 datasets of synthetic and real objects under diverse illumination conditions, showing our diffusion-aided approach significantly improves the appearance of reconstructed objects under novel lighting conditions. We intend to publicly release our BlenderVault dataset to support further research in this field.

AK

22,959 次观看 • 2 年前