SPADE
awesome-NeRF
SPADE | awesome-NeRF | |
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11 | 18 | |
7,533 | 6,238 | |
0.1% | 0.9% | |
0.0 | 6.9 | |
9 months ago | 24 days ago | |
Python | TeX | |
GNU General Public License v3.0 or later | MIT License |
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SPADE
- T2i Segmentation Colors Reference - Work in progress v18
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I'm looking for an AI Art generator from images
GauGAN (https://github.com/NVlabs/SPADE) - This is a PyTorch implementation of the SPADE (SPatially-Adaptive (DE)normalization) algorithm, which can generate images from segmentation maps. You can use it to generate realistic images of objects, landscapes, and other scenes.
- MegaPortraits: High-Res Deepfakes Created From a Single Photo
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Can NVIDIA Canvas be used as an API?
It is open source, if that helps. Here is a GitHub link.
- Care sunt meseriile/profesiile care, in opinia voastra, nu vor fi inlocuite de AI in urmatorii 35-50 de ani si de ce?
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Where/How should I start?
Generating photorealistic landscapes from brush strokes: Semantic Image Synthesis with Spatially-Adaptive Normalization
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Blursed rock formation
This look like an AI image synthesized with Nvidia's SPADE.
- Nvidia Canvas
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Gaugan Nvidia Unity
Spade Nvidia
awesome-NeRF
- Recommendation for a convenient NERF model to try out? And discussions...
- This is not drone footage or an iPhone video but An AI model made this. Google researchers created this 3D scene and walkthrough using just 2D images. This is called a NeRF (Anti-Aliased Grid-Based Neural Radiance Fields), where AI models can take 2D pictures and create 3D scenes.
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New AI Tools for iPhones: Motion Capture and Environment Scanning - But What About Android Users?
And plenty others from outside Google Research. However, I wasn't aware there was a whole product making the creation of them trivial already. It's great to see honestly as I was hoping this would make the leap from research to products given how useful it is.
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Is Nerf better than COLMAP for object reconstruction?
You can extract a mesh from a NeRF by using the marching cubes algorithm. But you'll have to texture it as well, and only after that can you be real time. NeRF training or inference isn't fast in the vanilla version. I suggest you browse https://github.com/awesome-NeRF/awesome-NeRF and look for the fast versions, this one is very fast, if you're ok with special CUDA kernels: https://nvlabs.github.io/instant-ngp/
- I volunteered to help out with the Awesome NeRF list - help me bring it up to date.
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Nerf meshes are crap and thats normal right ?
There are implementations of NeRF that are solely based on the aim of a good export, and the work is going fast. I'm mobile right now but think this is a good place to watch, if my bookmarks are correct: https://github.com/yenchenlin/awesome-NeRF
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Nvidia NeRF
https://github.com/yenchenlin/awesome-NeRF watch and learn from this page!
- A curated list of NeRF papers & other resources
What are some alternatives?
gaugan - Photorealistic landscape drawings using the Nvidia SPADE model
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
WaveFunctionCollapse - Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics
colmap - COLMAP - Structure-from-Motion and Multi-View Stereo
Parsec-Cloud-Preparation-Tool - Launch Parsec enabled cloud computers via your own cloud provider account.
nerf - Code release for NeRF (Neural Radiance Fields)
sketch-to-art - 🖼 Create artwork from your casual sketch with GAN and style transfer
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
svox2 - Plenoxels: Radiance Fields without Neural Networks
Parsec-Cloud-Preparation-Too
awesome-visual-slam - :books: The list of vision-based SLAM / Visual Odometry open source, blogs, and papers