CIPS-3D
pytorch-CycleGAN-and-pix2pix
| CIPS-3D | pytorch-CycleGAN-and-pix2pix | |
|---|---|---|
| 2 | 10 | |
| 606 | 22,029 | |
| - | - | |
| 2.7 | 2.5 | |
| 7 months ago | 3 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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CIPS-3D
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[R] AI Researchers From Huawei and Shanghai Jiao Tong University Introduce ‘CIPS-3D’: A 3D-Aware Generator of GANs
Code for https://arxiv.org/abs/2110.09788 found: https://github.com/PeterouZh/CIPS-3D
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AI Researchers From Huawei and Shanghai Jiao Tong University Introduce ‘CIPS-3D’: A 3D-Aware Generator of GANs
Researchers at Huawei and Shanghai Jiao Tong University have developed CIPS-3D, an approach that synthesizes each pixel value independently, just as its 2D version did.
pytorch-CycleGAN-and-pix2pix
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List of AI-Models
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- I want an A.I. to learn my art style so I can keep making art in my art style despite not having the time to do it.
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I'm looking for an AI Art generator from images
pix2pix (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) - This is a PyTorch implementation of the pix2pix algorithm for image-to-image translation. Given a set of images, the model can learn to generate a new image from a different domain that is similar to the input image.
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Seamless textures with SD and PBR maps with a pix2pix cGAN
Using junyanz/pytorch-CycleGAN-and-pix2pix as a basis for pix2pix, I applied the same blending method to fix seams. It essentially takes an input image and generates an output. The results depend on the paired training data. In this case, each map (height, roughness, etc.) is a separate checkpoint and had to be trained on paired training data with the diffuse as the input and the respective map as the output.
- IA art
- Segmentation and clasification with UNET
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Trying to understand PatchGAN discriminator
Code for https://arxiv.org/abs/1611.07004 found: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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I made a 3d topographic map based on my recent civ6 game
pix2pix algorithm is used for translating Civ6Maps to heightmaps. Synthesized terrain was rendered in blender.
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This Wojak Does Not Exist
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
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Training a neural net to generate Wojaks
I'm working on creating a face-to-wojak model using PyTorch CycleGan/Pix2Pix [0] and found some of my outputs to be outrageous yet somehow relatable. People are into it so thought I'd share on HN
[0] https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
What are some alternatives?
pi-GAN-pytorch - Implementation of π-GAN, for 3d-aware image synthesis, in Pytorch
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs
AvatarCLIP - [SIGGRAPH 2022 Journal Track] AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
nerfstudio - A collaboration friendly studio for NeRFs
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
kaolin-wisp - NVIDIA Kaolin Wisp is a PyTorch library powered by NVIDIA Kaolin Core to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD).
Deep-Fakes
giraffe - This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
Bringing-Old-Photos-Back-to-Life - Bringing Old Photo Back to Life (CVPR 2020 oral)
PaddleGAN - PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.