Stars
This repo contains the official implementation of Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection (ICML'23).
Single Image to 3D using Cross-Domain Diffusion for 3D Generation
🪐 Objaverse-XL is a Universe of 10M+ 3D Objects. Contains API Scripts for Downloading and Processing!
Code Release for "Minimum Class Confusion for Versatile Domain Adaptation"(ECCV2020)
Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.
Official PyTorch implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
A unified framework for 3D content generation.
This repository contains the official implementation of the research paper, "FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization" ICCV 2023
Official PyTorch Implementation of paper "Vision Transformer for NeRF-Based View Synthesis from a Single Input Image", WACV 2023.
[ICCV 2023] Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
Transformer related optimization, including BERT, GPT
PyTorch implementation of the ICCV paper "3D-aware Image Generation using 2D Diffusion Models"
Official implementation of "MeshDiffusion: Score-based Generative 3D Mesh Modeling" (ICLR 2023 Spotlight)
Using Low-rank adaptation to quickly fine-tune diffusion models.
Multi-class confusion matrix library in Python
[ICML'23] StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
🔥Highlighting the top ML papers every week.
Instant neural graphics primitives: lightning fast NeRF and more
Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".
A curated list of awesome neural radiance fields papers
A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF
[ICCV 2021] PixelSynth: Generating a 3D-Consistent Experience from a Single Image
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO