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top-cvpr-2023-papers

(★ 649)

This repository is a curated collection of the most exciting and influential CVPR 2023 papers. 🔥 [Paper + Code]

파일 탐색기

최종 버전 다운로드 (.zip)

- data.csv
- generate.py
- README.md
- requirements.txt
.gitignore
LICENSE
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/SkalskiP/top-cvpr-2023-papers

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd top-cvpr-2023-papers

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r automation/requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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![visitor badge](https://visitor-badge.laobi.icu/badge?page_id=SkalskiP.top-cvpr-2023-papers)

<div align="center">
  <h1 align="center">top CVPR 2023 papers</h1>
  <a href="https://github.com/SkalskiP/top-cvpr-2023-papers">2023</a> | <a href="https://github.com/SkalskiP/top-cvpr-2024-papers">2024</a> | <a href="https://github.com/SkalskiP/top-cvpr-2025-papers">2025</a> | <a href="https://github.com/SkalskiP/top-cvpr-2026-papers">2026</a>
</div>

<p align="center">
    </br>
    <img width="600" src="https://github.com/SkalskiP/top-cvpr-2023-papers/assets/26109316/2d7be39e-11a0-4298-ad90-c0645af0c5ac" alt="vancouver">
    </br>
</p>

## 👋 hello

Computer Vision and Pattern Recognition is a massive conference. In **2023** alone, **9,155** papers were submitted, and **2,359** were accepted. I created this repository to help you search for crème de la crème of CVPR publications. If the paper you are looking for is not on my short list, take a peek at the full [list](https://cvpr.thecvf.com/Conferences/2023/AcceptedPapers) of accepted papers.

## 🗞️ papers

| **topic** | **title** | **repository / paper** |
|:---------:|:---------:|:----------------------:|
| Segmentation | OneFormer: One Transformer To Rule Universal Image Segmentation |  [![GitHub](https://img.shields.io/github/stars/SHI-Labs/OneFormer?style=social)](https://github.com/SHI-Labs/OneFormer) [![arXiv](https://img.shields.io/badge/arXiv-2211.06220-b31b1b.svg)](https://arxiv.org/abs/2211.06220)|
| Segmentation | X-Decoder: Generalized Decoding for Pixel, Image and Language |  [![GitHub](https://img.shields.io/github/stars/microsoft/X-Decoder?style=social)](https://github.com/microsoft/X-Decoder) [![arXiv](https://img.shields.io/badge/arXiv-2212.11270-b31b1b.svg)](https://arxiv.org/abs/2212.11270)|
| Segmentation and Generative AI | Images Speak in Images: A Generalist Painter for In-Context Visual Learning |  [![GitHub](https://img.shields.io/github/stars/baaivision/Painter?style=social)](https://github.com/baaivision/Painter) [![arXiv](https://img.shields.io/badge/arXiv-2212.02499-b31b1b.svg)](https://arxiv.org/abs/2212.02499)|
| Segmentation | PACO: Parts and Attributes of Common Objects |  [![GitHub](https://img.shields.io/github/stars/facebookresearch/paco?style=social)](https://github.com/facebookresearch/paco) [![arXiv](https://img.shields.io/badge/arXiv-2301.01795-b31b1b.svg)](https://arxiv.org/abs/2301.01795)|
| Segmentation | Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP |  [![GitHub](https://img.shields.io/github/stars/facebookresearch/ov-seg?style=social)](https://github.com/facebookresearch/ov-seg) [![arXiv](https://img.shields.io/badge/arXiv-2210.04150-b31b1b.svg)](https://arxiv.org/abs/2210.04150)|
| NeRF | DynIBaR: Neural Dynamic Image-Based Rendering |  [![GitHub](https://img.shields.io/github/stars/google/dynibar?style=social)](https://github.com/google/dynibar) [![arXiv](https://img.shields.io/badge/arXiv-2211.11082-b31b1b.svg)](https://arxiv.org/abs/2211.11082)|
| 3D | Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition |  [![GitHub](https://img.shields.io/github/stars/MoyGcc/vid2avatar?style=social)](https://github.com/MoyGcc/vid2avatar) [![arXiv](https://img.shields.io/badge/arXiv-2302.11566-b31b1b.svg)](https://arxiv.org/abs/2302.11566)|
| Generative AI | 3D-aware Conditional Image Synthesis |  [![GitHub](https://img.shields.io/github/stars/dunbar12138/pix2pix3d?style=social)](https://github.com/dunbar12138/pix2pix3d) [![arXiv](https://img.shields.io/badge/arXiv-2302.08509-b31b1b.svg)](https://arxiv.org/abs/2302.08509)|
| 3D | 3D Human Mesh Estimation from Virtual Markers |  [![GitHub](https://img.shields.io/github/stars/ShirleyMaxx/VirtualMarker?style=social)](https://github.com/ShirleyMaxx/VirtualMarker) [![arXiv](https://img.shields.io/badge/arXiv-2303.11726-b31b1b.svg)](https://arxiv.org/abs/2303.11726)|
| Transfer Learning | A Data-Based Perspective on Transfer Learning |  [![GitHub](https://img.shields.io/github/stars/MadryLab/data-transfer?style=social)](https://github.com/MadryLab/data-transfer) [![arXiv](https://img.shields.io/badge/arXiv-2207.05739-b31b1b.svg)](https://arxiv.org/abs/2207.05739)|
| Segmentation | Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models |  [![GitHub](https://img.shields.io/github/stars/NVlabs/ODISE?style=social)](https://github.com/NVlabs/ODISE) [![arXiv](https://img.shields.io/badge/arXiv-2303.04803-b31b1b.svg)](https://arxiv.org/abs/2303.04803)|
| Generative AI | DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation |  [![GitHub](https://img.shields.io/github/stars/google/dreambooth?style=social)](https://github.com/google/dreambooth) [![arXiv](https://img.shields.io/badge/arXiv-2208.12242-b31b1b.svg)](https://arxiv.org/abs/2208.12242)|
| Generative AI | InstructPix2Pix: Learning to Follow Image Editing Instructions |  [![GitHub](https://img.shields.io/github/stars/timothybrooks/instruct-pix2pix?style=social)](https://github.com/timothybrooks/instruct-pix2pix) [![arXiv](https://img.shields.io/badge/arXiv-2211.09800-b31b1b.svg)](https://arxiv.org/abs/2211.09800)|
| Generative AI | High-resolution image reconstruction with latent diffusion models from human brain activity |  [![GitHub](https://img.shields.io/github/stars/yu-takagi/StableDiffusionReconstruction?style=social)](https://github.com/yu-takagi/StableDiffusionReconstruction) [![arXiv](https://img.shields.io/badge/arXiv-2306.11536-b31b1b.svg)](https://arxiv.org/abs/2306.11536)|
| Benchmarking | Beyond mAP: Towards better evaluation of instance segmentation |  [![GitHub](https://img.shields.io/github/stars/rohitrango/beyond-map?style=social)](https://github.com/rohitrango/beyond-map) [![arXiv](https://img.shields.io/badge/arXiv-2207.01614-b31b1b.svg)](https://arxiv.org/abs/2207.01614)|
| NeRF | SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields |  [![GitHub](https://img.shields.io/github/stars/SamsungLabs/SPIn-NeRF?style=social)](https://github.com/SamsungLabs/SPIn-NeRF) [![arXiv](https://img.shields.io/badge/arXiv-2211.12254-b31b1b.svg)](https://arxiv.org/abs/2211.12254)|
| 3D | Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild |  [![GitHub](https://img.shields.io/github/stars/facebookresearch/omni3d?style=social)](https://github.com/facebookresearch/omni3d) [![arXiv](https://img.shields.io/badge/arXiv-2207.10660-b31b1b.svg)](https://arxiv.org/abs/2207.10660)|
| 3D | ECON: Explicit Clothed humans Optimized via Normal integration |  [![GitHub](https://img.shields.io/github/stars/YuliangXiu/ECON?style=social)](https://github.com/YuliangXiu/ECON) [![arXiv](https://img.shields.io/badge/arXiv-2212.07422-b31b1b.svg)](https://arxiv.org/abs/2212.07422)|
| 3D | NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views |  [![GitHub](https://img.shields.io/github/stars/VITA-Group/NeuralLift-360?style=social)](https://github.com/VITA-Group/NeuralLift-360) [![arXiv](https://img.shields.io/badge/arXiv-2211.16431-b31b1b.svg)](https://arxiv.org/abs/2211.16431)|

## 🦸 contribution

We would love your help in making this repository even better! If you know of an amazing paper that isn't listed
here, or if you have any suggestions for improvement, feel free to open an
[issue](https://github.com/SkalskiP/top-cvpr-2023-papers/issues) or submit a
[pull request](https://github.com/SkalskiP/top-cvpr-2023-papers/pulls).