datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
dna_rendering_processed
DNA-Rendering-Processed Dataset
Project Page | Paper | Code | Model
To enable Diffuman4D model training, we meticulously process the DNA-Rendering dataset by recalibrating camera parameters, optimizing image color correction matrices (CCMs), predicting foreground masks, and estimating human skeletons.
To promote future research in the field of human-centric 3D/4D generation, we have open-sourced our re-annotated labels for the DNA-Rendering dataset in this repo, which includes… See the full description on the dataset page: https://huggingface.co/datasets/krahets/dna_rendering_processed.objaverse_rendering_setAIM2024-SparseNeuralRendering
Dataset
This repository cofntains SpaRe (Sparse Rendering) dataset.
Associated paper
The dataset contained in this repository is published as a part of AIM workshop at ECCV 2024.
Differentiable_rendering_tactile_datakorean-text-rendering-data
한글 텍스트 렌더링 학습 데이터
이미지 안에 정확한 한글 텍스트를 렌더링하는 능력 개선을 위해 만들어진 합성(synthetic) 이미지-프롬프트 데이터셋입니다. 2026년 5월~7월에 걸쳐 진행된 세 차례의 별도 학습 이터레이션에서 나온 데이터를 통합했습니다.
총 79,460장, 2개 config(콘텐츠 유형)로 구성. 각 config는 독립적으로 로드할 수 있습니다.
from datasets import load_dataset
ds = load_dataset("<repo_id>", name="diagram") # 유형별로 필요한 것만
이 릴리즈는 순수 한글 타이포그래피 학습에 초점을 맞춰 atomic_text(99.4% 한글)와
diagram(100% 한글) 두 유형만 포함합니다. 둘 다 코드·템플릿 기반 결정론적 생성이라
외부 생성형 서비스에 의존하지 않고, 라이선스 문제가 없습니다. "프롬프트 안 인용부호=정답
텍스트" 컨벤션은 둘 다… See the full description on the dataset page: https://huggingface.co/datasets/fasoo/korean-text-rendering-data.ShapeNet_Renderingtext_rendering
text_rendering
text_rendering
Trigger token: sks_textrender
Examples: 111
Format: p-image
Source: /Users/davidberenstein/Documents/programming/pruna/dataset-generator/training/text-rendering.zip
Use input.zip with p-image-trainer (Replicate). See TRAINING_PLAN.md in this directory.
Format
Trainer: p-image-trainer
Schema: See config.yml and TRAINING_PLAN.md in this repo.
Reproduce
generate.py in this repo documents how to regenerate this dataset… See the full description on the dataset page: https://huggingface.co/datasets/davidberenstein1957/text_rendering.objaverse-1.0-renderingstest_renderinggso_rendering
eval_data_1: elevation 30 degrees
eval_data_2: elevation -10, 0, 10, 20, 30, 40 degrees
Downalod link: https://app.gazebosim.org/GoogleResearch/fuel/collections/Scanned%20Objects%20by%20Google%20Research
Rendering resolution : 256x256
w/o background (last channel) : you can fill this while loading the images
display-inverse-rendering-dataset
Display Inverse Rendering Dataset
📄 Paper (ArXiv)
🌐 Project Page
💻 GitHub Repository
Introduction
This dataset is created for display inverse rendering, including multi-light stereo images captured by polarization cameras, and GT geometry (pixel-aligned point cloud and surface normals) scanned by high-precision 3D scanner.
Structure
DIR-basic: The basic version of the dataset released with the paper. It includes stereo polarized RAW images, RGB images… See the full description on the dataset page: https://huggingface.co/datasets/jinrongtong/display-inverse-rendering-dataset.qwen-image-text-renderingrendering_data