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_setobjaverse-1.0-renderingsdisplay-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/SeokjunChoi/display-inverse-rendering-dataset.Diffusion-Reward-Modeling-for-Text-Rendering-Dataset
🖼️ Text-to-Image Rendering Dataset
A dataset of 14k text prompts for image generation with text rendering evaluation
📚 Dataset Overview
This dataset contains 14,000 text prompts specifically designed for:
Image generation with text rendering
Evaluating text preservation in generated images
Training diffusion models for better text rendering
Each prompt comes with:
Pre-extracted target text for rendering
5 Stable Diffusion 3 generated latents (70k total)
Dual… See the full description on the dataset page: https://huggingface.co/datasets/leffff/Diffusion-Reward-Modeling-for-Text-Rendering-Dataset.AIM2024-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.
ShapeNet_Renderingabc-look3d-renderingskorean-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.text_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.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.NeLiF-Neural-Lighting-Function-Generation-for-Indoor-RenderingVoluspa_the_Seeresss_Vision_the_Ultimate_Poetic_Rendering
Völuspá the Seeress's Vision the Ultimate Poetic Rendering
Dataset Details
Dataset Description
This dataset weaves the ancient threads of Völuspá, the Seeress's profound vision from the Poetic Edda, into a modern ShareGPT JSONL format. It holds 66 sacred exchanges, each a rune-carved conversation: a mortal seeker requests the recitation of a stanza, and the divine voice responds with the poem's eternal words—from creation's dawn through Ragnarök's… See the full description on the dataset page: https://huggingface.co/datasets/RuneForgeAI/Voluspa_the_Seeresss_Vision_the_Ultimate_Poetic_Rendering.meaningfulness-cross-language-rendering
Cross-Language Rendering for Meaning vs Meaningfulness (Paper B 2026ap)
HF dataset DOI: 10.57967/hf/8971
Companion paper concept DOI: 10.5281/zenodo.20409701
Companion GitHub mirror: https://github.com/spectralbranding/meaningfulness-papers/tree/main/meaning-meaningfulness-empirical
Dataset Summary
This dataset contains the multi-language rendering and extraction artifacts demonstrating Proposition P4 (rendering-equivalence under spine-preservation) from Zharnikov… See the full description on the dataset page: https://huggingface.co/datasets/spectralbranding/meaningfulness-cross-language-rendering.pygame_rendering_test_4This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "koch",
"total_episodes": 2,
"total_frames": 593,
"total_tasks": 1,
"total_videos": 2,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:2"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/jackvial/pygame_rendering_test_4.objaverse_renderingNLR-LightProp-Objaverse-Renderings
LightProp Dataset for Neural LightRig (CVPR 2025)
Overview
This repo contains rendering dataset for our paper Neural LightRig.
More information is on our project page Neural LightRig.
metadata.json contains information on object uids used for training / validation / testing.
Download
Clone the whole repo or download with snapshot_download.
Merge the splits using zip -FF pbr_obj.zip merged.zip.
Unzip with unzip merged.zip.
License
The dataset are… See the full description on the dataset page: https://huggingface.co/datasets/zxhezexin/NLR-LightProp-Objaverse-Renderings.objaverse-multiview-renderings
Multi-view Renderings from Objaverse (RGB, Depth, Normals)
This dataset provides multi-view renderings generated from a subset of Objaverse assets for 3D reconstruction research.
It includes:
Multi-view RGB images
Rendered depth maps
Rendered normal maps
Per-sample file layout
Each object is stored in a directory:
<sample_dir>/
metadata.json
data.mdb
metadata.json stores camera parameters and image resolution.
data.mdb is an LMDB database file containing the… See the full description on the dataset page: https://huggingface.co/datasets/BaowenZ/objaverse-multiview-renderings.gso_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
test_renderingcamus-12-ascii-graph-rendering
We generate ASCII bar, line, pie, and scatter charts in the terminal using plotext.
ASCII Graph Generation via Terminal Rendering Pipeline
The Problem
Terminal AI systems need to communicate quantitative information visually without a GUI. ASCII graphs render in any terminal with no display server dependency.
What We Built
Integration of the plotext library for four chart types via /graphify command. Charts use Unicode block characters (U+2588) and… See the full description on the dataset page: https://huggingface.co/datasets/Anticloud/camus-12-ascii-graph-rendering.pygame_rendering_test_011Differentiable_rendering_tactile_dataNeural_Rendering_Assetpygame_rendering_test_1pygame_rendering_test_2dl3dv_renderingqwen-image-text-renderingopenlrm-renderingscamus-12-ascii-graph-rendering
We generate ASCII bar, line, pie, and scatter charts in the terminal using plotext.
ASCII Graph Generation via Terminal Rendering Pipeline
The Problem
Terminal AI systems need to communicate quantitative information visually without a GUI. ASCII graphs render in any terminal with no display server dependency.
What We Built
Integration of the plotext library for four chart types via /graphify command. Charts use Unicode block characters (U+2588) and… See the full description on the dataset page: https://huggingface.co/datasets/kleinnner/camus-12-ascii-graph-rendering.