datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
3D-PC
Summary
Visual perspective taking (VPT), the ability to accurately perceive and reason about the perspectives of others, is an essential feature of human intelligence.
Deep neural networks (DNNs) may be a good candidate for modeling VPT and its computational demands in light of a growing number of reports indicating that DNNs gain the ability to analyze 3D scenes after training on large static-image datasets.
We developed the 3D perception challenge (3D-PC) for comparing 3D… See the full description on the dataset page: https://huggingface.co/datasets/3D-PC/3D-PC.synthetic-human-expressions-poses-3d
3D Synthetic Human Poses and FACS Expressions Dataset
This is a high-fidelity synthetic dataset consisting of 10,075 pairs of 3D human character renders and detailed natural language annotations.
Dataset Structure & Generation
To ensure consistency, the dataset is generated using a single base 3D human model. The diversity of the dataset is achieved through a wide range of body poses, facial expressions, and camera angles:
Character: 1 base human model.
Camera… See the full description on the dataset page: https://huggingface.co/datasets/nadizik/synthetic-human-expressions-poses-3d.3D-DefectBench
3D-DefectBench
A controlled benchmark for evaluating vision-language models (VLMs) as fine-grained judges of
defects in text-to-3D generation.
3D-DefectBench is a VLM-as-a-judge benchmark for detecting fine-grained defects in textured 3D
meshes. It lets you measure how well any VLM judge aligns with human judgment: run your judge over the
assets and score its predictions against the human defect labels provided here.
Each example pairs a text prompt with a generated, textured 3D… See the full description on the dataset page: https://huggingface.co/datasets/zzhao0500/3D-DefectBench.DB-3DME
DB-3DME: From Dataset to Benchmark for Human-aligned Automatic 3D Mesh Evaluation
DB-3DME is a benchmark dataset for evaluating 3D mesh generation, featuring human annotations for Geometry and Prompt Adherence, along with corresponding text prompts and GIF visualizations of generated 3D assets. It is intended to facilitate research on reliable evaluation protocols for modern 3D generative models.
Dataset Description
Each entry in the dataset links a text prompt to a… See the full description on the dataset page: https://huggingface.co/datasets/nsjia/DB-3DME.