datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Video_Reality_Test
VideoASMR-Bench: Can AI-Generated ASMR Videos Fool VLMs and Humans?
This repository serves as a benchmark for evaluating the realism of video generation models. It specifically focuses on ASMR content, which requires high fidelity in texture rendering, micro-movements, and audio-visual synchronization.
Benchmark Structure
This benchmark is divided into two difficulty levels. All data is provided in the test split to reflect its purpose for evaluation:
real_hard: 100 samples.… See the full description on the dataset page: https://huggingface.co/datasets/kolerk/Video_Reality_Test.Video_Reality_Test
Video Reality Test: Can AI-Generated ASMR Videos fool VLMs and Humans?
This repository serves as a benchmark for evaluating the realism of video generation models. It specifically focuses on ASMR content, which requires high fidelity in texture rendering, micro-movements, and audio-visual synchronization.
Benchmark Structure
This benchmark is divided into two difficulty levels. All data is provided in the test split to reflect its purpose for evaluation:… See the full description on the dataset page: https://huggingface.co/datasets/ziweix/Video_Reality_Test.Video_Reality_Test
Video Reality Test: Can AI-Generated ASMR Videos fool VLMs and Humans?
This repository serves as a benchmark for evaluating the realism of video generation models. It specifically focuses on ASMR content, which requires high fidelity in texture rendering, micro-movements, and audio-visual synchronization.
Benchmark Structure
This benchmark is divided into two difficulty levels. All data is provided in the test split to reflect its purpose for evaluation:… See the full description on the dataset page: https://huggingface.co/datasets/Dii2/Video_Reality_Test.Video_Reality_Test
VideoASMR-Bench: Can AI-Generated ASMR Videos Fool VLMs and Humans?
This repository serves as a benchmark for evaluating the realism of video generation models. It specifically focuses on ASMR content, which requires high fidelity in texture rendering, micro-movements, and audio-visual synchronization.
Benchmark Structure
This benchmark is divided into two difficulty levels. All data is provided in the test split to reflect its purpose for evaluation:
real_hard: 100 samples.… See the full description on the dataset page: https://huggingface.co/datasets/videoasmrbench/Video_Reality_Test.