docs/Overview/intro.md

# Introduction

As a video generation correspondence of ImagenHub, VideoGenHub is a centralized framework to standardize the evaluation of conditional video generation models by curating unified datasets, building an inference library and a benchmark that align with real life applications. This is continuous effort to publish leaderboard to help everyone track the progress in the field.

## Why VideoGenHub?


### What sets #VideoGenHub apart?
1) Unified Datasets: We’ve meticulously curated evaluation datasets for 2 video generation tasks. This ensures comprehensive testing of models across diverse scenarios.
2) Inference Library: Say goodbye to inconsistent comparisons. Our unified inference pipeline ensures that every model is evaluated on a level playing field with full transparency.
3) Human-centric Evaluation: Beyond traditional metrics, we’ve innovated with human evaluation scores that measure Semantic Consistency & Perceptual Quality. This aligns evaluations closer to human perceptions, while better than existing Human-preference evaluation methods.

### Why should you use #VideoGenHub?
1) Streamlined Research: We’ve taken the guesswork out of research by defining clear tasks and providing curated datasets.
Objective Evaluation: Our framework ensures a bias-free, standardized evaluation, giving a true measure of a model’s capabilities.
2) Experiment Transparency: By standardizing the human-evaluation dataset, human evaluation results would be come far more convincing with the experiment transparency.
3) Collaborative Spirit: We believe in the power of community. Our platform is designed to foster collaboration, idea exchange, and innovation in the realm of image generation.
4) Comprehensive Functionality: From common GenAI metrics to visualization tools, we’ve got you covered. Also stay tuned for our upcoming Amazon Mechanical Turk Templates!
5) Engineering Excellence: We emphasize good engineering practice. Documentations, type hints, and (coming soon!) extensive code coverage.