VideoScore2 EU AI Act Compliance Profile

📃Paper | 🌐Website | 💻Code | 🛢️Dataset (VideoFeedback2) | 🤗Model (VideoScore2) | 🤗Space (VideoScore2)

🤔Ablation1: SFT-only | 🤔Ablation2: SFT-w/o-CoT | 🤔Ablation3: RL-w/o-SFT

Introduction

We present VideoScore2, a multi-dimensional, interpretable, and human-aligned framework that explicitly evaluates visual quality, text-to-video alignment, and physical/common-sense consistency while producing detailed chain-of-thought rationales.

Our model is trained on a large-scale dataset VideoFeedback2 containing 27,168 human-annotated videos with both scores and reasoning traces across three dimensions, using a two-stage pipeline of supervised fine-tuning followed by reinforcement learning with Group Relative Policy Optimization (GRPO) to enhance analytical robustness.

Extensive experiments demonstrate that VideoScore2 achieves superior performance with 44.35 (+5.94) accuracy on our in-domain benchmark VideoScore-Bench-v2 and 50.37 (+4.32) average performance across four out-of-domain benchmarks (VideoGenReward-Bench, VideoPhy2, etc).

Usage

Inference

For running inference of VideoScore2, firstly install:

Run inference over one video:

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Base Model