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VideoScore

Visual Question Answering
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Safetensors
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idefics2
text-classification
text-generation-inference
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metadata
license: apache-2.0
datasets:
  - TIGER-Lab/VideoFeedback
language:
  - en
metrics:
  - accuracy/spcc
library_name: transformers
pipeline_tag: visual-question-answering

[📃Paper] | 🌐Website | 💻Github | 🛢️Datasets | 🤗Model | 🤗Model-variant | 🤗Demo

MantisScore

Introduction

  • MantisScore is a video quality evaluation model, taking Mantis-8B-Idefics2 as base-model and trained on VideoFeedback, a large video evaluation dataset with multi-aspect human scores.

  • MantisScore can reach 75+ Spearman correlation with humans on VideoEval-test, surpassing all the MLLM-prompting methods and feature-based metrics.

  • MantisScore also beat the best baselines on other three benchmarks EvalCrafter, GenAI-Bench and VBench, showing high alignment with human evaluations.

Evaluation Results

We test our video evaluation model MantisScore on VideoEval-test, EvalCrafter, GenAI-Bench and VBench. For the first two benchmarks, we take Spearman corrleation between model's output and human ratings averaged among all the evaluation aspects as indicator. For GenAI-Bench and VBench, which include human preference data among two or more videos, we employ the model's output to predict preferences and use pairwise accuracy as the performance indicator.

Moreover, we use MantisScore trained on VideoFeedback dataset for VideoFeedback-test set, while for other three benchmarks, we use MantisScore-anno-only variant trained on VideoFeedback dataset with real videos excluded.

The evaluation results are shown below:

metric Final Avg Score VideoFeedback-test EvalCrafter GenAI-Bench VBench
MantisScore (reg) 69.6 75.7 51.1 78.5 73.0
MantisScore (gen) 55.6 77.1 27.6 59.0 58.7
Gemini-1.5-Pro 39.7 22.1 22.9 60.9 52.9
Gemini-1.5-Flash 39.4 20.8 17.3 67.1 52.3
GPT-4o 38.9 23.1 28.7 52.0 51.7
CLIP-sim 31.7 8.9 36.2 34.2 47.4
DINO-sim 30.3 7.5 32.1 38.5 43.3
SSIM-sim 29.5 13.4 26.9 34.1 43.5
CLIP-Score 28.6 -7.2 21.7 45.0 54.9
LLaVA-1.5-7B 27.1 8.5 10.5 49.9 39.4
LLaVA-1.6-7B 23.3 -3.1 13.2 44.5 38.7
X-CLIP-Score 23.2 -1.9 13.3 41.4 40.1
PIQE 19.6 -10.1 -1.2 34.5 55.1
BRISQUE 19.0 -20.3 3.9 38.5 53.7
Idefics2 18.3 6.5 0.3 34.6 31.7
MSE-dyn 10.6 -5.5 -17.0 28.4 36.5
SSIM-dyn 9.2 -12.9 -26.4 31.4 44.5

The best in MantisScore series is in bold and the best in baselines is underlined.

Usage

Installation 

git clone https://github.com/TIGER-AI-Lab/MantisScore.git

Inference 

cd MantisScore/examples

import av
import numpy as np
from typing import List
from PIL import Image
import torch
from transformers import AutoProcessor
from models.idefics2 import Idefics2ForSequenceClassification

def _read_video_pyav(
    frame_paths:List[str], 
    max_frames:int,
):
    frames = []
    container.seek(0)
    start_index = indices[0]
    end_index = indices[-1]
    for i, frame in enumerate(container.decode(video=0)):
        if i > end_index:
            break
        if i >= start_index and i in indices:
            frames.append(frame)
    return np.stack([x.to_ndarray(format="rgb24") for x in frames])

MAX_NUM_FRAMES=16
ROUND_DIGIT=3
REGRESSION_QUERY_PROMPT = """
Suppose you are an expert in judging and evaluating the quality of AI-generated videos,
please watch the following frames of a given video and see the text prompt for generating the video,
then give scores from 5 different dimensions:
(1) visual quality: the quality of the video in terms of clearness, resolution, brightness, and color
(2) temporal consistency, both the consistency of objects or humans and the smoothness of motion or movements
(3) dynamic degree, the degree of dynamic changes
(4) text-to-video alignment, the alignment between the text prompt and the video content
(5) factual consistency, the consistency of the video content with the common-sense and factual knowledge

for each dimension, output a float number from 1.0 to 4.0,
the higher the number is, the better the video performs in that sub-score, 
the lowest 1.0 means Bad, the highest 4.0 means Perfect/Real (the video is like a real video)
Here is an output example:
visual quality: 3.2
temporal consistency: 2.7
dynamic degree: 4.0
text-to-video alignment: 2.3
factual consistency: 1.8

For this video, the text prompt is "{text_prompt}",
all the frames of video are as follows:
"""

model_name="TIGER-Lab/MantisScore"
video_path="video1.mp4"
video_prompt="Near the Elephant Gate village, they approach the haunted house at night. Rajiv feels anxious, but Bhavesh encourages him. As they reach the house, a mysterious sound in the air adds to the suspense."

processor = AutoProcessor.from_pretrained(model_name,torch_dtype=torch.bfloat16)
model = Idefics2ForSequenceClassification.from_pretrained(model_name,torch_dtype=torch.bfloat16).eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# sample uniformly 8 frames from the video
container = av.open(video_path)
total_frames = container.streams.video[0].frames
if total_frames > MAX_NUM_FRAMES:
    indices = np.arange(0, total_frames, total_frames / MAX_NUM_FRAMES).astype(int)
else:
    indices = np.arange(total_frames)

frames = [Image.fromarray(x) for x in _read_video_pyav(container, indices)]
eval_prompt = REGRESSION_QUERY_PROMPT.format(text_prompt=video_prompt)
num_image_token = eval_prompt.count("<image>")
if num_image_token < len(frames):
    eval_prompt += "<image> " * (len(frames) - num_image_token)

flatten_images = []
for x in [frames]:
    if isinstance(x, list):
        flatten_images.extend(x)
    else:
        flatten_images.append(x)
flatten_images = [Image.open(x) if isinstance(x, str) else x for x in flatten_images]
inputs = processor(text=eval_prompt, images=flatten_images, return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits
num_aspects = logits.shape[-1]

aspect_scores = []
for i in range(num_aspects):
    aspect_scores.append(round(logits[0, i].item(),ROUND_DIGIT))
print(aspect_scores)

"""
model output on visual quality, temporal consistency, dynamic degree,
text-to-video alignment, factual consistency, respectively

[2.297, 2.469, 2.906, 2.766, 2.516]
"""

Training

see MantisScore/training for details

Evaluation

see MantisScore/benchmark for details

Citation