Comprehensive Assessment of AI-Generated Video Quality: Evaluating Visual Harmony, Video-Text Consistency, and Domain Distribution Gaps

แนวคิดหลัก

This work proposes a comprehensive framework for assessing the quality of AI-generated videos, focusing on three key dimensions: visual harmony, video-text consistency, and domain distribution gaps among different generative models.

บทคัดย่อ

The paper presents a novel framework for assessing the quality of AI-generated videos (AIGC videos). It identifies three key dimensions for AIGC video quality assessment:

Visual Harmony: This dimension evaluates the aesthetic and technical aspects of the generated videos, building upon the DOVER method.

Video-Text Consistency: To capture the inherent multimodal nature of AIGC videos, the framework incorporates explicit prompt injection, implicit text guidance, and video-text caption similarity. These modules enable a more comprehensive evaluation of the alignment between the video content and the corresponding textual prompts.

Domain Distribution Gap: Recognizing that videos generated by different text-to-video models can exhibit distinct visual quality, fluency, and style, the authors introduce an auxiliary inter-domain classification task. This helps the model better understand the discriminative features of AIGC videos and enhances the overall quality assessment performance.

The proposed method was used in the third-place winner of the NTIRE 2024 Quality Assessment for AI-Generated Content - Track 2 Video challenge, demonstrating its effectiveness. Ablation studies further validate the contributions of the individual components, highlighting the importance of the multimodal and domain-aware approaches for AIGC video quality assessment.

สถิติ

"The dataset comprises 7000 videos in the training set, 2000 videos in the validation set, and 1000 videos in the test set, all with corresponding textual prompts."
"All videos have a duration of 4 seconds and a frame rate of either 3.75 or 4 frames per second."

คำพูด

"Predicting the specific generative model behind AIGC videos can lead to the extraction of more discriminative features. This capability significantly aids in the enhanced assessment of AIGC video quality."
"Our method was used in the third-place winner of the NTIRE 2024 Quality Assessment for AI-Generated Content - Track 2 Video, demonstrating its effectiveness."

ข้อมูลเชิงลึกที่สำคัญจาก

Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap

by Bowen Qu,Xia... ที่ arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13573.pdf

สอบถามเพิ่มเติม

How can the proposed framework be extended to handle AIGC videos generated by emerging text-to-video models in the future

The proposed framework for AIGC video quality assessment can be extended to handle videos generated by emerging text-to-video models by incorporating adaptability mechanisms. As new text-to-video models are developed, the framework can include a module for model adaptation. This module would involve retraining or fine-tuning the existing assessment models to align with the characteristics and nuances of the new generative models. Additionally, the framework can implement a continuous learning approach where it dynamically updates its evaluation criteria based on the performance of the latest text-to-video models. By staying updated with the advancements in the field, the framework can ensure accurate and relevant quality assessment for AIGC videos generated by emerging models.

What are the potential limitations of the current approach, and how can it be further improved to address more complex challenges in AIGC video quality assessment

One potential limitation of the current approach is its reliance on specific datasets or pretrained models for metrics like Inception Score (IS) and Fréchet Video Distance (FVD). To address this, the framework can be improved by incorporating more generalized and adaptable evaluation metrics that are not dataset-specific. By developing metrics that are more robust and versatile, the framework can provide a more comprehensive assessment of AIGC video quality across different datasets and generative models. Furthermore, enhancing the model ensemble techniques and incorporating more diverse training data can help in addressing complex challenges such as visual inconsistencies and domain distribution gaps in AIGC videos. Additionally, exploring novel approaches for video-text consistency assessment, beyond explicit and implicit text guidance, can further enhance the framework's capabilities.

Given the rapid advancements in text-to-video generation, how might the distribution of AIGC video quality evolve over time, and how can the assessment framework adapt to these changes

With the rapid advancements in text-to-video generation, the distribution of AIGC video quality is likely to evolve over time. As newer models are introduced, the quality of generated videos may exhibit variations in terms of visual fidelity, style coherence, and temporal fluency. To adapt to these changes, the assessment framework can implement a continuous monitoring system that tracks the performance of different text-to-video models. By regularly updating the evaluation criteria based on the characteristics of the latest models, the framework can ensure that it remains effective in assessing the quality of AIGC videos. Additionally, incorporating a feedback loop mechanism where the framework learns from its assessments and adjusts its evaluation strategies can help in accommodating the evolving distribution of AIGC video quality. This adaptive approach will enable the framework to stay relevant and accurate in evaluating videos generated by emerging text-to-video models.