AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM

• URL: http://arxiv.org/abs/2411.17221v1
• Date: Tue, 26 Nov 2024 08:43:15 GMT
• Title: AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM
• Authors: Jiarui Wang, Huiyu Duan, Guangtao Zhai, Juntong Wang, Xiongkuo Min,
• Abstract summary: We first present AIGVQA-DB, a large-scale dataset comprising 36,576 AIGVs generated by 15 advanced text-to-video models using 1,048 prompts. We then introduce AIGV-Assessor, a novel VQA model that leverages intricate quality attributes to capture precise video quality scores and pair video preferences.
• Score: 54.44479359918971
• License:
• Abstract: The rapid advancement of large multimodal models (LMMs) has led to the rapid expansion of artificial intelligence generated videos (AIGVs), which highlights the pressing need for effective video quality assessment (VQA) models designed specifically for AIGVs. Current VQA models generally fall short in accurately assessing the perceptual quality of AIGVs due to the presence of unique distortions, such as unrealistic objects, unnatural movements, or inconsistent visual elements. To address this challenge, we first present AIGVQA-DB, a large-scale dataset comprising 36,576 AIGVs generated by 15 advanced text-to-video models using 1,048 diverse prompts. With these AIGVs, a systematic annotation pipeline including scoring and ranking processes is devised, which collects 370k expert ratings to date. Based on AIGVQA-DB, we further introduce AIGV-Assessor, a novel VQA model that leverages spatiotemporal features and LMM frameworks to capture the intricate quality attributes of AIGVs, thereby accurately predicting precise video quality scores and video pair preferences. Through comprehensive experiments on both AIGVQA-DB and existing AIGV databases, AIGV-Assessor demonstrates state-of-the-art performance, significantly surpassing existing scoring or evaluation methods in terms of multiple perceptual quality dimensions.

Related papers

• LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models [53.64461404882853]
  Video quality assessment (VQA) algorithms are needed to monitor and optimize the quality of streaming videos. Here, we propose the first Large Multi-Modal Video Quality Assessment (LMM-VQA) model, which introduces a novel visual modeling strategy for quality-aware feature extraction.
  arXiv  Detail & Related papers  (2024-08-26T04:29:52Z)
• Benchmarking AIGC Video Quality Assessment: A Dataset and Unified Model [54.69882562863726]
  We try to systemically investigate the AIGC-VQA problem from both subjective and objective quality assessment perspectives. We evaluate the perceptual quality of AIGC videos from three dimensions: spatial quality, temporal quality, and text-to-video alignment. We propose a Unify Generated Video Quality assessment (UGVQ) model to comprehensively and accurately evaluate the quality of AIGC videos.
  arXiv  Detail & Related papers  (2024-07-31T07:54:26Z)
• GAIA: Rethinking Action Quality Assessment for AI-Generated Videos [56.047773400426486]
  Action quality assessment (AQA) algorithms predominantly focus on actions from real specific scenarios and are pre-trained with normative action features. We construct GAIA, a Generic AI-generated Action dataset, by conducting a large-scale subjective evaluation from a novel causal reasoning-based perspective. Results show that traditional AQA methods, action-related metrics in recent T2V benchmarks, and mainstream video quality methods perform poorly with an average SRCC of 0.454, 0.191, and 0.519, respectively.
  arXiv  Detail & Related papers  (2024-06-10T08:18:07Z)
• Enhancing Blind Video Quality Assessment with Rich Quality-aware Features [79.18772373737724]
  We present a simple but effective method to enhance blind video quality assessment (BVQA) models for social media videos. We explore rich quality-aware features from pre-trained blind image quality assessment (BIQA) and BVQA models as auxiliary features. Experimental results demonstrate that the proposed model achieves the best performance on three public social media VQA datasets.
  arXiv  Detail & Related papers  (2024-05-14T16:32:11Z)
• Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment [25.5501280406614]
  Video quality assessment (VQA) has attracted growing attention in recent years. The great expense of annotating large-scale VQA datasets has become the main obstacle for current deep-learning methods. An Adaptive Diverse Quality-aware feature Acquisition (Ada-DQA) framework is proposed to capture desired quality-related features.
  arXiv  Detail & Related papers  (2023-08-01T16:04:42Z)
• Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models [71.06007696593704]
  Blind quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. We conduct a first-of-its-kind computational analysis of VQA datasets via minimalistic BVQA models.
  arXiv  Detail & Related papers  (2023-07-26T06:38:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.