Objective Quality Assessment of AI-Generated Content Videos With Transformation Consistency Focus

Unnatural motion artifacts—such as implausible object dynamics or discontinuous scene transitions—characterize a critical challenge in AI-generated content (AIGC) videos. Assessing these temporal inconsistencies is essential for benchmarking the performance of text-to-video (T2V) models. Current assessment methods derive motion features from either action recognition models or optical flow estimators. However, these motion features cannot faithfully reflect the human-aligned interpretability of how objects or scenes transform between frames. For instance, a model might detect a “running” action but fail to penalize implausible leg movements. To address this gap, we propose Transformation Consistency-based Video Quality Assessment (TCVQA), a novel framework that quantifies transformation consistency by measuring the recognizability of semantic transformations across frames. The core module of TCVQA is the TC-branch, which includes three core components: A Feature Extractor to capture high-level, fine-grained, and low-level motion features. A Flow-Driven Transformation module Warps extracted features from the source frame to the target frame using predicted optical flow. A Differential Perceiver computes discrepancies between warped source features and actual target features, yielding a consistency score that reflects deviations from natural motion patterns. Besides, the TCVQA also integrates three other branches, the TV-branch, the V-branch, and the F-branch, to perceive multiple aspects of distortions. Extensive experiments on AIGC-VQA benchmarks—including T2VQA-DB, LGVQ, FETV, and MQT demonstrate TCVQA’s superiority, achieving consistent improvement in correlation with human judgments over state-of-the-art methods. Our work establishes transformation consistency as a pivotal axis, enabling more reliable evaluation of AIGC video quality assessment.