AI bringing old photos to life: User Attitude Differentiation and Driving Mechanisms in Image-to-Video Generation Technology in Diverse Situations

Abstract

This study examines user attitude differentiation toward Image-to-Video Generation Technology across various situations, focusing on the proportions of attitudes and their formation. Using the Theory of Institutional Contextualism, we analyze how contextual fit shapes responses in three scenarios: heroes, elders, and ancient paintings. Through content analysis for attitude assessment and text analysis via word frequency, we identify sentiment patterns. Results show most users hold positive attitudes, with the heroes’ scenario eliciting the strongest positivity, likely due to national pride. The elders’ scenario exhibits significant negative sentiment, reflecting the emotional complexities of personal memory animation. The ancient paintings’ scenario, tied to cultural values, shows balanced attitudes. These findings emphasize the role of contextual alignment in user acceptance, informing the design of context-sensitive AI content.