We seek to answer the question: what can a motion-blurred image reveal about a scene's past, present, and future? Although motion blur obscures image details and degrades visual quality, it also encodes information about scene and camera motion during an exposure. Previous techniques leverage this information to estimate a sharp image from an input blurry one, or to predict a sequence of video frames showing what might have occurred at the moment of image capture. However, they rely on handcrafted priors or network architectures to resolve ambiguities in this inverse problem, and do not incorporate image and video priors on large-scale datasets. As such, existing methods struggle to reproduce complex scene dynamics and do not attempt to recover what occurred before or after an image was taken. Here, we introduce a new technique that repurposes a pre-trained video diffusion model trained on internet-scale datasets to recover videos revealing complex scene dynamics during the moment of capture and what might have occurred immediately into the past or future. Our approach is robust and versatile; it outperforms previous methods for this task, generalizes to challenging in-the-wild images, and supports downstream tasks such as recovering camera trajectories, object motion, and dynamic 3D scene structure. Code and data are available at blur2vid.github.io

Livestreaming has become a core format in electronic commerce, where social interaction, real time media, and promotional timing jointly shape consumer responses. Although prior studies have examined individual social and technical mechanisms in livestream commerce, less is known about how consumers form a holistic customer experience (CX) that translates livestream stimuli into behavioural and relational outcomes. Grounded in the Stimulus-Organism-Response framework, this study conceptualises CX as a second order organismic state that integrates cognitive, affective, social, and sensory appraisals in fashion livestreaming.Survey data from 374 fashion livestream shoppers in China were analysed using PLS-SEM to examine how parasocial interaction, interactivity, media richness, price benefit, and ease of use shape CX, and how CX subsequently influences trust, continuance intention, swift guanxi, herd behaviour, and the urge to buy impulsively. The results show that parasocial interaction is the strongest driver of CX, followed by price benefit, interactivity, and media richness. Ease of use is not significant, indicating that in high engagement, presenter led livestream formats, social and value-based cues outweigh marginal technical usability. CX positively predicts all five outcomes and serves as a key mediating mechanism, transmitting the effects of significant stimuli to behavioural and relational responses. The findings position multidimensional CX as the central psychological mechanism linking livestream design features to consumer responses in fashion commerce and offer actionable guidance for streamer selection, interaction design, and the strategic framing of time bound promotions.