(Wydawnictwo Politechniki Łódzkiej, 2023) Kubicki, Kacper; Ślot, Krzysztof
Generating videos from a single image with user-controlled attributes
is a complex challenge in the field of computer vision, despite the significant
advancements recently made in the field. This paper presents a novel
approach to tackle this issue, leveraging a convolutional autoencoder with
supervised principal component analysis and autoregressive inference step.
The efficacy of the proposed method is evaluated on two datasets – MNIST
handwritten-digits and time-lapse photos of the sky. Results from both quantitative
and qualitative analyses show that the proposed approach produces
high-quality videos of variable duration with user-defined attributes, while
preserving the integrity of original image contents.
