Autoregressive Label-Conditioned Autoencoder for Controllable Image-To-Video Generation




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Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press


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.


Słowa kluczowe

video generation, image-to-video, autoencoder, supervised PCA, generacja wideo, przetwarzanie obrazu na wideo, autoenkoder, nadzorowana PCA


Kubicki K., Ślot K., Autoregressive Label-Conditioned Autoencoder for Controllable Image-To-Video Generation. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 307-311, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.49.