Autoregressive Video Autoencoder with Decoupled Temporal and Spatial Context

• URL: http://arxiv.org/abs/2512.11293v1
• Date: Fri, 12 Dec 2025 05:40:01 GMT
• Title: Autoregressive Video Autoencoder with Decoupled Temporal and Spatial Context
• Authors: Cuifeng Shen, Lumin Xu, Xingguo Zhu, Gengdai Liu,
• Abstract summary: Video autoencoders compress videos into compact latent representations for efficient reconstruction.<n>We propose Autoregressive Video Autoencoder (ARVAE), which compresses and reconstructs each frame conditioned on its predecessor in an autoregressive manner.<n>ARVAE achieves superior reconstruction quality with extremely lightweight models and small-scale training data.
• Score: 8.458436768725212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video autoencoders compress videos into compact latent representations for efficient reconstruction, playing a vital role in enhancing the quality and efficiency of video generation. However, existing video autoencoders often entangle spatial and temporal information, limiting their ability to capture temporal consistency and leading to suboptimal performance. To address this, we propose Autoregressive Video Autoencoder (ARVAE), which compresses and reconstructs each frame conditioned on its predecessor in an autoregressive manner, allowing flexible processing of videos with arbitrary lengths. ARVAE introduces a temporal-spatial decoupled representation that combines downsampled flow field for temporal coherence with spatial relative compensation for newly emerged content, achieving high compression efficiency without information loss. Specifically, the encoder compresses the current and previous frames into the temporal motion and spatial supplement, while the decoder reconstructs the original frame from the latent representations given the preceding frame. A multi-stage training strategy is employed to progressively optimize the model. Extensive experiments demonstrate that ARVAE achieves superior reconstruction quality with extremely lightweight models and small-scale training data. Moreover, evaluations on video generation tasks highlight its strong potential for downstream applications.

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