Hi-VAE: Efficient Video Autoencoding with Global and Detailed Motion

• URL: http://arxiv.org/abs/2506.07136v1
• Date: Sun, 08 Jun 2025 13:30:11 GMT
• Title: Hi-VAE: Efficient Video Autoencoding with Global and Detailed Motion
• Authors: Huaize Liu, Wenzhang Sun, Qiyuan Zhang, Donglin Di, Biao Gong, Hao Li, Chen Wei, Changqing Zou,
• Abstract summary: Hi-VAE formulates an efficient video autoencoding framework that encodes coarse-to-fine motion representations of video dynamics.<n>We show that Hi-VAE exhibits a high compression factor of 1428$times$, almost 30$times$ higher than baseline methods.
• Score: 23.80254637449824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent breakthroughs in video autoencoders (Video AEs) have advanced video generation, but existing methods fail to efficiently model spatio-temporal redundancies in dynamics, resulting in suboptimal compression factors. This shortfall leads to excessive training costs for downstream tasks. To address this, we introduce Hi-VAE, an efficient video autoencoding framework that hierarchically encode coarse-to-fine motion representations of video dynamics and formulate the decoding process as a conditional generation task. Specifically, Hi-VAE decomposes video dynamics into two latent spaces: Global Motion, capturing overarching motion patterns, and Detailed Motion, encoding high-frequency spatial details. Using separate self-supervised motion encoders, we compress video latents into compact motion representations to reduce redundancy significantly. A conditional diffusion decoder then reconstructs videos by combining hierarchical global and detailed motions, enabling high-fidelity video reconstructions. Extensive experiments demonstrate that Hi-VAE achieves a high compression factor of 1428$\times$, almost 30$\times$ higher than baseline methods (e.g., Cosmos-VAE at 48$\times$), validating the efficiency of our approach. Meanwhile, Hi-VAE maintains high reconstruction quality at such high compression rates and performs effectively in downstream generative tasks. Moreover, Hi-VAE exhibits interpretability and scalability, providing new perspectives for future exploration in video latent representation and generation.

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