Hybrid Video Diffusion Models with 2D Triplane and 3D Wavelet Representation

• URL: http://arxiv.org/abs/2402.13729v4
• Date: Wed, 3 Apr 2024 11:03:35 GMT
• Title: Hybrid Video Diffusion Models with 2D Triplane and 3D Wavelet Representation
• Authors: Kihong Kim, Haneol Lee, Jihye Park, Seyeon Kim, Kwanghee Lee, Seungryong Kim, Jaejun Yoo,
• Abstract summary: We propose a novel hybrid video autoencoder, called HVtemporalDM, which can capture intricate dependencies more effectively. The HVDM is trained by a hybrid video autoencoder which extracts a disentangled representation of the video. Our hybrid autoencoder provide a more comprehensive video latent enriching the generated videos with fine structures and details.
• Score: 35.52770785430601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating high-quality videos that synthesize desired realistic content is a challenging task due to their intricate high-dimensionality and complexity of videos. Several recent diffusion-based methods have shown comparable performance by compressing videos to a lower-dimensional latent space, using traditional video autoencoder architecture. However, such method that employ standard frame-wise 2D and 3D convolution fail to fully exploit the spatio-temporal nature of videos. To address this issue, we propose a novel hybrid video diffusion model, called HVDM, which can capture spatio-temporal dependencies more effectively. The HVDM is trained by a hybrid video autoencoder which extracts a disentangled representation of the video including: (i) a global context information captured by a 2D projected latent (ii) a local volume information captured by 3D convolutions with wavelet decomposition (iii) a frequency information for improving the video reconstruction. Based on this disentangled representation, our hybrid autoencoder provide a more comprehensive video latent enriching the generated videos with fine structures and details. Experiments on video generation benchamarks (UCF101, SkyTimelapse, and TaiChi) demonstrate that the proposed approach achieves state-of-the-art video generation quality, showing a wide range of video applications (e.g., long video generation, image-to-video, and video dynamics control).

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