EasyControl: Adding Control to Video Diffusion for Controllable Video Generation and Interpolation

The diffusion model is widely leveraged for either controllable video generation or video interpolation. As each field has its task-specific problems, it is difficult to merely develop a single model for completing both tasks simultaneously. Moreover, most existing works only support image conditions and necessitate redesigning the model structure to accommodate other types of conditions. Even so, they still face frame flickering issues when using the image as the condition due to the strong alignment of image pixels. To tackle these problems, in this work, we are the first to propose a unified diffusion framework, EasyControl, for both tasks of controllable video generation and interpolation with different types of conditions. The proposed EasyControl introduces a condition adapter to extract the condition features, which is then injected into an interchangeable fundamental text-to-video model to guide the video generation. To alleviate frame flicker problems, we propose a module named VideoInit to integrate the low-frequency band of input condition images, ensuring smoother generation. Experimental results on four benchmarks suggest that our method outperforms the previous methods on each task.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10889997
Primary Topic
Advanced Vision and Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

EasyControl: Adding Control to Video Diffusion for Controllable Video Generation and Interpolation

Yuanfan Guo, Panwen Hu, Xiaodan Liang, Cong Wang et al.
Advanced Vision and Imaging
article

EasyControl: Adding Control to Video Diffusion for Controllable Video Generation and Interpolation

Yuanfan Guo, Panwen Hu, Xiaodan Liang, Cong Wang, Xiao Dong, Hang Xu, Jiaxi Gu
article en

Abstract

The diffusion model is widely leveraged for either controllable video generation or video interpolation. As each field has its task-specific problems, it is difficult to merely develop a single model for completing both tasks simultaneously. Moreover, most existing works only support image conditions and necessitate redesigning the model structure to accommodate other types of conditions. Even so, they still face frame flickering issues when using the image as the condition due to the strong alignment of image pixels. To tackle these problems, in this work, we are the first to propose a unified diffusion framework, EasyControl, for both tasks of controllable video generation and interpolation with different types of conditions. The proposed EasyControl introduces a condition adapter to extract the condition features, which is then injected into an interchangeable fundamental text-to-video model to guide the video generation. To alleviate frame flicker problems, we propose a module named VideoInit to integrate the low-frequency band of input condition images, ensuring smoother generation. Experimental results on four benchmarks suggest that our method outperforms the previous methods on each task.

Sun Yat-sen University (CN), Huawei Technologies (China) (CN), Chinese University of Hong Kong, Shenzhen (CN)
Sun Yat-sen University
Openalex Percentile: Top 97%
Advanced Vision and Imaging
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