MOTIONFLOW:Learning Implicit Motion Flow for Complex Camera Trajectory Control in Video Generation
Keywords: Camera trajectory, video generation, diffusion model
TL;DR: Our method achieves high-quality video generation results that adhere to the input camera trajectories via iteratively learning a implicit motion flow as a prior
Abstract: Generating videos guided by camera trajectories poses significant challenges in achieving consistency and generalizability, particularly when both camera and object motions are present. Existing approaches often attempt to learn these motions separately, which may lead to confusion regarding the relative motion between the camera and the objects. To address this challenge, we propose a novel approach that integrates both camera and object motions by converting them into the motion of corresponding pixels. Utilizing a stable diffusion network, we effectively learn reference motion maps in relation to the specified camera trajectory. These maps, along with an extracted semantic object prior, are then fed into an image-to-video network to generate the desired video that can accurately follow the designated camera trajectory while maintaining consistent object motions. Extensive experiments verify that our model outperforms SOTA methods by a large margin.
Primary Area: generative models
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Submission Number: 455
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