Video Generation with Learned Action Prior

• URL: http://arxiv.org/abs/2406.14436v1
• Date: Thu, 20 Jun 2024 16:00:07 GMT
• Title: Video Generation with Learned Action Prior
• Authors: Meenakshi Sarkar, Devansh Bhardwaj, Debasish Ghose,
• Abstract summary: Video generation is particularly challenging when the camera is mounted on a moving platform as camera motion interacts with image pixels. Existing methods typically address this by focusing on raw pixel-level image reconstruction without explicitly modelling camera motion dynamics. We propose a solution by considering camera or action as part of the observed image state, modelling both image state and action within a multi-AP learning framework.
• Score: 1.740992908651449
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Stochastic video generation is particularly challenging when the camera is mounted on a moving platform, as camera motion interacts with observed image pixels, creating complex spatio-temporal dynamics and making the problem partially observable. Existing methods typically address this by focusing on raw pixel-level image reconstruction without explicitly modelling camera motion dynamics. We propose a solution by considering camera motion or action as part of the observed image state, modelling both image and action within a multi-modal learning framework. We introduce three models: Video Generation with Learning Action Prior (VG-LeAP) treats the image-action pair as an augmented state generated from a single latent stochastic process and uses variational inference to learn the image-action latent prior; Causal-LeAP, which establishes a causal relationship between action and the observed image frame at time $t$, learning an action prior conditioned on the observed image states; and RAFI, which integrates the augmented image-action state concept into flow matching with diffusion generative processes, demonstrating that this action-conditioned image generation concept can be extended to other diffusion-based models. We emphasize the importance of multi-modal training in partially observable video generation problems through detailed empirical studies on our new video action dataset, RoAM.

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