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In this paper, we present a new inpainting framework for recovering missing regions of video frames. Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently. Towards this end, we propose a novel deep learning architecture which incorporates ConvLSTM and optical flow for modeling the spatial-temporal consistency in videos. It also saves much computational resource such that our method can handle videos with larger frame size and arbitrary length streamingly in real-time. Furthermore, to generate an accurate optical flow from corrupted frames, we propose a robust flow generation module, where two sources of flows are fed and a flow blending network is trained to fuse them. We conduct extensive experiments to evaluate our method in various scenarios and different datasets, both qualitatively and quantitatively. The experimental results demonstrate the superior of our method compared with the state-of-the-art inpainting approaches.
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This content will become publicly available on April 1, 2026
VILP: Imitation Learning With Latent Video Planning
In the era of generative AI, integrating video generation models into robotics opens new possibilities for the general-purpose robot agent. This paper introduces imitation learning with latent video planning (VILP). We propose a latent video diffusion model to generate predictive robot videos that adhere to temporal consistency to a good degree. Our method is able to generate highly time-aligned videos from multiple views, which is crucial for robot policy learning. Our video generation model is highly time-effcient. For example, it can generate videos from two distinct perspectives, each consisting of six frames with a resolution of 96x160 pixels, at a rate of 5 Hz. In the experiments, we demonstrate that VILP outperforms the existing video generation robot policy across several metrics: training costs, inference speed, temporal consistency of generated videos, and the performance of the policy. We also compared our method with other imitation learning methods. Our fndings indicate that VILP can rely less on extensive high-quality task-specifc robot action data while still maintaining robust performance. In addition, VILP possesses robust capabilities in representing multi-modal action distributions. Our paper provides a practical example of how to effectively integrate video generation models into robot policies, potentially offering insights for related felds and directions. For more details, please refer to our open-source repository https://github.com/ZhengtongXu/VILP.
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- Award ID(s):
- 2423068
- PAR ID:
- 10599520
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Robotics and Automation Letters
- Volume:
- 10
- Issue:
- 4
- ISSN:
- 2377-3774
- Page Range / eLocation ID:
- 3350 to 3357
- Subject(s) / Keyword(s):
- Imitation learning, diffusion models, video synthesis for robotics.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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