Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific
  Adaptation
Video outpainting is a challenging task, aiming at generating video content
outside the viewport of the input video while maintaining inter-frame and
intra-frame consistency. Existing methods fall short in either generation
quality or flexibility. We introduce MOTIA Mastering Video Outpainting Through
Input-Specific Adaptation, a diffusion-based pipeline that leverages both the
intrinsic data-specific patterns of the source video and the image/video
generative prior for effective outpainting. MOTIA comprises two main phases:
input-specific adaptation and pattern-aware outpainting. The input-specific
adaptation phase involves conducting efficient and effective pseudo outpainting
learning on the single-shot source video. This process encourages the model to
identify and learn patterns within the source video, as well as bridging the
gap between standard generative processes and outpainting. The subsequent
phase, pattern-aware outpainting, is dedicated to the generalization of these
learned patterns to generate outpainting outcomes. Additional strategies
including spatial-aware insertion and noise travel are proposed to better
leverage the diffusion model's generative prior and the acquired video patterns
from source videos. Extensive evaluations underscore MOTIA's superiority,
outperforming existing state-of-the-art methods in widely recognized
benchmarks. Notably, these advancements are achieved without necessitating
extensive, task-specific tuning.
65fbba2ba9d83c79a62d0c9c
Fu-Yun Wang
claimed_verified
2024-03-21T08:26:23.869Z
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Fu-Yun Wang
user
wangfuyun
65fbba2ba9d83c79a62d0c9d
Xiaoshi Wu
admin_assigned
2024-03-21T10:31:30.970Z
6423f44d30b0e4ab36dd939d
/avatars/9bbf6f00188d21d7bd5b678b955b9fc0.svg
Xiaoshi Wu
user
xswu
65fbba2ba9d83c79a62d0c9e
Zhaoyang Huang
admin_assigned
2024-03-21T10:31:51.493Z
64c26e0e400274d129a10d5d
/avatars/c1debf3d7f61c2ea01d9a25d7e332712.svg
Zhaoyang Huang
user
DrinkingCoder
65fbba2ba9d83c79a62d0c9f
Xiaoyu Shi
65fbba2ba9d83c79a62d0ca0
Dazhong Shen
65fbba2ba9d83c79a62d0ca1
Guanglu Song
admin_assigned
2024-03-21T10:32:45.504Z
636a2efffbc31ee68fee5c1c
/avatars/21e119f23e1e6fe926567409e74d1a89.svg
Song
user
Guanglu
65fbba2ba9d83c79a62d0ca2
Yu Liu
admin_assigned
2024-03-21T10:33:26.329Z
636de85cc4a7a729c164d2b5
/avatars/3e281e547e1697e1c06805e7e63f3918.svg
Yu Liu
user
YuLiu
65fbba2ba9d83c79a62d0ca3
Hongsheng Li
admin_assigned
2024-03-21T10:33:34.953Z
65c04e9c27a5fdca81abcbd9
/avatars/12a155683c824fa23da4a9e2bed4f64e.svg
Hongsheng LI
user
hsli-cuhk
Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific
  Adaptation
Video outpainting is a challenging task, aiming at generating video content
outside the viewport of the input video while maintaining inter-frame and
intra-frame consistency. Existing methods fall short in either generation
quality or flexibility. We introduce MOTIA Mastering Video Outpainting Through
Input-Specific Adaptation, a diffusion-based pipeline that leverages both the
intrinsic data-specific patterns of the source video and the image/video
generative prior for effective outpainting. MOTIA comprises two main phases:
input-specific adaptation and pattern-aware outpainting. The input-specific
adaptation phase involves conducting efficient and effective pseudo outpainting
learning on the single-shot source video. This process encourages the model to
identify and learn patterns within the source video, as well as bridging the
gap between standard generative processes and outpainting. The subsequent
phase, pattern-aware outpainting, is dedicated to the generalization of these
learned patterns to generate outpainting outcomes. Additional strategies
including spatial-aware insertion and noise travel are proposed to better
leverage the diffusion model's generative prior and the acquired video patterns
from source videos. Extensive evaluations underscore MOTIA's superiority,
outperforming existing state-of-the-art methods in widely recognized
benchmarks. Notably, these advancements are achieved without necessitating
extensive, task-specific tuning.
MOTIA, a diffusion-based pipeline, achieves video outpainting with high quality and flexibility by leveraging input-specific adaptation and pattern-aware outpainting phases.
diffusion-based pipeline pseudo outpainting input-specific adaptation pattern-aware outpainting spatial-aware insertion noise travel video outpainting inter-frame consistency intra-frame consistency