EditVerse: Unifying Image and Video Editing and Generation with
  In-Context Learning
Recent advances in foundation models highlight a clear trend toward
unification and scaling, showing emergent capabilities across diverse domains.
While image generation and editing have rapidly transitioned from task-specific
to unified frameworks, video generation and editing remain fragmented due to
architectural limitations and data scarcity. In this work, we introduce
EditVerse, a unified framework for image and video generation and editing
within a single model. By representing all modalities, i.e., text, image, and
video, as a unified token sequence, EditVerse leverages self-attention to
achieve robust in-context learning, natural cross-modal knowledge transfer, and
flexible handling of inputs and outputs with arbitrary resolutions and
durations. To address the lack of video editing training data, we design a
scalable data pipeline that curates 232K video editing samples and combines
them with large-scale image and video datasets for joint training. Furthermore,
we present EditVerseBench, the first benchmark for instruction-based video
editing covering diverse tasks and resolutions. Extensive experiments and user
studies demonstrate that EditVerse achieves state-of-the-art performance,
surpassing existing open-source and commercial models, while exhibiting
emergent editing and generation abilities across modalities.
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Xuan Ju
claimed_verified
2025-09-25T07:57:25.912Z
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Xuan Ju
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juxuan27
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Tianyu Wang
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Yuqian Zhou
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He Zhang
68d49f9f36950a9dff156869
Qing Liu
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Nanxuan Zhao
68d49f9f36950a9dff15686b
Zhifei Zhang
68d49f9f36950a9dff15686c
Yijun Li
68d49f9f36950a9dff15686d
Yuanhao Cai
admin_assigned
2025-09-25T11:58:49.953Z
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Yuanhao Cai
user
CaiYuanhao
68d49f9f36950a9dff15686e
Shaoteng Liu
admin_assigned
2025-09-25T11:58:41.491Z
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Shaoteng Liu
user
Shaldon
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Daniil Pakhomov
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Zhe Lin
68d49f9f36950a9dff156871
Soo Ye Kim
admin_assigned
2025-09-25T11:58:24.755Z
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/avatars/2df54a1dcc1438282faa1aa12ae66b84.svg
Soo Ye Kim
user
sooyek
68d49f9f36950a9dff156872
Qiang Xu
EditVerse: Unifying Image and Video Editing and Generation with
  In-Context Learning
Recent advances in foundation models highlight a clear trend toward
unification and scaling, showing emergent capabilities across diverse domains.
While image generation and editing have rapidly transitioned from task-specific
to unified frameworks, video generation and editing remain fragmented due to
architectural limitations and data scarcity. In this work, we introduce
EditVerse, a unified framework for image and video generation and editing
within a single model. By representing all modalities, i.e., text, image, and
video, as a unified token sequence, EditVerse leverages self-attention to
achieve robust in-context learning, natural cross-modal knowledge transfer, and
flexible handling of inputs and outputs with arbitrary resolutions and
durations. To address the lack of video editing training data, we design a
scalable data pipeline that curates 232K video editing samples and combines
them with large-scale image and video datasets for joint training. Furthermore,
we present EditVerseBench, the first benchmark for instruction-based video
editing covering diverse tasks and resolutions. Extensive experiments and user
studies demonstrate that EditVerse achieves state-of-the-art performance,
surpassing existing open-source and commercial models, while exhibiting
emergent editing and generation abilities across modalities.
EditVerse is a unified framework using self-attention for image and video generation and editing, achieving state-of-the-art performance with a scalable data pipeline and benchmark.
self-attention in-context learning cross-modal knowledge transfer EditVerse EditVerseBench instruction-based video editing