Phantom-Data : Towards a General Subject-Consistent Video Generation
  Dataset
Subject-to-video generation has witnessed substantial progress in recent
years. However, existing models still face significant challenges in faithfully
following textual instructions. This limitation, commonly known as the
copy-paste problem, arises from the widely used in-pair training paradigm. This
approach inherently entangles subject identity with background and contextual
attributes by sampling reference images from the same scene as the target
video. To address this issue, we introduce Phantom-Data, the first
general-purpose cross-pair subject-to-video consistency dataset, containing
approximately one million identity-consistent pairs across diverse categories.
Our dataset is constructed via a three-stage pipeline: (1) a general and
input-aligned subject detection module, (2) large-scale cross-context subject
retrieval from more than 53 million videos and 3 billion images, and (3)
prior-guided identity verification to ensure visual consistency under
contextual variation. Comprehensive experiments show that training with
Phantom-Data significantly improves prompt alignment and visual quality while
preserving identity consistency on par with in-pair baselines.
685a0fb40e4ad7e219758528
Zhuowei Chen
admin_assigned
2025-06-24T09:44:49.590Z
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Zhuowei_Chen
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ZhuoweiChen
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Bingchuan Li
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2025-06-24T09:44:57.735Z
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Bingchuan Li
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Tianxiang Ma
admin_assigned
2025-06-24T09:45:10.156Z
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Tianxiang Ma
user
TianxiangMa
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Lijie Liu
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Mingcong Liu
claimed_verified
2025-06-24T08:08:23.740Z
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Mingcong Liu
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onion-liu
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Yi Zhang
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Gen Li
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Xinghui Li
admin_assigned
2025-06-24T09:45:40.054Z
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Xinghui Li
user
Crayon-Shinchan
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Siyu Zhou
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Qian He
685a0fb40e4ad7e219758532
Xinglong Wu
admin_assigned
2025-06-24T09:45:50.807Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dttL8Fb3bKCVG5zjg_02q.png
Xinglong Wu
user
Xingzhe-xlwu
Phantom-Data : Towards a General Subject-Consistent Video Generation
  Dataset
Subject-to-video generation has witnessed substantial progress in recent
years. However, existing models still face significant challenges in faithfully
following textual instructions. This limitation, commonly known as the
copy-paste problem, arises from the widely used in-pair training paradigm. This
approach inherently entangles subject identity with background and contextual
attributes by sampling reference images from the same scene as the target
video. To address this issue, we introduce Phantom-Data, the first
general-purpose cross-pair subject-to-video consistency dataset, containing
approximately one million identity-consistent pairs across diverse categories.
Our dataset is constructed via a three-stage pipeline: (1) a general and
input-aligned subject detection module, (2) large-scale cross-context subject
retrieval from more than 53 million videos and 3 billion images, and (3)
prior-guided identity verification to ensure visual consistency under
contextual variation. Comprehensive experiments show that training with
Phantom-Data significantly improves prompt alignment and visual quality while
preserving identity consistency on par with in-pair baselines.
A cross-pair dataset called Phantom-Data improves subject-to-video generation by enhancing prompt alignment and visual quality while maintaining identity consistency.
Phantom-Data subject-to-video generation copy-paste problem in-pair training paradigm subject detection cross-context subject retrieval prior-guided identity verification