OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion
  Transformer Models
Recent advances in video insertion based on diffusion models are impressive.
However, existing methods rely on complex control signals but struggle with
subject consistency, limiting their practical applicability. In this paper, we
focus on the task of Mask-free Video Insertion and aim to resolve three key
challenges: data scarcity, subject-scene equilibrium, and insertion
harmonization. To address the data scarcity, we propose a new data pipeline
InsertPipe, constructing diverse cross-pair data automatically. Building upon
our data pipeline, we develop OmniInsert, a novel unified framework for
mask-free video insertion from both single and multiple subject references.
Specifically, to maintain subject-scene equilibrium, we introduce a simple yet
effective Condition-Specific Feature Injection mechanism to distinctly inject
multi-source conditions and propose a novel Progressive Training strategy that
enables the model to balance feature injection from subjects and source video.
Meanwhile, we design the Subject-Focused Loss to improve the detailed
appearance of the subjects. To further enhance insertion harmonization, we
propose an Insertive Preference Optimization methodology to optimize the model
by simulating human preferences, and incorporate a Context-Aware Rephraser
module during reference to seamlessly integrate the subject into the original
scenes. To address the lack of a benchmark for the field, we introduce
InsertBench, a comprehensive benchmark comprising diverse scenes with
meticulously selected subjects. Evaluation on InsertBench indicates OmniInsert
outperforms state-of-the-art closed-source commercial solutions. The code will
be released.
68d20ce31ca7156988a8ed2a
Jinshu Chen
admin_assigned
2025-09-23T14:10:58.669Z
6678045bc3f824dde8e217ff
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jinshu chen
user
JinshuChen
68d20ce31ca7156988a8ed2b
Xinghui Li
admin_assigned
2025-09-23T14:10:50.287Z
6752cd83ffaeeb979db974ae
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ci915Tdmv3uWbCqGy7LqL.png
Xinghui Li
user
Crayon-Shinchan
68d20ce31ca7156988a8ed2c
Xu Bai
68d20ce31ca7156988a8ed2d
Tianxiang Ma
68d20ce31ca7156988a8ed2e
Pengze Zhang
68d20ce31ca7156988a8ed2f
Zhuowei Chen
admin_assigned
2025-09-23T14:11:09.773Z
6304e2dabad6ce7fc0287d57
/avatars/3fd4a9a62b0ef98db2573411463a9247.svg
Zhuowei_Chen
user
ZhuoweiChen
68d20ce31ca7156988a8ed30
Gen Li
68d20ce31ca7156988a8ed31
Lijie Liu
68d20ce31ca7156988a8ed32
Songtao Zhao
68d20ce31ca7156988a8ed33
Bingchuan Li
admin_assigned
2025-09-23T14:11:24.038Z
63b415037af2e415f2599c18
/avatars/4afbe7d6d05a702f1beeed9c53e78153.svg
Bingchuan Li
user
lbc402
68d20ce31ca7156988a8ed34
Qian He
OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion
  Transformer Models
Recent advances in video insertion based on diffusion models are impressive.
However, existing methods rely on complex control signals but struggle with
subject consistency, limiting their practical applicability. In this paper, we
focus on the task of Mask-free Video Insertion and aim to resolve three key
challenges: data scarcity, subject-scene equilibrium, and insertion
harmonization. To address the data scarcity, we propose a new data pipeline
InsertPipe, constructing diverse cross-pair data automatically. Building upon
our data pipeline, we develop OmniInsert, a novel unified framework for
mask-free video insertion from both single and multiple subject references.
Specifically, to maintain subject-scene equilibrium, we introduce a simple yet
effective Condition-Specific Feature Injection mechanism to distinctly inject
multi-source conditions and propose a novel Progressive Training strategy that
enables the model to balance feature injection from subjects and source video.
Meanwhile, we design the Subject-Focused Loss to improve the detailed
appearance of the subjects. To further enhance insertion harmonization, we
propose an Insertive Preference Optimization methodology to optimize the model
by simulating human preferences, and incorporate a Context-Aware Rephraser
module during reference to seamlessly integrate the subject into the original
scenes. To address the lack of a benchmark for the field, we introduce
InsertBench, a comprehensive benchmark comprising diverse scenes with
meticulously selected subjects. Evaluation on InsertBench indicates OmniInsert
outperforms state-of-the-art closed-source commercial solutions. The code will
be released.
OmniInsert addresses challenges in mask-free video insertion using a novel data pipeline, feature injection, progressive training, and context-aware rephrasing, outperforming commercial solutions.
diffusion models Mask-free Video Insertion data scarcity Condition-Specific Feature Injection Progressive Training Subject-Focused Loss Insertive Preference Optimization Context-Aware Rephraser InsertBench