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VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control

Yuxuan Bian, Zhaoyang Zhang, Xuan Ju, Mingdeng Cao, Liangbin Xie, Ying Shan, Qiang Xu

arXiv:2503.05639v3cs.CVcs.AIcs.MM

TL;DR

Existing video inpainting methods struggle with fully masked objects, background preservation, foreground generation, and long-video identity consistency. VideoPainter addresses these issues with a lightweight dual-branch context-control architecture, ID resampling, and scalable datasets, achieving state-of-the-art performance across video inpainting and editing metrics while remaining limited by base-model quality and input quality.

  • Problem

    Existing non-generative and single-branch generative methods struggle with fully masked objects, background preservation, foreground generation, and long-video identity consistency.

  • Method

    VideoPainter adds a context encoder using 6% of backbone parameters to a pre-trained video DiT, with ID resampling and scalable VPData and VPBench construction.

  • Results

    VideoPainter achieves state-of-the-art performance across 8 metrics in video inpainting and editing, including video quality, masked-region preservation, and text coherence.

  • Takeaways & Limitations

    The framework provides plug-and-play background control and supports any-length video inpainting and editing.

  • Takeaways & Limitations

    Generation quality depends on the base model, while performance is suboptimal with low-quality masks or misaligned video captions.

Abstract

from arXiv · show

Video inpainting, which aims to restore corrupted video content, has experienced substantial progress. Despite these advances, existing methods, whether propagating unmasked region pixels through optical flow and receptive field priors, or extending image-inpainting models temporally, face challenges in generating fully masked objects or balancing the competing objectives of background context preservation and foreground generation in one model, respectively. To address these limitations, we propose a novel dual-stream paradigm VideoPainter that incorporates an efficient context encoder (comprising only 6% of the backbone parameters) to process masked videos and inject backbone-aware background contextual cues to any pre-trained video DiT, producing semantically consistent content in a plug-and-play manner. This architectural separation significantly reduces the model's learning complexity while enabling nuanced integration of crucial background context. We also introduce a novel target region ID resampling technique that enables any-length video inpainting, greatly enhancing our practical applicability. Additionally, we establish a scalable dataset pipeline leveraging current vision understanding models, contributing VPData and VPBench to facilitate segmentation-based inpainting training and assessment, the largest video inpainting dataset and benchmark to date with over 390K diverse clips. Using inpainting as a pipeline basis, we also explore downstream applications including video editing and video editing pair data generation, demonstrating competitive performance and significant practical potential. Extensive experiments demonstrate VideoPainter's superior performance in both any-length video inpainting and editing, across eight key metrics, including video quality, mask region preservation, and textual coherence.

1 INTRODUCTION

VideoPainter addresses limitations in video inpainting by separating background preservation from foreground generation through a lightweight, plug-and-play dual-branch design. It also supports any-length videos, large-scale dataset construction, and video editing applications.

  • Motivation and approach: VideoPainter uses a dual-branch architecture that combines a lightweight context encoder with a pre-trained video DiT.The design separates masked-video context extraction from semantic video generation.
  • Motivation and approach: The context encoder comprises only 6% of the backbone parameters and provides efficient, dense background guidance.Its features are integrated into the pre-trained DiT in a group-wise manner.
  • Key components: Inpainting region ID resampling enables any-length video processing while preserving target identity across clips.Previous-clip inpainting tokens are appended to current key-value vectors during inference.
  • Datasets and applications: VPData and VPBench contain over 390K clips with precise segmentation masks and dense text captions for training and evaluation.The dataset pipeline uses advanced vision models for scalable construction.
  • Results: Experiments report superior training efficiency and final results against state-of-the-art baselines and a single-branch finetuning setup.The evaluation includes video inpainting and editing outcomes across eight metrics.

2 RELATED WORK

Prior video inpainting methods divide into non-generative pixel-propagation approaches and generative extensions of image-inpainting models, each with important limitations. VideoPainter addresses these gaps alongside a larger, captioned dataset and benchmark.

  • Video inpainting methods: Non-generative methods propagate pixels or features but cannot reliably inpaint fully segmentation-masked objects.They are mainly effective for partial occlusions with random masks because fully masked regions provide insufficient context.
  • Video inpainting datasets: VPData is described as the largest video inpainting dataset, with over 390K clips containing segmentation masks, video captions, and masked-region descriptions.VPBench provides the corresponding benchmark.
  • Video inpainting methods: Generative methods extend single-branch image-inpainting architectures with temporal attention but struggle to balance background preservation and foreground generation.They also have inferior temporal coherence compared with native video DiTs.
  • Video inpainting datasets: The dataset construction pipeline comprises collection, annotation, splitting, selection, and captioning.These five preprocessing steps support scalable dataset creation.
  • Video inpainting datasets: Existing video segmentation datasets lack sufficient scale and textual control conditions needed for generating masked objects.These limitations constrain their suitability for generative video inpainting.

3 METHOD

The method combines scalable dataset construction with a lightweight context-control branch attached to a frozen video DiT. It further uses selective feature fusion, temporal transition handling, and ID resampling for long videos and diverse backbones.

  • VPData and VPBench Construction Pipeline: VPData and VPBench are built through collection, annotation, splitting, selection, and captioning.The pipeline uses automated vision-model workflows to produce masks and captions.
  • Dual-branch Inpainting Control: The context encoder receives noisy latent, masked-video latent, and downsampled masks as concatenated inputs.The masked-video latent is extracted through a VAE, while cubic interpolation makes masks dimensionally compatible.
  • Dual-branch Inpainting Control: VideoPainter clones only the first two pre-trained DiT layers, using 6% of backbone parameters for contextual control.The first and second encoder layers modulate the first and second backbone halves, respectively.
  • Dual-branch Inpainting Control: Token-selective fusion integrates only pure-background tokens into the frozen DiT to avoid foreground-background ambiguity.This provides dense background guidance while leaving foreground generation to the backbone.
  • Target Region ID Resampling: Overlapping generation and weighted averaging smooth transitions, while previous-clip region tokens support long-video identity consistency.During inference, inpainting-region tokens are appended to current key-value pairs for ID sampling.
  • Plug-and-play control: The plug-and-play framework supports stylization backbones or LoRAs and both text-to-video and image-to-video DiTs.Image-to-video compatibility integrates with existing image-inpainting capabilities.

4 EXPERIMENTS

Experiments evaluate VideoPainter on standard and long video inpainting, video editing, human preferences, and architectural ablations. Across these settings, it delivers strong quantitative and qualitative results while supporting plug-and-play control.

  • 4.2 Video Inpainting: VideoPainter achieves optimal performance across segmentation-based standard and long videos and random-mask inpainting benchmarks.The comparison includes ProPainter, COCOCO, and Cog-Inp on VPBench and Davis.
  • 4.2 Video Inpainting: VideoPainter maintains coherent, high-quality inpainting and consistent target identities beyond one minute through ID resampling.Qualitative comparisons report failures from fully masked objects, inconsistent identities, and blending artifacts in competing methods.
  • 4.3 Video Editing: VideoPainter surpasses UniEdit, DiTCtrl, and ReVideo on both standard and long video editing in VPBench.The dual-branch design preserves non-edited regions while aligning edited regions with instructions, and ID resampling maintains long-video consistency.
  • 4.4 Human Evaluation: 30 participants evaluated 50 cases, and VideoPainter received higher preference rates than existing baselines across all criteria in both tasks.The criteria were background preservation, text alignment, and video quality.
  • 4.5 Ablation Analysis: The dual-branch model significantly outperforms single-branch fine-tuning by separating background preservation from foreground generation.Ablations also examine context-encoder depth, selective token integration, backbone choice, ID resampling, and mask robustness.
  • 4.6 Plug-and-Play Control: VideoPainter integrates with community-developed Gromit-style LoRA despite the domain gap between anime-style data and its training dataset.This demonstrates flexible plug-and-play control over the selected base diffusion transformer.

5 DISCUSSION

VideoPainter combines dual-branch plug-and-play control, long-video ID resampling, and a 390K-plus-clip dataset pipeline. The authors report state-of-the-art results across eight metrics, while identifying dependencies on base-model quality and input quality.

  • 5 DISCUSSION: VideoPainter introduces a dual-branch framework, ID resampling, and VPData and VPBench containing over 390K video clips.The framework targets plug-and-play control and long-video identity consistency for inpainting and editing.
  • 5 DISCUSSION: Generation quality remains limited by the base model’s complex physical and motion modeling capabilities.The authors also report suboptimal performance with low-quality masks or misaligned video captions.

A USER STUDY

A user study evaluates VideoPainter against prior inpainting and editing models using standard-length VPBench samples. Participants assess background preservation, text alignment, and overall video quality.

  • A USER STUDY: 30 participants evaluated 50 randomly selected cases from VPBench’s inpainting and editing subsets.Each sample was judged on background preservation, text alignment, and video quality.
  • A USER STUDY: Participants selected the model producing the best result for each criterion in every sample.The evaluation reports average proportions of selections as the best response.

B ABLATION ON DUAL-BRANCH ARTECTURE

Ablations compare dual-branch VideoPainter with single-branch fine-tuning and test design choices affecting efficiency, token integration, mask handling, and long-video identity consistency. The dual-branch design converges more effectively and reaches a lower final loss.

  • B ABLATION ON DUAL-BRANCH ARTECTURE: The dual-branch model significantly outperforms its single-branch counterpart by separating background preservation from foreground generation.This separation reduces model complexity and avoids their competing objectives within one branch.
  • B ABLATION ON DUAL-BRANCH ARTECTURE: Ablations test context-encoder depth, selective token integration, text-to-video backbones, target-region ID resampling, and mask-generation strategies.These comparisons evaluate efficiency, token disambiguation, backbone compatibility, long-video consistency, and robustness to mask quality.
  • B ABLATION ON DUAL-BRANCH ARTECTURE: The dual-branch training curve shows faster, more stable convergence and better final performance than single-branch fine-tuning.The figure compares the two training-loss curves despite the dual-branch model using significantly fewer trainable parameters.
  • B ABLATION ON DUAL-BRANCH ARTECTURE: The final loss is approximately half that of single-branch fine-tuning.The reported convergence advantage follows the separation of background preservation and foreground generation into distinct branches.

C ABLATION ON MASK QUALITY

VideoPainter generalizes across varied video segmentation-mask qualities but is less robust to atypical rectangular masks. The degradation is attributed to artifacts along rectangular edges caused by their geometric mismatch with natural masks.

  • C ABLATION ON MASK QUALITY: VideoPainter exhibits robust generalization across various qualities of video segmentation masks.The masks were augmented by randomly sampling dilation and erosion kernel sizes applied to original segmentation masks.
  • C ABLATION ON MASK QUALITY: Approximately 13% performance degradation occurs with random rectangular masks.The authors associate this decrease primarily with artifacts generated along rectangular edges.
  • C ABLATION ON MASK QUALITY: Rectangular-mask artifacts are attributed to substantial geometric disparity from natural segmentation masks encountered in real-world scenarios.

D ABLATION ON ID RESAMPLING

The ablation identifies target-region ID resampling as important for maintaining identity consistency in long-video inpainting. Removing it increasingly harms performance as video length grows.

  • D ABLATION ON ID RESAMPLING: Removing ID resampling degrades long-video inpainting performance, with deterioration increasing as video length increases.
  • D ABLATION ON ID RESAMPLING: ID resampling maintains ID consistency by explicitly resampling inpainted-region tokens from previous clips.

E.1 Video Inpainting

Video inpainting methods include non-generative pixel-propagation approaches and generative approaches that extend image-inpainting architectures with temporal modeling. Existing generative methods add control modules or temporal attention but retain limitations in background preservation and video conditioning.

  • E.1 Video Inpainting: Non-generative methods leverage architectural priors, local perception, or attention to propagate pixels and fill masked video regions.
  • E.1 Video Inpainting: Generative methods extend pretrained generative backbones with additional modules or training strategies for video inpainting.
  • E.1 Video Inpainting: VideoComposer integrates text, depth, mask, and motion-vector controls through a shared spatiotemporal condition-fusion module.
  • E.1 Video Inpainting: VideoComposer’s background preservation is compromised by control-condition compression and the lack of masked-video conditioning support.
  • E.1 Video Inpainting: AVID and COCOCO augment Stable Diffusion Inpainting with trainable temporal-attention layers for per-frame filling and temporal smoothing.

E.2 Video Inpainting Datasets

Existing video inpainting datasets provide high-quality masks and diverse objects but lack the scale and captions needed for generative masked-object completion. VPData addresses these gaps with over 390K clips containing precise masks and rich textual annotations.

  • E.2 Video Inpainting Datasets: Existing video segmentation datasets face insufficient scale for generative models and lack captions needed to generate masked objects.
  • E.2 Video Inpainting Datasets: Removing ID resampling is evaluated by comparing the original VideoPainter with a version without ID resampling on long-video inpainting.
  • E.2 Video Inpainting Datasets: VPData contains over 390K high-quality clips with precise segmentation masks, comprehensive video captions, and masked-region descriptions.
  • E.2 Video Inpainting Datasets: VPData is described as the largest video inpainting dataset to date.
  • E.2 Video Inpainting Datasets: VideoPainter’s performance is suboptimal with low-quality or misaligned video captions.
  • E.2 Video Inpainting Datasets: The paper presents additional results for video inpainting and video editing, including long-video comparisons and downstream editing examples.
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