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Generative Video Propagation

Shaoteng Liu, Tianyu Wang, Jui-Hsien Wang, Qing Liu, Zhifei Zhang, Joon-Young Lee, Yijun Li, Bei Yu, Zhe Lin, Soo Ye Kim, Jiaya Jia

arXiv:2412.19761v1cs.CV

TL;DR

Many video-editing and propagation approaches focus on specific tasks or rely on intermediate motion representations, despite the potential of video generation models to represent realistic scenes. GenProp propagates first-frame edits with an image-to-video model while preserving unchanged content, and the paper reports gains over prior methods in video editing and object removal. It also demonstrates broader editing and tracking capabilities, with higher-level effects remaining challenging.

  • Problem

    Prior propagation methods can accumulate errors, generalize poorly, or require task-specific designs or retraining, while video-editing approaches often focus on particular tasks.

    Source: §1 · p. 2

  • Method

    GenProp uses a selective content encoder to retain unchanged video content and an image-to-video model to propagate first-frame edits, trained with synthetic data, a region-aware loss, and a mask-prediction head.

    Source: §1 · p. 2

  • Results

    GenProp outperforms state-of-the-art methods in video editing and object removal, while demonstrating applications including insertion, replacement, outpainting, and tracking.

    Source: §1 · pp. 2–3

  • Takeaways & Limitations

    The demonstrated scope includes substantial shape edits, independent motion for inserted objects, and removal or tracking of objects with effects such as shadows and reflections.

    Source: §1 · p. 2

    Higher-level effects caused by objects or events, such as a splash from a jump, remain challenging to edit or control.

    Source: S6. Limitations · p. 23

Abstract

from arXiv · show

Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative video propagation framework, various video tasks can be addressed in a unified way by leveraging the generative power of such models. Specifically, our framework, GenProp, encodes the original video with a selective content encoder and propagates the changes made to the first frame using an image-to-video generation model. We propose a data generation scheme to cover multiple video tasks based on instance-level video segmentation datasets. Our model is trained by incorporating a mask prediction decoder head and optimizing a region-aware loss to aid the encoder to preserve the original content while the generation model propagates the modified region. This novel design opens up new possibilities: In editing scenarios, GenProp allows substantial changes to an object's shape; for insertion, the inserted objects can exhibit independent motion; for removal, GenProp effectively removes effects like shadows and reflections from the whole video; for tracking, GenProp is capable of tracking objects and their associated effects together. Experiment results demonstrate the leading performance of our model in various video tasks, and we further provide in-depth analyses of the proposed framework.

1. Introduction

GenProp frames diverse video edits as propagation from the first frame, using video generation to modify regions while preserving unchanged content. The introduction presents a unified framework and situates it against prior methods that often depend on task-specific models or intermediate motion representations.

1. Introduction

  • The framework treats video applications commonly developed for specific tasks as instances of a more general video-propagation problem.The paper identifies removal, insertion, replacement, text-based editing, outpainting, and tracking as applications.Source: §1 · p. 2
  • GenProp propagates first-frame changes while preserving other video content, without requiring additional motion predictions such as optical flow.Its selective content encoder retains unchanged-region information while an image-to-video model propagates the edits.Source: §1 · p. 2
  • Training combines synthetic data from video-instance-segmentation datasets, a region-aware loss, and an auxiliary decoder that predicts the modified region.The loss penalizes encoder gradients in modified areas to help separate unchanged content from edits.Source: §1 · p. 2
  • GenProp outperforms state-of-the-art methods in video editing and object removal, while extending supported applications to tracking and other tasks.The framework also supports substantial object-shape changes, independent motion for inserted objects, and removal of effects such as shadows and reflections.Source: §1 · pp. 2–3

2. Related Work

  • Prior propagation approaches use representations such as optical flow or depth, while many are task-specific or require retraining for new tasks.The related-work discussion also notes methods based on mask tracking and inpainting, deformation fields, or sparse keypoints.Source: §2 · p. 3
  • Many diffusion-based editors focus on appearance changes, and some rely on intermediate motion controls or per-video fine-tuning.The discussion describes challenges with substantial shape edits and precise, coherent background changes in complex scenes.Source: §2 · p. 3

3. Method

GenProp uses an image-to-video model to propagate first-frame edits while preserving unchanged content, with its design addressing realism, consistency, and generality through complementary model components, losses, and synthetic training data.

3. Method

  • GenProp pairs an I2V model for realistic propagation with SCE, MPD, and region-aware loss for consistency, plus synthetic data and I2V versatility for generality.These components address the framework’s three stated challenges: realism, consistency, and generality.Source: §3 · p. 3

3.1. Problem Formulation

  • At inference, the I2V model uses the edited first frame and SCE-encoded original video to generate later frames; training feeds synthetic data only to SCE.Training pairs are constructed from original video sequences, while the I2V model uses the original video to avoid learning synthetic artifacts.Source: §3.1 · p. 4

3.2. Model Design

  • SCE injects unchanged-region features into corresponding I2V blocks, while MPD predicts edit masks from late latent features and receives instance-mask supervision.The MPD’s mask prediction helps the encoder disentangle edits from unchanged content.Source: §3.2 · pp. 4–5

3.3. Region-Aware Loss

  • The region-aware objective separately computes diffusion error in masked and unmasked regions and includes MPD supervision.The loss uses downsampled masks aligned with the video latent representation.Source: §3.3 · p. 5
  • A finite-difference gradient penalty reduces the SCE’s influence on masked regions by minimizing their effect on encoder input.The finite difference approximates second-order gradients using a small perturbation.Source: §3.3 · p. 5

3.4. Synthetic Data Generation

  • Synthetic training pairs use copy-and-paste, mask-and-fill, and color-fill augmentations with task embeddings to cover distinct propagation tasks.The data generation pipeline can be applied to available video instance segmentation datasets.Source: §3.4 · p. 5

4. Experiments

GenProp is evaluated across editing, removal, and tracking, with implementation variants and ablations examining its design. The experiments report advantages on challenging editing and removal benchmarks and show how the decoder, region-aware loss, and color-fill augmentation contribute.

4.1. Implementation Details

  • Experiments use both DiT and U-Net/SVD video-generation backbones; the main results use DiT, while ablations use the SVD-based architecture.The paper reports better video-generation quality with DiT.Source: §4.1 · p. 5

4.2. Comparisons

  • In editing comparisons, InsV2V, Pika, and AnyV2V struggle with substantial shape changes; ReVideo’s box-based two-stage approach can blur boundaries and accumulate motion errors.The compared methods also have limitations with insertion or complex background edits.Source: §4.2 · p. 7
  • GenProp tracks object effects such as reflections and shadows, while SAM-V2 often produces more precise masks and runs faster than real-time generation-based tracking.The figure shows tracking when solid-color fills are used as the first frame.Source: Figure 6 · p. 6, §4.2 · p. 7
  • GenProp outperforms other methods on most video-editing metrics, especially on the Challenging Test Set, and users prefer it over baselines by a large margin.Pika has better consistency on the Classic Test Set, where object shapes remain relatively unchanged.Source: Table 1 · p. 7, §4.2 · p. 8
  • For object removal, GenProp achieves the highest consistency and removes object effects, while ReVideo may leave box artifacts and ProPainter struggles with effects.The comparison also reports removal of large objects and natural filling, including large occluded areas.Source: §4.2 · pp. 7–8, Table 2 · p. 7

4.3. Ablation Study

  • The mask prediction decoder improves text alignment and consistency, and its supervision helps prevent partially removed objects from reappearing, including under heavy occlusion.The ablation reports more accurate removal results and predicted masks with MPD.Source: Table 3 · p. 7, §4.3 · p. 8
  • Without region-aware loss, the original object can gradually reappear; with it, edited areas propagate stably and consistently.The stated challenge is that SCE may select edited regions from the original video for reconstruction, weakening I2V generation.Source: Table 3 · p. 7, §4.3 · p. 8
  • Color-fill augmentation explicitly trains tracking, and it lets GenProp propagate a large shape change, such as changing a girl into a small cat.Copy-and-paste and mask-and-fill augmentations instead implicitly train object modifications, replacements, and deletions.Source: §4.3 · p. 8

5. Conclusion

GenProp uses image-to-video generation to support removal, insertion, and tracking while preserving unchanged content and propagating changes. The authors report realistic videos and a wider range of edits, while leaving multi-key-frame edits and additional tasks for future work.

5. Conclusion

  • GenProp applies image-to-video generation to removal, insertion, and tracking, and expands achievable edits to include objects and their associated effects.The conclusion says it does not rely on traditional intermediate representations such as optical flow or depth maps.Source: §5 · p. 8
  • A selective content encoder and I2V generation model preserve unchanged content while dynamically propagating edits; synthetic data and region-aware loss further support disentanglement across frames.The authors characterize the resulting videos as highly realistic and the framework as effective across its scope.Source: §5 · p. 9
  • The authors plan to extend GenProp to accept edits at more than one key frame and to investigate additional video tasks.Source: §5 · p. 9

S1. Synthetic Data Generation

GenProp trains on synthetic video pairs derived from instance-segmentation data, using copy-and-paste, mask-and-fill, and color-fill augmentations for different propagation tasks.

S1.1. Copy-and-Paste

  • Copy-and-paste places a masked object from a second video onto the first, but does not explicitly harmonize the pasted object with its target video.Object size, position, and motion trajectory vary across samples.Source: S1.1. Copy-and-Paste · p. 13

S1.2. Mask-and-Fill

  • Mask-and-fill replaces masked regions using either surrounding-background mean fill or OpenCV inpainting.Both are designed for online data generation; mean fill prioritizes speed and simplicity, while inpainting offers more sophisticated results at slightly higher computational cost, at an approximately 2:1 ratio.Source: S1.2. Mask-and-Fill · p. 13

S1.3. Color-Fill

  • Color-fill replaces masked pixels with palette colors, creating visual cues for tracking or editing specific regions.In 30% of cases, a second color is sampled for another instance to support tracking multiple instances.Source: S1.3. Color-Fill · p. 13

S1. Synthetic Data Generation

  • Synthetic training pairs use copy-and-paste for removal, mask-and-fill for editing and insertion, and color-fill for tracking and editing enhancement.The pipeline combines these approaches on video instance-segmentation data.Source: S1. Synthetic Data Generation · p. 13, Figure 8 · p. 14

S2. Controls for Generation

GenProp offers text prompts, an adjustable injection weight, and black regions in the input video as controls over generated content and edited-region motion.

S2.1. Text Control Analysis

  • Text prompts guide the depicted behavior and movement of inserted objects, including an eagle’s behavior and a duck’s swimming direction.The examples vary prompts to control whether the eagle stands or flies and the direction the duck swims.Source: S2.1. Text Control Analysis · p. 15

S2.2. Injection Weight Analysis

  • The injection weight trades off reconstruction of the original video against generation of edited content.In the sofa example, lowering the weight from 1.0 to 0.8 makes flames more pronounced; at 0.6, ground and window reconstruction weakens while smoke spreads farther.Source: S2.2. Injection Weight Analysis · pp. 15–16, Figure 10 · p. 16

S2.3. Black Region in Input Video

  • Adding black regions to the input can help control edited-element motion, including simulating a car being overtaken and steering a lemon’s motion with text.The paper also describes moving masks as a way to guide motion of modified content.Source: S2.3. Black Region in Input Video · p. 16, Figure 11 · p. 17

S3. User Study Details

The user study compares GenProp with a random baseline on editing-goal alignment and output video quality, while accompanying observations relate mask predictions to editing outcomes.

S3. User Study Details

  • Users compared GenProp and a random baseline, rating alignment to the editing goal and output video quality without knowing which result came from which model.Responses under one second were excluded to filter unreasonable response times.Source: S3. User Study Details · p. 18
  • The accompanying mask visualizations show that editing may fail when mask prediction fails, and that predicted regions can extend beyond the original object.The decoder can estimate the edited region even when its shape extends beyond the original object.Source: Figure 13 · p. 19, Figure 14 · p. 19

S4. Mask Prediction Analysis

The mask prediction decoder’s outputs are associated with editing success and failure, and can estimate effects and future insertion regions beyond the removed object itself.

S4. Mask Prediction Analysis

  • Editing outcomes and mask predictions often succeed or fail together, and the decoder can predict shadows and future appearance areas of inserted objects beyond its removal-mask training target.The paper links these estimates to integrating edits consistently with existing video content.Source: S4. Mask Prediction Analysis · p. 19

S5. More Results

The supplementary comparisons show GenProp propagating first-frame edits across videos, including removal, editing, and object replacement with independent motion. They also illustrate a remaining challenge: removing an object does not necessarily remove effects caused by its events.

Removal

  • GenProp consistently removes objects and their effects, such as shadows and reflections, throughout the video.The comparison is shown in Fig. 15.Source: Figure 15 · p. 20

Editing on TGVE

  • On TGVE, GenProp propagates the desired first-frame edit throughout the video.Additional comparisons are shown in Fig. 16.Source: Figure 16 · p. 21

Editing on the Challenging Test Set

  • On the Challenging Test Set, GenProp replaces existing objects and generates independent motion for inserted objects across frames.Additional comparisons are shown in Fig. 17.Source: Figure 17 · p. 22

Limitation

  • Removing an object does not remove every event it caused; for example, a water splash remains when the girl is removed.This limitation is illustrated in Fig. 18.Source: Figure 18 · p. 23

S6. Limitations

GenProp can handle side effects such as shadows and reflections, but higher-level effects caused by objects or events remain challenging to edit.

Limitations

  • Higher-level effects caused by objects or events remain challenging to edit; for example, removing the girl does not directly modify or control the resulting water splash.The splash example is shown in Fig. 18.Source: S6. Limitations · p. 23
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