Generative Video Propagation (2412.19761v1)

Abstract: 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.

• The paper introduces GenProp, a unified framework that propagates first-frame edits across entire videos.
• The paper leverages a selective content encoder, mask prediction decoder, and region-aware loss to ensure precise modifications.
• The paper demonstrates superior performance compared to state-of-the-art methods in object removal, background replacement, and tracking.

Generative Video Propagation

The paper "Generative Video Propagation" introduces a novel framework, GenProp, designed to address various video editing tasks through a unified generative approach. The researchers propose leveraging the inherent capabilities of large-scale image-to-video (I2V) models to seamlessly propagate edits from the first frame of a video throughout the entire sequence. This paper positions GenProp as a comprehensive solution for tasks that traditionally required distinct methodologies, such as object removal, background replacement, and object tracking.

Overview

GenProp utilizes a selective content encoder and an image-to-video generation model, complemented by a data generation scheme that employs instance-level video segmentation datasets. The framework is engineered to maintain the integrity of unchanged elements in the video while adeptly modifying selected regions. This is achieved through a mask prediction decoder head and a region-aware loss function, which collectively bolster the model's ability to preserve content while effectively propagating changes.

Experimentation demonstrates the model's proficiency across numerous video tasks:

• Editing: GenProp supports drastic shape modifications and independent motion for object insertions.
• Removal: The framework handles the elimination of objects and their effects, such as shadows and reflections, throughout the video.
• Tracking: Capable of tracking objects and their associated effects without requiring dense mask labeling.

The proposed approach significantly challenges traditional methods, which often depend on auxiliary representations like optical flow, depth, or radiance fields, and are susceptible to error accumulation and limited robustness.

Technical Contributions

1. Generative Video Propagation: GenProp redefines video propagation by harnessing the generative potential of I2V models, extending the framework's applicability to a broad range of tasks without the need for motion predictions.
2. Selective Content Encoder and Mask Prediction Decoder: These components enable the model to focus selectively on content modifications while maintaining high fidelity in unaltered video regions.
3. Region-Aware Loss: This loss function ensures that the generative model maintains a clear distinction between altered and preserved content, optimizing the encoder's role in content preservation.
4. Synthetic Data Generation: By using instance segmentation datasets to create synthetic training pairs with varied augmentation strategies, the model is trained to handle diverse editing scenarios, enhancing its flexibility.

Numerical Results and Comparisons

The results on the provided benchmarks exhibit GenProp's clear advantage over state-of-the-art models, particularly in challenging scenarios requiring complex edits like large object replacement or simultaneous object removal and insertion. GenProp consistently outperforms existing models in text alignment, consistency, and user preference metrics, demonstrating its efficacy in practical applications.

Implications and Future Directions

The research presented in this paper marks a significant advance in the field of video editing, offering a versatile tool that simplifies processes traditionally constrained by technical and computational limitations. Practically, the ability to edit videos with such ease introduces potential for new applications in media production, virtual reality, and augmented reality.

Theoretically, GenProp raises interesting questions about the future of generative models and their applications across domains, particularly how they might be further generalized and applied to even more complex video manipulation tasks. Future research might explore expanding the framework's capabilities to accommodate multiple keyframe edits and investigate the broader array of video tasks that generative models could potentially support.

In conclusion, GenProp represents a significant step towards more efficient, scalable, and generalizable video editing techniques, setting a precedent for the continued evolution of generative approaches in computer vision applications.