Video Inpainting of Complex Scenes (1503.05528v2)

Abstract: We propose an automatic video inpainting algorithm which relies on the optimisation of a global, patch-based functional. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Furthermore, we achieve this in an order of magnitude less execution time with respect to the state-of-the-art. We are also able to achieve good quality results on high definition videos. Finally, we provide specific algorithmic details to make implementation of our algorithm as easy as possible. The resulting algorithm requires no segmentation or manual input other than the definition of the inpainting mask, and can deal with a wider variety of situations than is handled by previous work. 1. Introduction. Advanced image and video editing techniques are increasingly common in the image processing and computer vision world, and are also starting to be used in media entertainment. One common and difficult task closely linked to the world of video editing is image and video " inpainting ". Generally speaking, this is the task of replacing the content of an image or video with some other content which is visually pleasing. This subject has been extensively studied in the case of images, to such an extent that commercial image inpainting products destined for the general public are available, such as Photoshop's " Content Aware fill " [1]. However, while some impressive results have been obtained in the case of videos, the subject has been studied far less extensively than image inpainting. This relative lack of research can largely be attributed to high time complexity due to the added temporal dimension. Indeed, it has only very recently become possible to produce good quality inpainting results on high definition videos, and this only in a semi-automatic manner. Nevertheless, high-quality video inpainting has many important and useful applications such as film restoration, professional post-production in cinema and video editing for personal use. For this reason, we believe that an automatic, generic video inpainting algorithm would be extremely useful for both academic and professional communities.

• The paper presents an automatic video inpainting method that accelerates patch matching by extending the PatchMatch algorithm to spatio-temporal contexts.
• The paper incorporates texture descriptors and robust affine motion estimation to accurately reconstruct dynamic textures and moving backgrounds.
• The approach significantly reduces execution time while delivering high-quality inpainting results with minimal manual intervention.

Video Inpainting of Complex Scenes: A Summary

The paper "Video Inpainting of Complex Scenes" proposes an advanced and efficient automatic video inpainting technique. This algorithm stands out due to its capability to address a variety of challenging scenarios inherent in video inpainting, such as dynamic textures, moving objects, and fluctuating backgrounds. The authors emphasize that despite the complex nature of these tasks, their approach achieves substantial improvements in execution time compared to previously established methods.

The proposed method leverages a global, patch-based functional optimization framework, that significantly reduces the computational time scale, rendering it feasible for real-world applications across high-definition video content. Central to this advancement is the bypassing of manual intervention typical of traditional approaches, requiring only the specification of an inpainting mask while automatically handling the reconstruction of complex video elements.

Key Methodological Contributions

Here are the principal elements that distinguish this algorithm:

• Acceleration of Patch Matching: By extending the PatchMatch method to accommodate spatio-temporal contexts, the algorithm significantly accelerates the nearest-neighbor search. This efficiency allows processing even in extensive video sequences in a substantially reduced timeframe.
• Incorporating Texture Features: The inclusion of texture descriptors in the patch distance calculation addresses difficulties in correctly identifying dynamic textures. This advancement is vital for accurately reconstructing textured regions of videos.
• Handling Moving Backgrounds: The algorithm incorporates robust affine motion estimation to manage moving backgrounds effectively. This feature ensures consistent results where background elements are not static—a frequent occurrence in videos captured using handheld cameras.
• Initialization and Multi-resolution Schemes: The use of an onion-peel layering technique guides the initialization process at coarser levels, ensuring that initialization does not dictate the final inpainting quality adversely. Additionally, a detailed multi-resolution approach is integrated, which addresses potential issues concerning the disparity in resolution levels.

Implications and Future Prospects

The newly developed algorithm maintains accuracy and reduces execution times, achieving a balance necessary for practical and large-scale applications in areas such as video post-production and restoration. Its adaptability to automatically handle multiple scene elements, without necessitating separate algorithms or manual segmentation, reflects its potential for widespread use in media manipulation tasks.

Looking forward, further reductions in computational time could be explored, potentially via dimensionality reduction techniques applied to the patch space. Additionally, advancing methodologies to handle extended occlusions of moving objects might enhance the robustness and versatility of video inpainting approaches.

This research marks a pivotal step towards making video inpainting an accessible, efficient tool for both professional and personal media editing. By building upon and surpassing the capabilities of previous methods, this work enriches the toolkit available for advanced image processing applications.