INVE: Interactive Neural Video Editing (2307.07663v1)

Abstract: We present Interactive Neural Video Editing (INVE), a real-time video editing solution, which can assist the video editing process by consistently propagating sparse frame edits to the entire video clip. Our method is inspired by the recent work on Layered Neural Atlas (LNA). LNA, however, suffers from two major drawbacks: (1) the method is too slow for interactive editing, and (2) it offers insufficient support for some editing use cases, including direct frame editing and rigid texture tracking. To address these challenges we leverage and adopt highly efficient network architectures, powered by hash-grids encoding, to substantially improve processing speed. In addition, we learn bi-directional functions between image-atlas and introduce vectorized editing, which collectively enables a much greater variety of edits in both the atlas and the frames directly. Compared to LNA, our INVE reduces the learning and inference time by a factor of 5, and supports various video editing operations that LNA cannot. We showcase the superiority of INVE over LNA in interactive video editing through a comprehensive quantitative and qualitative analysis, highlighting its numerous advantages and improved performance. For video results, please see https://gabriel-huang.github.io/inve/

• The paper introduces INVE, a framework that boosts video editing speed by fivefold using multi-resolution hash grid encoding.
• It employs bi-directional mapping to achieve precise texture tracking and supports layered editing for sketches, textures, and metadata.
• Quantitative experiments reveal a reduction in training iterations from 300,000 to 12,000, significantly enhancing reconstruction quality and interactivity.

Interactive Neural Video Editing: A Technical Overview

The paper "INVE: Interactive Neural Video Editing" introduces a novel solution for video editing, leveraging recent advancements in neural representations to facilitate real-time, user-friendly interactions. The proposed method, INVE, is positioned as an improvement upon the Layered Neural Atlas (LNA) approach, overcoming notable limitations by enhancing processing speed and editing flexibility.

Main Contributions and Methodology

The INVE framework builds upon the concept of using neural networks to model video frames and atlases, offering significant enhancements that enable interactive video editing capabilities:

1. Improved Computational Efficiency: INVE employs a multi-resolution hash grid encoding, inspired by the Instant Neural Graphics Primitives (InstantNGP), which drastically accelerates both the training and inference phases. This results in a fivefold increase in processing speed compared to LNA, making the method suitable for real-time applications.
2. Bi-directional Mapping and Texture Tracking: The authors introduce bi-directional functions that map between the image-atlas and vice versa. This innovation supports more versatile editing actions, particularly enabling rigid texture tracking effects, which is crucial for operations like object or logo attachment that maintain spatial consistency across frames.
3. Enhanced Editing Capabilities with Layers: A layered approach for video editing is introduced, allowing separate editing layers for sketches, textures, and metadata adjustments. This provides a structured framework that aligns with established image editing paradigms, making the transition from image to video editing seamless for the user.
4. Vectorized Sketching: To address the aliasing artifacts and computational inefficiencies of frame-based sketching in LNA, INVE introduces vectorized sketching. This methodology utilizes control points to define sketches, reducing computational overhead and improving visual consistency across frames.

Quantitative and Qualitative Analysis

The paper demonstrates, through a set of experiments, the superiority of INVE over LNA in terms of performance metrics and user interaction quality. With a reduced training time down to approximately 12,000 iterations compared to LNA's 300,000, the method achieves high frame reconstruction quality and mapping accuracy. The practical outcomes in the form of reconstructing edited video frames indicate a significant enhancement in performance speed and user experience.

Implications and Speculative Directions

The developments presented in this research have substantial implications for both practical and theoretical pursuits in AI-driven media processing. On a practical level, INVE offers a user-friendly interface for non-professional users, democratizing video editing and potentially expanding the user base of advanced editing software. Theoretically, the integration of bi-directional mapping in neural representations may stimulate further research into more sophisticated and efficient neural pipelines for video and image processing tasks.

Looking towards the future, the adaptive architectures utilized in INVE could pave the way for broader applications in real-world editing software, potentially integrating seamlessly with commercial platforms. Further exploration into optimizing neural representations to handle higher-resolution content and incorporating advanced color and object editing technologies could expand the capabilities and applications of the framework.

In summary, "INVE: Interactive Neural Video Editing" contributes an innovative approach to video editing by building upon and enhancing existing neural representation frameworks. Through significant improvements in efficiency and editability, INVE sets a foundation for future explorations in interactive media manipulation, enhancing accessibility and capability for novice editors and researchers alike.