Flow-Guided Transformer for Video Inpainting (2208.06768v1)

Abstract: We propose a flow-guided transformer, which innovatively leverage the motion discrepancy exposed by optical flows to instruct the attention retrieval in transformer for high fidelity video inpainting. More specially, we design a novel flow completion network to complete the corrupted flows by exploiting the relevant flow features in a local temporal window. With the completed flows, we propagate the content across video frames, and adopt the flow-guided transformer to synthesize the rest corrupted regions. We decouple transformers along temporal and spatial dimension, so that we can easily integrate the locally relevant completed flows to instruct spatial attention only. Furthermore, we design a flow-reweight module to precisely control the impact of completed flows on each spatial transformer. For the sake of efficiency, we introduce window partition strategy to both spatial and temporal transformers. Especially in spatial transformer, we design a dual perspective spatial MHSA, which integrates the global tokens to the window-based attention. Extensive experiments demonstrate the effectiveness of the proposed method qualitatively and quantitatively. Codes are available at https://github.com/hitachinsk/FGT.

• The paper introduces a novel flow-guided transformer that integrates completed optical flows into a transformer architecture to boost video inpainting accuracy.
• It employs a two-phase approach, using a flow completion network followed by spatial multi-head self-attention to guide the inpainting process.
• Empirical results on Youtube-VOS and DAVIS demonstrate significant improvements in PSNR, SSIM, and LPIPS, underscoring robust spatiotemporal performance.

Analysis of "Flow-Guided Transformer for Video Inpainting"

The research paper "Flow-Guided Transformer for Video Inpainting" authored by Kaidong Zhang, Jingjing Fu, and Dong Liu presents an advanced method for video inpainting—a task that fills corrupted regions in video frames with contextually appropriate and spatiotemporally coherent content. This new framework leverages motion information obtained from optical flows, incorporating these insights into the self-attention mechanism of transformers. The work is motivated by the observed limitation in previous transformer-based video inpainting efforts, which typically focus only on appearance features and neglect the crucial motion information that optical flows can provide.

Methodology

The paper introduces a Flow-Guided Transformer (FGT), which is an intricate combination of optical flow-guiding mechanisms with transformer architecture, tailored for video inpainting. The core innovation lies in using motion discrepancies revealed by optical flows to better guide the attention retrieval processes in transformers, thus achieving higher fidelity inpainting results. The method is divided into two distinct phases:

1. Flow Completion Network: To handle initially corrupted optical flows, the authors propose a novel flow completion network. This network completes corrupted flows using local temporal reference flows, effectively utilizing the content within a short temporal window. This approach contrasts with previously employed stacking strategies in earlier work like DFGVI and single flow completion methods in FGVC. They incorporate spatial-temporal partitioning using P3D blocks and propose an edge loss specifically aimed at improving the reconstruction quality of flow boundaries.
2. Flow-Guided Transformer: Once optical flows are completed, the FGT aims to synthesize missing video content. The paper details the adoption of a separated temporal and spatial transformer mechanism, integrating the completed flows only within spatial transformers to guide attention more accurately. The researchers design a flow-reweight module that adaptively moderates the influence of completed flows on each spatial transformer—a necessary feature due to potential inaccuracies in flow completion.

The transformer efficiency is enhanced by implementing a window partitioning strategy. The spatial transformer is equipped with a dual perspective spatial multi-head self-attention (MHSA), allowing it to attend both local window-focused and global-scale content by incorporating global tokens into attention operations.

Results and Implications

Through extensive experimentation, the paper showcases the substantial gain in inpainting accuracy both qualitatively and quantitatively. The proposed method demonstrates superior performance over contemporary baselines regarding parameters such as PSNR, SSIM, and LPIPS across standard datasets like Youtube-VOS and DAVIS. This improvement underscores the effective use of motion information through completed optical flows in guiding transformer attention mechanisms to improve video inpainting results.

Future Directions

The implications of this research are multifaceted. Practically, the method could be adapted to enhance a robust and efficient preprocessing step in various video-editing applications, such as the removal of unwanted objects or artifacts from video frames with high perceptual quality. Theoretically, the introduction of flow-guided attention systems could catalyze further exploration of how motion information could be harnessed across other video-related tasks where temporal coherence is crucial, such as video stabilization or retargeting.

Looking forward, potential future developments could focus on further improving flow completion accuracy, especially under challenging scenarios like complex motion patterns or substantial scene changes. Additionally, exploring alternative forms of integrating motion information beyond optical flows might yield even more robust multi-modal attention mechanisms, pushing the boundaries of state-of-the-art video inpainting.