VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation (2303.08340v3)

Abstract: We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently estimates bi-directional optical flows for multiple frames that are available in videos by sufficiently exploiting temporal cues. We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner. The information of the frame triplet is iteratively fused onto the center frame. To extend TROF for handling more frames, we further propose a MOtion Propagation (MOP) module that bridges multiple TROFs and propagates motion features between adjacent TROFs. With the iterative flow estimation refinement, the information fused in individual TROFs can be propagated into the whole sequence via MOP. By effectively exploiting video information, VideoFlow presents extraordinary performance, ranking 1st on all public benchmarks. On the Sintel benchmark, VideoFlow achieves 1.649 and 0.991 average end-point-error (AEPE) on the final and clean passes, a 15.1% and 7.6% error reduction from the best-published results (1.943 and 1.073 from FlowFormer++). On the KITTI-2015 benchmark, VideoFlow achieves an F1-all error of 3.65%, a 19.2% error reduction from the best-published result (4.52% from FlowFormer++). Code is released at \url{https://github.com/XiaoyuShi97/VideoFlow}.

• The paper introduces VideoFlow, which combines the TROF module for bi-directional flow estimation with the MOP module for propagating motion cues across frames, reducing AEPE by up to 19.2%.
• It employs iterative refinement and recurrent mechanisms to integrate temporal dynamics beyond traditional two-frame methods, effectively handling occlusions and rapid motion.
• This approach sets a new benchmark on datasets like Sintel and KITTI-2015, offering robust motion analysis for applications such as object detection, video synthesis, and action recognition.

An Analysis of VideoFlow: Temporal Cues in Multi-frame Optical Flow Estimation

The paper presents VideoFlow, a sophisticated approach to optical flow estimation which departs from traditional two-frame methodologies to leverage the temporal information inherent in sequences of video frames. The authors introduce compelling advancements in both model architecture and cross-frame data integration to enhance optical flow accuracy, specifically designed for sequences longer than two frames. This approach manifests in demonstrably superior performance across leading optical flow benchmarks.

VideoFlow primarily utilizes two innovative components: the TRi-frame Optical Flow (TROF) module and the MOtion Propagation (MOP) module. The TROF module is a novel construct aimed at handling bi-directional optical flow estimation across three consecutive frames. It achieves this by iteratively refining flow predictions to drive the alignment and integration of motion across the triplet, focusing on the significance of the center frame as a temporal bridge. The TROF fully integrates the bi-directional motion information through a recurrent mechanism, synthesizing flow trajectories from the center to its adjacent frames, inherently capturing transitional dynamics that traditional pairwise methods could miss.

The MOP module extends the scope of TROF for longer frame sequences by linking multiple TROF units. It efficiently warps and propagates motion features across these units, ensuring multi-frame temporal cues are not merely sequential but integrated into flow prediction. This propagation mechanism results in an expansion of the temporal receptive field, allowing VideoFlow to comprehensively leverage broader temporal contexts in refining optical flow estimates. This facilitates effective prediction even in scenarios challenging to earlier methods, such as occlusions and frames with rapid motion or blur.

Quantitatively, VideoFlow sets a new benchmark by achieving the lowest average end-point-error (AEPE) on premier datasets including Sintel and KITTI-2015. The substantial reduction in AEPE—by as much as 19.2% on KITTI-2015 compared to previous state-of-the-art methods—underscores the framework's technical edge. VideoFlow’s successful capturing of temporal dynamics leads to finer-grained motion estimations, and its success at reducing errors in complex and fast-changing scenes illustrates the robustness of this approach.

A key strength of the paper lies in its rigorous evaluation framework. It positions VideoFlow's performance against both existing two-frame and limited multi-frame models, demonstrating consistent superiority. For instance, the compared models, often reliant on sequential reasoning or limited contextual foresight, lack the comprehensive integration and iterative refinement mechanisms that VideoFlow employs.

The implications of this research are multifaceted, with immediate applications in advancing video processing tasks such as object detection, video synthesis, and action recognition, where precise understanding of motion is crucial. Furthermore, the methodological rigor and innovations introduced could refine theoretical underpinnings in temporal data modeling and motion-centric neural computations.

Looking ahead, the paper suggests fertile ground for further exploration within the domain of optical flow estimation. Potential future developments could extend beyond the methodologies presented, into deeper integration with machine learning topics like temporal attention mechanisms, or exploring cross-task synergy with video frame interpolation and dynamic scene understanding.

Overall, VideoFlow represents a significant advancement in the field of optical flow estimation, effectively utilizing temporal cues for refined motion analysis in video sequences, and sets a new standard for further research and applications within both academia and industry.