An Inverse Partial Optimal Transport Framework for Music-guided Movie Trailer Generation (2407.19456v2)

Abstract: Trailer generation is a challenging video clipping task that aims to select highlighting shots from long videos like movies and re-organize them in an attractive way. In this study, we propose an inverse partial optimal transport (IPOT) framework to achieve music-guided movie trailer generation. In particular, we formulate the trailer generation task as selecting and sorting key movie shots based on audio shots, which involves matching the latent representations across visual and acoustic modalities. We learn a multi-modal latent representation model in the proposed IPOT framework to achieve this aim. In this framework, a two-tower encoder derives the latent representations of movie and music shots, respectively, and an attention-assisted Sinkhorn matching network parameterizes the grounding distance between the shots' latent representations and the distribution of the movie shots. Taking the correspondence between the movie shots and its trailer music shots as the observed optimal transport plan defined on the grounding distances, we learn the model by solving an inverse partial optimal transport problem, leading to a bi-level optimization strategy. We collect real-world movies and their trailers to construct a dataset with abundant label information called CMTD and, accordingly, train and evaluate various automatic trailer generators. Compared with state-of-the-art methods, our IPOT method consistently shows superiority in subjective visual effects and objective quantitative measurements.

• The paper introduces an IPOT framework that leverages cross-modal alignment to synchronize visual movie shots with corresponding music cues.
• It employs a two-tower encoder and a Sinkhorn-based matching network to optimally select and sequence shots based on latent audio and visual features.
• Quantitative metrics and the new CMTD dataset validate its superior performance with improved precision and F1-scores in automated trailer generation.

An Inverse Partial Optimal Transport Framework for Music-Guided Movie Trailer Generation

The paper introduces a novel framework employing inverse partial optimal transport (IPOT) for the task of music-guided movie trailer generation, an intricate problem due to the subjective nature of creative film editing. The proposed framework models trailer generation as the selection and organization of key movie shots guided by a soundtrack, planned through cross-modal alignment of visual and audio latent representations. This paper offers insights into leveraging optimal transport (OT) theory to bridge visual-acoustic modalities to automate trailer creation, a task traditionally handled by human editors.

The IPOT framework operates through a modular architecture whereby a two-tower encoder processes visual and audio inputs separately, generating latent representations that serve as the basis for selecting and aligning shots. Introducing a cross-attention mechanism for conditional shot selection allows the model to adaptively emphasize movie scenes that align aesthetically and semantically with the background music. Onto this base, a Sinkhorn-based matching network formulates an OT problem to derive a doubly-stochastic plan that aligns selected movie shots with the soundtrack's audio shots.

A significant contribution of this work is in demonstrating how the entropic regularization within the Sinkhorn algorithm enables an efficient computation of the optimal transport plan, providing a balance between precision of alignment and diversity of trailer content. This mechanism facilitates the adaptive construction of movie trailers that maintain logical narrative progression synchronized with music beat and emotion.

Quantitatively, the IPOT framework showcases improvements over existing state-of-the-art trailer generation models and video summarization approaches. Objective metrics, such as Precision and F1-Scores across different shot selection levels, indicate a superior ability in effectively mimicking professionally crafted trailers. Moreover, subjective evaluations through user studies corroborate these findings as the framework generates content more aligned with viewer preferences in terms of rhythm and narrative compatibility.

The introduction of the Comprehensive Movie-Trailer Dataset (CMTD), an extensive repository featuring detailed shot-level alignments of trailers and their movie counterparts, is another critical aspect of this research. CMTD's inclusion of rich metadata, such as subtitles and narrative turning points, poses significant potential for future research into more nuanced video understanding tasks.

Furthermore, the paper discusses an exploration of hyperparameters within the IPOT framework, ensuring robust performance across varying input distributions. Characterizing flexibility in tuning allows the model to adapt efficiently to the plethora of cinematic styles and rhythmic compositions characterizing contemporary trailers.

Speculatively, the broader deployment of the IPOT framework could revolutionize automatic content generation across multimedia applications, affecting industries from film marketing to interactive entertainment. As AI continues evolving in its capacity to understand and interpret complex audiovisual cues, this framework represents a significant step toward more human-like AI capabilities in creative domains.

In conclusion, the research presents a structured approach to automate trailer generation infused with theoretical rigor and practical applicability. While there remain challenges in equating human artistry's subtlety, the results reveal substantial promise in transcending beyond conventional automation boundaries, creating frameworks where AI not only assists but co-creates compelling narrative expressions with balance and depth. Future works might involve expanding the dataset and leveraging richer semantic annotations to enhance model performance further, potentially incorporating generative models for truly dynamic trailer customization.