Video-to-Music generation seeks to generate musically appropriate background music that enhances audiovisual immersion for videos. However, current approaches suffer from two critical limitations: 1) incomplete representation of video details, leading to weak alignment, and 2) inadequate temporal and rhythmic correspondence, particularly in achieving precise beat synchronization. To address the challenges, we propose Video Echoed in Music (VeM), a latent music diffusion that generates high-quality soundtracks with semantic, temporal, and rhythmic alignment for input videos. To capture video details comprehensively, VeM employs a hierarchical video parsing that acts as a music conductor, orchestrating multi-level information across modalities. Modality-specific encoders, coupled with a storyboard-guided cross-attention mechanism (SG-CAtt), integrate semantic cues while maintaining temporal coherence through position and duration encoding. For rhythmic precision, the frame-level transition-beat aligner and adapter (TB-As) dynamically synchronize visual scene transitions with music beats. We further contribute a novel video-music paired dataset sourced from e-commerce advertisements and video-sharing platforms, which imposes stricter transition-beat synchronization requirements. Meanwhile, we introduce novel metrics tailored to the task. Experimental results demonstrate superiority, particularly in semantic relevance and rhythmic precision.

In addressing the challenge of interpretability and generalizability of artificial music intelligence, this article introduces a novel symbolic representation that amalgamates both explicit and implicit musical information across diverse traditions and granularities. Utilizing a hierarchical and-or graph representation, the model employs nodes and edges to encapsulate a broad spectrum of musical elements, including structures, textures, rhythms, and harmonies. This hierarchical approach expands the representability across various scales of music. This representation serves as the foundation for an energy-based model, uniquely tailored to learn musical concepts through a flexible algorithm framework relying on the minimax entropy principle. Utilizing an adapted Metropolis–Hastings sampling technique, the model enables fine-grained control over music generation. Through a comprehensive empirical evaluation, this novel approach demonstrates significant improvements in interpretability and controllability compared to existing methodologies. This study marks a substantial contribution to the fields of music analysis, composition, and computational musicology.

Automatically generating video background music mitigates the inefficiency and time-consuming drawbacks of current manual video editing. Two key challenges hinder the expansion of the inception of video-to-music tasks. 1) Limited availability of high-quality video–music datasets and annotations. 2) Absence of music generation methods that consider actual musicality, which are controlled by interpretable factors based on music theory. In the article, we propose video echoed in harmony (VEH), a method for learning and sampling video-integrated chord progression sequences. Our approach adopts harmony, represented by chord progressions that are aligned with various music formats [musical instrument digital interface (MIDI), audio, and score], imitating chord precedence in human music composition. Visual-language models link visual features to chord progressions through genre labels and descriptive words in generated textualized videos. The two aforementioned features collectively obviate the necessity of extensive video–music paired data. Besides, an energy-based chord progression learning and sampling algorithm quantifies abstract harmony impressions to statistical features, serving as interpretable factors for the controllable music generation based on music theory. Experimental results demonstrate that the proposed method outperforms the state-of-the-art, producing a superior music alignment for the given video.

In this paper, we touch on the problem of markerless multi-modal human motion capture especially for string performance capture which involves inherently subtle hand-string contacts and intricate movements. To fulfill this goal, we first collect a dataset, named String Performance Dataset (SPD), featuring cello and violin performances. The dataset includes videos captured from up to 23 different views, audio signals, and detailed 3D motion annotations of the body, hands, instrument, and bow. Moreover, to acquire the detailed motion annotations, we propose an audio-guided multi-modal motion capture framework that explicitly incorporates hand-string contacts detected from the audio signals for solving detailed hand poses. This framework serves as a baseline for string performance capture in a completely markerless manner without imposing any external devices on performers, eliminating the potential of introducing distortion in such delicate movements. We argue that the movements of performers, particularly the sound-producing gestures, contain subtle information often elusive to visual methods but can be inferred and retrieved from audio cues. Consequently, we refine the vision-based motion capture results through our innovative audio-guided approach, simultaneously clarifying the contact relationship between the performer and the instrument, as deduced from the audio. We validate the proposed framework and conduct ablation studies to demonstrate its efficacy. Our results outperform current state-of-the-art vision-based algorithms, underscoring the feasibility of augmenting visual motion capture with audio modality. To the best of our knowledge, SPD is the first dataset for musical instrument performance, covering fine-grained hand motion details in a multi-modal, large-scale collection. It holds significant implications and guidance for string instrument pedagogy, animation, and virtual concerts, as well as for both musical performance analysis and generation. Our code and SPD dataset are available at https://github.com/Yitongishere/string_performance.