Multi-Condition Guided V2M Generation

• The paper introduces a dual-stage training strategy that enables precise video-to-music mapping through adaptive temporal alignment and multi-condition fusion.
• It employs hierarchical video feature extraction with dynamic modules like FGFS, PTAA, and DCF to refine feature alignment and synchronization.
• Experimental results demonstrate improved music-video correspondence and enhanced user control, validated by objective metrics and subjective listening tests.

A multi-condition guided Video-to-Music (V2M) generation framework is a system that enables the precise and controllable generation of music from video by incorporating multiple, often time-varying, conditional signals such as beat, melody, intensity, and emotion. These frameworks address the limitations of conventional V2M baselines, which typically rely exclusively on visual features and lack mechanisms for fine-grained user control or explicit alignment with video dynamics, leading to unsatisfactory user experiences. The core advance is the introduction of architectures and training paradigms that allow for the dynamic integration of multiple control streams, adaptive temporal alignment between video and music, and robust user-driven customization.

1. Architectural Overview of Multi-Condition Guided V2M Generation

The multi-condition guided V2M architecture employs a two-stage training strategy comprising: (i) a pre-training stage for learning V2M fundamentals and temporal alignment, and (ii) a fine-tuning stage for enabling multi-condition, time-varying control over the generated music (Wu et al., 28 Jul 2025).

Key Components

• Hierarchical Video Feature Extraction: Three-level feature extractors produce patch-level, frame-level (e.g., via CLIP), and context-aware video features (e.g., via VideoMAE V2), each preserving different granularities of information.
• Video Feature Aggregation (VFA) Module: Aggregates frame-level features into a unified global representation injected as a high-level tonal cue for music generation.
• Fine-Grained Feature Selection (FGFS) Module: Dynamically filters and fuses patch- and context-level features using learnable parameters $\alpha$ and $\beta$, enhancing feature alignment.
• Progressive Temporal Alignment Attention (PTAA) Module: Employs a decoder-only Transformer architecture with hierarchical “4D-Blocks,” leveraging adaptive temporal masks for precise local and global alignment between video segments and music tokens.
• Dynamic Conditional Fusion (DCF) Module: Integrates multiple time-varying control signals by projecting each to a shared latent space, partitioned into temporal patches, then adaptively weighted for fusion.
• Control-Guided Decoder (CGD) Module: Incorporates control features directly into the music generator’s attention modules via a parallel, trainable branch, following a ControlNet-style mechanism.

2. Multi-Condition Feature Integration and Temporal Alignment

The architecture operates with two main forms of conditioning:

• Visual Conditioning: Features from different video abstraction levels are fused, with patch-level features adaptively selected and contextually refined. The FGFS module computes fused key–value pairs as:

$$K_i^c = \alpha_i K_i^c + (1-\alpha_i) K_i^f, \quad V_i^c = \alpha_i V_i^c + (1-\alpha_i) V_i^f,$$

followed by a cross-attention process that aligns them with temporal music tokens.

• Time-Varying Conditional Control: Scalar or sequence-valued control streams (beat, melody, intensity, emotion) are linearly projected and concatenated. The DCF module applies context-aware weighting over time via a dual-context weight generator $\mathcal{G}$, yielding:

$C_{out} = \alpha \odot C'_{in},$

where $\odot$ denotes element-wise multiplication, and the weights $\alpha$ are computed by adaptive pooling and convolutional operations on both intra- and inter-patch contexts.

• Progressive Temporal Alignment: Employing a Transformer with 4D-blocks and adaptive temporal masking in attention layers, each music token can only attend to relevant video segments:

$$\text{MaskCA}(Q, K, V, M) = \text{softmax}\left(\frac{Q K^\top}{\sqrt{d_k}} + M\right) V$$

where $M$ is an adaptive mask restricting the temporal window for each music token.

3. Training Strategy

Stage 1: Pre-Training

• The system learns a basic mapping from aggregated video features (via VFA, FGFS, PTAA) to music sequences.
• Attention modules use large-to-small temporal windows to enable both global musical tone setting and fine-grained correspondence with visual rhythm.

Stage 2: Multi-Condition Fine-Tuning

• All backbone pre-trained parameters are frozen.
• The DCF and CGD modules are trained to inject multiple control signals and modulate music generation.
• Complementary masking randomly drops segments of control signals during training, improving robustness and enabling partial user control.

This two-phase schedule allows the core V2M mapping to remain stable while additional control branches learn to influence generation without catastrophic forgetting or interference.

4. Mathematical Formulation

The main mathematical relationships governing module operation include:

• FGFS feature fusion:

$$K_i^c = \alpha_i K_i^c + (1-\alpha_i) K_i^f, \quad V_i^c = \alpha_i V_i^c + (1-\alpha_i) V_i^f \ K_i^f = \beta_i K_i^c + (1-\beta_i) K_i^f, \quad V_i^f = \beta_i V_i^c + (1-\beta_i) V_i^f$$

• Temporal Attention Masking:

$$M_j = \begin{cases} 0, & \text{if } k_j\gamma \leq i < k_j(\gamma+1) \ -\infty, & \text{otherwise} \end{cases}$$

• CGD Condition Injection:

$$M_c = \mathcal{F}(x; \theta) + L(\mathcal{F}(x + L(C; \theta_{l1}); \theta_c); \theta_{l2})$$

where $x$ is the prior-layer input token sequence, $C$ is the fused condition, $L$ introduces zero-initialized linear transformations, and $\mathcal{F}$ is an attention module.

5. Experimental Results and Evaluation

The proposed method was evaluated on both objective and subjective criteria using publicly available V2M datasets.

Objective Metrics:

• Standard Audio Generation: Kullback-Leibler Divergence (KL), Fréchet Audio Distance (FAD), Fréchet Distance (FD), CLAP Score (audio similarity), Diversity, and Coverage.
• Synchronization and Control Adherence: ImageBind Score, Cross-Modal Relevance, Temporal Alignment, Pearson/Concordance Correlation Coefficient (emotion/intensity), Melody Accuracy, and Rhythm F1.

Subjective Metrics:

• Listening tests assessed Overall Music Quality (OMQ), Music-Video Correspondence (MVC), and User Expectation Conformity (UEC).

Key findings:

• Multi-condition V2M achieves tighter alignment of music with video rhythm and scene dynamics.
• The system supports fine control, successfully adjusting music properties (e.g., intensity, emotion) dynamically in response to input conditions.
• Subjective listening studies indicate improved satisfaction with users’ control instructions and expectations relative to previous "black-box" methods.

6. User-Centric Control and Generalization

By decoupling control signal injection into modular DCF and CGD layers, users can specify any subset of control sequences (e.g., only emotion curve or detailed beat structure), and the network infers the rest, maintaining musicality and video agreement. Complementary masking during training improves the model’s capacity to handle missing or partial controls. This design supports use cases such as interactive editing—fine-tuning music after initial generation via incremental, user-specified condition modifications.

7. Broader Implications and Future Directions

The multi-condition guided V2M framework introduces a new paradigm for controllable music generation tightly coupled to visual dynamics, enabling trajectories in creative media, film scoring, gaming, and assistive artistic applications. The modular design facilitates future integration of additional conditions (e.g., speech, text, external style references), hybridization with diffusion-based generative models, and application to broader cross-modal generation problems beyond V2M.

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In summary, the multi-condition guided V2M generation framework represents a technical advance in integrating hierarchical visual modeling, progressive temporal alignment, and dynamic, modular conditional control for flexible and precise video-driven music generation, validated through rigorous quantitative and qualitative evaluation (Wu et al., 28 Jul 2025).