Vision-to-Music Generation: A Survey
Survey on vision-to-music generation, analyzing technical challenges and providing datasets and evaluation metrics.
Key Findings
Methodology
This paper systematically reviews the research progress in vision-to-music generation, analyzing technical characteristics and core challenges for three input types (general videos, human movement videos, images) and two output types (symbolic music, audio music). It summarizes existing methodologies from an architectural perspective and provides a detailed review of common datasets and evaluation metrics.
Key Results
- The study finds that vision-to-music generation has broad application prospects in film scoring and short video creation.
- Existing methods face challenges in handling complex dynamic relationships between vision and music.
- Symbolic music generation methods offer better controllability but are limited in emotional expression.
Significance
Vision-to-music generation is a significant branch of multimodal AI with vast application prospects. This paper fills the gap in current research by providing a comprehensive survey, laying the groundwork for future innovation.
Technical Contribution
The paper provides a detailed analysis of the technical characteristics of vision-to-music generation, identifies core challenges, and summarizes existing methods and datasets, offering a reference for future research.
Novelty
This is the first comprehensive survey on vision-to-music generation, systematically analyzing input-output types and method architectures.
Limitations
- Existing methods still struggle with complex vision-music relationships.
- Standardization of datasets and benchmarks needs improvement.
Future Work
Future research can explore dataset standardization, method innovation, and expansion of application scenarios.
AI Executive Summary
Vision-to-music generation is an important branch of multimodal AI with broad application prospects such as film scoring, short video creation, and dance music synthesis. However, compared to the rapid development of modalities like text and images, research in vision-to-music is still in its preliminary stage due to its complex internal structure and the difficulty of modeling dynamic relationships with video. Existing surveys focus on general music generation without comprehensive discussion on vision-to-music.
This paper systematically reviews the research progress in the field of vision-to-music generation. It first analyzes the technical characteristics and core challenges for three input types: general videos, human movement videos, and images, as well as two output types of symbolic music and audio music. It then summarizes existing methodologies from the architecture perspective and provides a detailed review of common datasets and evaluation metrics. Finally, it discusses current challenges and promising directions for future research.
We hope our survey can inspire further innovation in vision-to-music generation and the broader field of multimodal generation in academic research and industrial applications. We have also established a GitHub repository to continuously maintain the latest papers in this field.
Deep Analysis
Background
Recent advances in multimodal artificial intelligence have witnessed substantial progress in generating and understanding content for modalities like text, images, video, and speech. Music generation, as an important part of this multimodal ecosystem, has also seen remarkable development. Vision-to-music generation has garnered particular interest due to its practical applications in film scoring, short video platforms, and music accompaniment.
Core Problem
The core problem of vision-to-music generation lies in aligning rich visual cues with musical structure and handling the multifaceted nature of music generation. This task's complexity makes it more challenging than common text-to-music tasks.
Innovation
The core innovation of this paper is the first systematic survey of the vision-to-music generation field, analyzing the technical characteristics and core challenges of input-output types and summarizing existing method architectures, providing a reference for future research.
Methodology
- �� Analyze input types: general videos, human movement videos, images
- �� Analyze output types: symbolic music, audio music
- �� Summarize existing method architectures: vision encoding, vision-music projection, music generation
- �� Provide a detailed review of common datasets and evaluation metrics
Experiments
The paper does not involve specific experimental design but provides a systematic review of existing research, analyzing the strengths and weaknesses of different methods and their applicable scenarios.
Results
The study finds that vision-to-music generation has broad application prospects in film scoring and short video creation. Existing methods face challenges in handling complex dynamic relationships between vision and music.
Applications
Vision-to-music generation can be used in film scoring, short video creation, and dance music synthesis, with broad application prospects.
Limitations & Outlook
Existing methods still struggle with complex vision-music relationships, and standardization of datasets and benchmarks needs improvement.
Plain Language Accessible to non-experts
Imagine watching a movie where each scene has different emotions and rhythms. Vision-to-music generation is like a smart musician that automatically creates suitable background music based on the movie's visuals. For example, during an intense chase scene, this musician would create tense and exciting music; during a romantic scene, it would create soft and gentle music. This process customizes a soundtrack for the movie, enhancing the viewer's experience.
ELI14 Explained like you're 14
Imagine you're playing a game, and every scene in the game has different music. Vision-to-music generation is like a DJ in the game that automatically plays the right music based on the game's visuals. For example, when you're fighting monsters, it plays intense music; when you're exploring a mysterious forest, it plays mysterious music. This makes playing the game more fun!
Glossary
Multimodal
Involves multiple sensory modalities like vision, hearing, and touch.
Used in vision-to-music generation to handle both visual and musical modalities.
Symbolic Music
Music represented as discrete elements like notes and chords.
Used for generating controllable music outputs.
Audio Music
Music generated directly in audio form, often more expressive.
Used for generating music with greater emotional depth.
Vision Encoding
The process of extracting features from input video or image.
Used in vision-to-music generation to capture visual semantic information.
Vision-Music Projection
The process of mapping visual features into the music space.
Used to align visual and musical features during generation.
Open Questions Unanswered questions from this research
- 1 How to better handle complex dynamic relationships in vision-to-music generation?
- 2 How to standardize datasets to improve model generalization?
Applications
Immediate Applications
Film Scoring
Automatically generate suitable background music for film scenes, reducing time and cost of manual scoring.
Long-term Vision
Personalized Music Creation
Automatically generate personalized music based on user-uploaded videos or images, enhancing user experience.
Abstract
Vision-to-music Generation, including video-to-music and image-to-music tasks, is a significant branch of multimodal artificial intelligence demonstrating vast application prospects in fields such as film scoring, short video creation, and dance music synthesis. However, compared to the rapid development of modalities like text and images, research in vision-to-music is still in its preliminary stage due to its complex internal structure and the difficulty of modeling dynamic relationships with video. Existing surveys focus on general music generation without comprehensive discussion on vision-to-music. In this paper, we systematically review the research progress in the field of vision-to-music generation. We first analyze the technical characteristics and core challenges for three input types: general videos, human movement videos, and images, as well as two output types of symbolic music and audio music. We then summarize the existing methodologies on vision-to-music generation from the architecture perspective. A detailed review of common datasets and evaluation metrics is provided. Finally, we discuss current challenges and promising directions for future research. We hope our survey can inspire further innovation in vision-to-music generation and the broader field of multimodal generation in academic research and industrial applications. To follow latest works and foster further innovation in this field, we are continuously maintaining a GitHub repository at https://github.com/wzk1015/Awesome-Vision-to-Music-Generation.