Video-based Music Generation

• URL: http://arxiv.org/abs/2602.07063v1
• Date: Thu, 05 Feb 2026 13:42:36 GMT
• Title: Video-based Music Generation
• Authors: Serkan Sulun,
• Abstract summary: This thesis presents EMSYNC, a fast, free, and automatic solution that generates music tailored to the input video.<n>Our model creates music that is emotionally and rhythmically synchronized with the video.<n>We show the generalization abilities of our method by obtaining state-of-the-art results on Ekman-6 and MovieNet.
• Score: 1.5229257192293202
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As the volume of video content on the internet grows rapidly, finding a suitable soundtrack remains a significant challenge. This thesis presents EMSYNC (EMotion and SYNChronization), a fast, free, and automatic solution that generates music tailored to the input video, enabling content creators to enhance their productions without composing or licensing music. Our model creates music that is emotionally and rhythmically synchronized with the video. A core component of EMSYNC is a novel video emotion classifier. By leveraging pretrained deep neural networks for feature extraction and keeping them frozen while training only fusion layers, we reduce computational complexity while improving accuracy. We show the generalization abilities of our method by obtaining state-of-the-art results on Ekman-6 and MovieNet. Another key contribution is a large-scale, emotion-labeled MIDI dataset for affective music generation. We then present an emotion-based MIDI generator, the first to condition on continuous emotional values rather than discrete categories, enabling nuanced music generation aligned with complex emotional content. To enhance temporal synchronization, we introduce a novel temporal boundary conditioning method, called "boundary offset encodings," aligning musical chords with scene changes. Combining video emotion classification, emotion-based music generation, and temporal boundary conditioning, EMSYNC emerges as a fully automatic video-based music generator. User studies show that it consistently outperforms existing methods in terms of music richness, emotional alignment, temporal synchronization, and overall preference, setting a new state-of-the-art in video-based music generation.

Related papers

• EmoCAST: Emotional Talking Portrait via Emotive Text Description [56.42674612728354]
  EmoCAST is a diffusion-based framework for precise text-driven emotional synthesis.<n>In appearance modeling, emotional prompts are integrated through a text-guided decoupled emotive module.<n>EmoCAST achieves state-of-the-art performance in generating realistic, emotionally expressive, and audio-synchronized talking-head videos.
  arXiv  Detail & Related papers  (2025-08-28T10:02:06Z)
• Let Your Video Listen to Your Music! [62.27731415767459]
  We propose a novel framework, MVAA, that automatically edits video to align with the rhythm of a given music track.<n>We modularize the task into a two-step process in our MVAA: aligning motion with audio beats, followed by rhythm-aware video editing.<n>This hybrid approach enables adaptation within 10 minutes with one on a single NVIDIA 4090 GPU using CogVideoX-5b-I2V as the backbone.
  arXiv  Detail & Related papers  (2025-06-23T17:52:16Z)
• Extending Visual Dynamics for Video-to-Music Generation [51.274561293909926]
  DyViM is a novel framework to enhance dynamics modeling for video-to-music generation.<n>High-level semantics are conveyed through a cross-attention mechanism.<n>Experiments demonstrate DyViM's superiority over state-of-the-art (SOTA) methods.
  arXiv  Detail & Related papers  (2025-04-10T09:47:26Z)
• Video Soundtrack Generation by Aligning Emotions and Temporal Boundaries [1.1743167854433303]
  EMSYNC is a video-based symbolic music generation model that aligns music with a video's emotional content and temporal boundaries.<n>We introduce boundary offsets, a novel temporal conditioning mechanism that enables the model to anticipate and align musical chords with scene cuts.<n>In subjective listening tests, EMSYNC outperforms state-of-the-art models across all subjective metrics, for music theory-aware participants as well as the general listeners.
  arXiv  Detail & Related papers  (2025-02-14T13:32:59Z)
• Emotion-Guided Image to Music Generation [0.5461938536945723]
  This paper presents an emotion-guided image-to-music generation framework. It produces music that aligns with the emotional tone of a given image. The model employs a CNN-Transformer architecture, featuring pre-trained CNN image feature extractors and three Transformer encoders.
  arXiv  Detail & Related papers  (2024-10-29T17:47:51Z)
• MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization [52.498942604622165]
  This paper presents MuVi, a framework to generate music that aligns with video content. MuVi analyzes video content through a specially designed visual adaptor to extract contextually and temporally relevant features. We show that MuVi demonstrates superior performance in both audio quality and temporal synchronization.
  arXiv  Detail & Related papers  (2024-10-16T18:44:56Z)
• EmoGene: Audio-Driven Emotional 3D Talking-Head Generation [47.6666060652434]
  EmoGene is a framework for high-fidelity, audio-driven video portraits with accurate emotional expressions.<n>Our approach employs a variational autoencoder (VAE)-based audio-to-motion module to generate facial landmarks.<n>NeRF-based emotion-to-video module renders realistic emotional talkinghead videos.
  arXiv  Detail & Related papers  (2024-10-07T08:23:05Z)
• Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model [32.801213106782335]
  We develop a generative music AI framework, Video2Music, that can match a provided video. In a thorough experiment, we show that our proposed framework can generate music that matches the video content in terms of emotion.
  arXiv  Detail & Related papers  (2023-11-02T03:33:00Z)
• Lets Play Music: Audio-driven Performance Video Generation [58.77609661515749]
  We propose a new task named Audio-driven Per-formance Video Generation (APVG) APVG aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip.
  arXiv  Detail & Related papers  (2020-11-05T03:13:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.