⚡ electrical engineering

Bring Music The Horizon: Music-Driven 360 $^\circ$ Video Generation

This paper introduces "Bring Music The Horizon," an emotion-aware pipeline that generates immersive 360° music visualization videos by predicting a song's emotional trajectory and utilizing it to guide keyframe generation and video synthesis for a temporally continuous, immersive experience.

Original authors: Kai Hsu Tsai, Yong Wei Fu, Hung I Yang, Yu-Chih Chen

Published 2026-07-16

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CC BY 4.0

Original authors: Kai Hsu Tsai, Yong Wei Fu, Hung I Yang, Yu-Chih Chen

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are sitting in a dark room with a pair of headphones on, listening to a song that makes your heart race. You close your eyes and see a movie playing in your mind, but what if you could step inside that movie? This is the dream of music visualization, a field where scientists try to turn sound into pictures. Usually, when we watch music videos, we are stuck looking at a flat screen, like watching a painting that doesn't move around us. But there is a newer, cooler technology called 360° video. Think of this like a virtual reality bubble where you can look up, down, left, and right, and the world moves with you. The big question researchers are asking is: How do we make a 360° world that doesn't just look random, but actually feels like the music? They want the visuals to dance to the rhythm and change colors based on whether the song sounds happy, sad, or exciting, without needing to read the lyrics to tell the story.

Enter the team behind Bring Music The Horizon, a project that tries to solve this puzzle. Instead of just guessing what a song looks like or relying on the words the singer says, these researchers built a "magic pipeline" that listens to the music's emotional heartbeat. They figured out how to break a song down into tiny chunks, figure out how "happy" (valence) and "energetic" (arousal) each chunk is, and then use that information to paint a new 360° picture for every single moment of the song.

Here is how their system works, step-by-step, like a recipe for a visual feast:

First, the system acts like a super-smart music detective. It takes a song and chops it up into four-bar segments (a standard musical unit, kind of like a musical sentence). For each of these little chunks, it calculates a specific "emotional score." It doesn't need the lyrics to do this; it just listens to the sound waves to guess if that part of the song is calm and sad, or wild and energetic.

Next, the system takes a simple idea you give it—like "a forest at sunset"—and acts like a mood-shifting artist. It uses a special tool called EmotiCrafter to tweak your idea based on the emotional score it just calculated. If the music is getting intense, the forest might turn stormy and dark; if the music is soft, the forest might glow with golden light. This creates a series of "keyframes," which are like the main snapshots of a movie. The system uses another tool, SEGA, to make sure these snapshots stay true to your original idea while still changing with the music's mood.

Finally, the system takes these snapshots and animates them. It uses a video generator to fill in the gaps between the snapshots, creating smooth, moving 360° clips. It stitches these clips together in the exact order of the song, so the video flows perfectly with the music's timeline. The result is a complete, immersive video where the scenery breathes and shifts exactly as the song does.

The researchers tested this with songs from different styles and found that it works. They showed that their videos can be viewed in VR headsets, creating a truly immersive experience where the visuals match the emotional journey of the song. However, they are honest about the fact that it's not perfect yet. Sometimes, if two parts of the song feel very similar, the video might look a bit repetitive. Also, because the technology is still new, the videos aren't super high-definition, and sometimes the camera movement can feel a little weird if the instructions are too long. But overall, they proved that it is possible to turn a song into a 360° emotional journey, moving us away from flat screens and into a world where music truly surrounds us.

Technical Summary: Bring Music The Horizon

Problem Statement

Current music visualization methods face significant limitations in creating immersive experiences. Most existing approaches generate flat, fixed-view videos resembling conventional music videos, failing to provide the 360° immersion required for modern VR environments. Furthermore, many methods rely heavily on lyrics to infer visual semantics, which restricts their applicability to instrumental music and often overlooks the continuous emotional dynamics inherent in the audio itself. There is a lack of pipelines that can translate the intrinsic emotional trajectory and structural progression of a song into a temporally coherent, immersive 360° visual narrative.

Methodology

The paper proposes Bring Music The Horizon, an emotion-aware pipeline for music-driven 360° video generation. The system takes an input song $S$ and a user-specified base prompt $p_{base}$ to generate a complete 360° visualization $V_{360}$ . The process is formalized as $(S, p_{base}) \rightarrow (B, U, E) \rightarrow K \rightarrow V_{360}$ , consisting of three primary modules:

1. Music Information Retrieval (MIR)

The first module analyzes the input song to extract its temporal structure and emotional trajectory without relying on lyrics.

Structure Extraction: The system uses the All-In-One model to estimate downbeat timestamps ( $B$ ) and functional segment information.
Segmentation: Based on downbeats, the song is divided into four-bar units ( $U$ ).
Emotion Prediction: A proposed Dynamic Valence-Arousal (V-A) Regressor predicts an emotion condition $e_t = (v_t, a_t)$ for each four-bar unit. This regressor is trained to estimate segment-level emotional dynamics directly from audio, providing a sequence of emotional conditions ( $E$ ) that serve as the guide for visual generation.

2. Emotion-guided 360° Keyframe Generation

This module maps each four-bar music unit to an emotion-aware panoramic keyframe ( $k_t$ ).

Emotional Residuals: The base prompt $p_{base}$ and the emotion condition $e_t$ are fed into a retrained EmotiCrafter model to generate an emotional residual $r_t$ . This residual defines how the visual semantics of the base prompt should shift to align with the target emotion.
Semantic Control: The residual $r_t$ is converted into a SEGA guidance condition ( $g_t$ ), enabling fine-grained semantic control during the diffusion process.
Image Synthesis: Using SDXL equipped with a 360° LoRA adapter, the system generates the panoramic keyframe $k_t$ . This ensures the visual content preserves the user's scene concept while evolving according to the song's emotional progression.

3. 360° Video Generation

The final module synthesizes dynamic video from the sequence of keyframes $K$ .

Dynamic Scenes: For each keyframe $k_t$ , the first three bars of the corresponding four-bar unit are synthesized as a dynamic scene ( $d_t$ ) using Wan-I2V (Image-to-Video).
Transitions: The final bar is generated as a transition clip ( $\rho_t$ ) using Wan-flf2v (Frame-to-Frame-to-Video) to connect the current scene to the next keyframe ( $k_{t+1}$ ).
Assembly: All dynamic scenes and transition clips are concatenated in temporal order to produce the final immersive 360° music visualization video.

Key Contributions

Immersive Pipeline: The work introduces a novel pipeline specifically designed to generate 360° music visualization, moving beyond flat, conventional video formats.
Emotion-Driven Generation: Unlike methods relying on lyrics, this approach utilizes a dynamic V-A regressor to extract emotional trajectories directly from audio, allowing for the visualization of instrumental music.
Fine-Grained Control: By integrating EmotiCrafter, SEGA, and SDXL with 360° LoRA, the system achieves precise control over visual semantics, ensuring scenes align with both the user's prompt and the song's emotional state.
Artifact Reduction: The authors note that retraining EmotiCrafter with a custom dataset successfully reduced undesired human-activity artifacts, resulting in cleaner, scene-focused outputs.

Results

The experimental results demonstrate that the pipeline successfully generates 360° music visualization videos viewable in VR headsets.

Temporal Alignment: The generated scenes follow the temporal structure of the input songs, with scene changes and transitions synchronized to the musical flow.
Emotional Consistency: The visual atmosphere and scene dynamics reflect the estimated valence-arousal trajectory across different musical segments.
Qualitative Comparison: The paper provides qualitative comparisons against From-Sound-To-Sight, a representative audio-to-visual baseline, highlighting the proposed method's ability to create immersive, emotion-aligned content.

Significance and Limitations

The authors claim this work establishes a promising foundation for cross-modal and immersive content creation, offering a practical framework for extending music visualization from flat videos to immersive VR experiences.

However, the paper explicitly acknowledges several limitations:

Repetitiveness: Similar V-A values may lead to repetitive keyframes.
Visual Artifacts: Visible seams can appear due to boundary inconsistencies in the generated 360° videos.
Resolution and Control: The current output resolution is limited, and the forward-moving visual effect cannot yet be fully controlled, which may impact motion comfort.
Prompt Sensitivity: Overly long prompts may degrade the 360° generation quality, causing the model to produce flat rather than immersive visuals.