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AudioX: A Unified Framework for Anything-to-Audio Generation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
Audio and music generationDiT
Abstract:

Audio and music generation based on flexible multimodal control signals is a widely applicable topic, with the following key challenges: 1) a unified multimodal modeling framework, and 2) large-scale, high-quality training data. As such, we propose AudioX, a unified framework for anything-to-audio generation that integrates varied multimodal conditions (i.e., text, video, image, and audio signals) in this work. The core design in this framework is a Multimodal Adaptive Fusion module, which enables the effective fusion of diverse multimodal inputs, enhancing cross-modal alignment and improving overall generation quality. To train this unified model, we construct a large-scale, high-quality dataset, IF-caps, comprising over 7 million samples curated through a structured data annotation pipeline. This dataset provides comprehensive supervision for multimodal-conditioned audio generation. We benchmark AudioX against state-of-the-art methods across a wide range of tasks, finding that our model achieves superior performance, especially in text-to-audio and text-to-music generation. These results demonstrate our method is capable of audio generation under multimodal control signals, showing powerful instruction-following potential. We will release the code, model, and dataset.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

AudioX proposes a unified framework for anything-to-audio generation, integrating text, video, image, and audio signals through a Multimodal Adaptive Fusion module. The taxonomy places this work in the 'Multimodal Fusion and Alignment Architectures' leaf, which contains four papers total, including AudioX itself. This leaf sits within the broader 'Unified Multimodal Generation Frameworks' branch, indicating a moderately populated research direction focused on systems that explicitly fuse diverse modalities rather than handling single modality pairs. The sibling papers in this leaf similarly address multimodal integration challenges, suggesting this is an active but not overcrowded area.

The taxonomy reveals that AudioX's leaf is one of three under 'Unified Multimodal Generation Frameworks,' alongside 'Large Language Model-Based Generation' (three papers) and 'Multi-Agent and Reasoning Systems' (two papers). Neighboring branches include specialized directions like 'Video-to-Audio Generation' (seven papers across four leaves) and 'Text-to-Music Generation' (four papers across four leaves). The scope note for AudioX's leaf explicitly excludes simple concatenation or single-encoder approaches, positioning it among architectures with explicit fusion mechanisms. This structural context suggests AudioX addresses a recognized gap between modality-specific methods and more loosely integrated multimodal systems.

Among the three contributions analyzed, the unified framework examined ten candidates and found one potentially refutable prior work, indicating some overlap with existing multimodal generation systems within the limited search scope. The IF-caps dataset contribution examined ten candidates with no clear refutations, suggesting this large-scale data curation effort may be more distinctive. The Multimodal Adaptive Fusion module examined six candidates without finding clear prior work, though the smaller candidate pool limits confidence. These statistics reflect a top-26 semantic search, not an exhaustive literature review, so the presence of one refutable candidate for the framework suggests moderate but not complete novelty within the examined sample.

Based on the limited search scope of 26 candidates, AudioX appears to occupy a recognized research direction with established sibling work, yet its specific fusion architecture and dataset contributions show some distinctiveness. The taxonomy structure indicates this is neither a pioneering new direction nor an overcrowded space, with the framework contribution showing the most overlap among examined candidates. The analysis cannot assess novelty beyond the top-K semantic matches and their citation neighborhoods.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
26
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: multimodal-conditioned audio and music generation. This field encompasses systems that synthesize audio or music from diverse input modalities—text, video, images, facial expressions, or even other audio signals. The taxonomy reveals a rich structure organized around both the conditioning modality and the generation target. Major branches include unified frameworks that handle multiple modalities simultaneously (e.g., AudioX[0], Audiogen Omni[2]), specialized pipelines for video-to-audio or video-to-music generation (e.g., Video2Music[5], Kling Foley[6]), text-driven music synthesis (e.g., MusicLM[4]), and image or visual arts-to-music approaches (e.g., Paintings to Music[31]). Additional branches address controllable editing, domain-specific applications such as bioacoustic or videogame music, and cross-modal audio-visual learning (e.g., Cross Modal Audio Visual[13]). Surveys and meta-analyses (e.g., Music Generation Survey[34], Text to Music Review[30]) provide overarching perspectives on these diverse directions. Recent work has increasingly emphasized unified architectures capable of fusing multiple modalities within a single model, balancing flexibility with computational efficiency. AudioX[0] exemplifies this trend by proposing a multimodal fusion and alignment architecture that integrates text, video, and other signals into a coherent generation pipeline. This places it alongside other unified frameworks like Audiogen Omni[2] and Mumu Llama[7], which similarly aim to handle varied conditioning inputs. In contrast, many specialized branches focus on a single modality pair—such as video-to-audio (e.g., MMAudio[23], Hunyuanvideo Foley[8]) or text-to-music (e.g., MusicLM[4])—often achieving higher fidelity within their narrower scope. Key open questions revolve around how to effectively align heterogeneous modalities, manage temporal synchronization, and preserve musical or acoustic coherence across diverse conditioning signals. AudioX[0] sits squarely within the unified frameworks branch, sharing design goals with Audiogen Omni[2] and Mumu Llama[7], yet its emphasis on explicit fusion and alignment mechanisms distinguishes it from more modular or retrieval-based approaches.

Claimed Contributions

AudioX unified framework for anything-to-audio generation

The authors introduce AudioX, a unified framework that supports audio and music generation from diverse multimodal inputs including text, video, and audio signals. The framework incorporates a Multimodal Adaptive Fusion module to effectively fuse different modalities and enhance cross-modal alignment.

10 retrieved papers
Can Refute
IF-caps large-scale multimodal dataset

The authors design a data curation pipeline and construct IF-caps, a large-scale dataset containing over 7 million samples with fine-grained annotations. This dataset provides comprehensive supervision for multimodal-conditioned audio generation and addresses the scarcity of high-quality multimodal training data.

10 retrieved papers
Multimodal Adaptive Fusion module

The authors propose a lightweight Multimodal Adaptive Fusion module that uses gates and learnable queries to filter, reweight, and aggregate multimodal embeddings. This module enables stronger cross-modal control and reduces interference between different modalities, improving generation quality.

6 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

AudioX unified framework for anything-to-audio generation

The authors introduce AudioX, a unified framework that supports audio and music generation from diverse multimodal inputs including text, video, and audio signals. The framework incorporates a Multimodal Adaptive Fusion module to effectively fuse different modalities and enhance cross-modal alignment.

Contribution

IF-caps large-scale multimodal dataset

The authors design a data curation pipeline and construct IF-caps, a large-scale dataset containing over 7 million samples with fine-grained annotations. This dataset provides comprehensive supervision for multimodal-conditioned audio generation and addresses the scarcity of high-quality multimodal training data.

Contribution

Multimodal Adaptive Fusion module

The authors propose a lightweight Multimodal Adaptive Fusion module that uses gates and learnable queries to filter, reweight, and aggregate multimodal embeddings. This module enables stronger cross-modal control and reduces interference between different modalities, improving generation quality.

Table of Contents