SeamCam quantifies camouflage by computing one minus the highest IoU recoverable from category-conditioned detection proposals against a ground-truth mask, achieving 78.82% agreement with human judgments.
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AudioX: A Unified Framework for Anything-to-Audio Generation
13 Pith papers cite this work. Polarity classification is still indexing.
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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, 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. The code and datasets will be available at https://zeyuet.github.io/AudioX/.
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Geo2Sound generates geographically realistic soundscapes from satellite imagery via geospatial attribute modeling, semantic hypothesis expansion, and geo-acoustic alignment, achieving SOTA FAD of 1.765 on a new 20k-pair benchmark.
VidAudio-Bench benchmarks V2A and VT2A models across four audio categories, revealing poor speech/singing performance and a tension between visual alignment and text instruction following.
A single DiT-based diffusion model unifies video-to-audio, text-to-audio, and joint video-text-to-audio generation, supported by a new 470k-pair dataset and three-stage progressive training that resolves task competition.
MVAD is the first comprehensive benchmark dataset for AI-generated multimodal video-audio detection, with three realistic forgery patterns, high-quality outputs from state-of-the-art models, and diversity across visual styles and content categories.
StereoFoley is an end-to-end video-to-stereo-audio framework that uses a base generative model fine-tuned on synthetic object-tracked data with panning and distance controls to achieve object-aware spatial sound.
Audio-Interaction unifies offline and online audio tasks into one streaming model via the SoundFlow framework and a new 2.6M-item streaming corpus, enabling real-time instruction following and proactive responses.
Foley-Omni extends isolated audio synthesis to joint generation of full video soundtracks across speech, effects, and music, with a new V2ST-Bench for evaluation showing competitive single-task results and gains in mixed-track consistency.
JenBridge pretrains a flow-matching Transformer on text-audio data then adapts it with video conditioning and an LLM director to select transitions, claiming better coherence than prior methods on a new LVS benchmark.
Dasheng AudioGen uses multi-view captions and a unified semantic-acoustic representation to enable end-to-end generation of mixed audio scenes from text descriptions.
WavFlow performs direct waveform audio generation via flow matching on 2D token grids from raw patches plus amplitude lifting, matching latent-based methods on VGGSound and AudioCaps without intermediate compression.
ControlFoley introduces a unified framework for controllable video-to-audio generation using joint visual encoding, temporal-timbre decoupling, and robust multimodal training to handle cross-modal conflicts.
EntangleCodec unifies semantic and acoustic audio tokenization via caption alignment and flow-matching decoding, reporting competitive reconstruction, +7.4% gains on MMAR understanding, and 0.6B-parameter ALMs surpassing 13B-parameter continuous baselines.
citing papers explorer
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SeamCam: Quantifying Seamless Camouflage via Multi-Cue Visual Detectability
SeamCam quantifies camouflage by computing one minus the highest IoU recoverable from category-conditioned detection proposals against a ground-truth mask, achieving 78.82% agreement with human judgments.
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Geo2Sound: A Scalable Geo-Aligned Framework for Soundscape Generation from Satellite Imagery
Geo2Sound generates geographically realistic soundscapes from satellite imagery via geospatial attribute modeling, semantic hypothesis expansion, and geo-acoustic alignment, achieving SOTA FAD of 1.765 on a new 20k-pair benchmark.
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VidAudio-Bench: Benchmarking V2A and VT2A Generation across Four Audio Categories
VidAudio-Bench benchmarks V2A and VT2A models across four audio categories, revealing poor speech/singing performance and a tension between visual alignment and text instruction following.
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Omni2Sound: Towards Unified Video-Text-to-Audio Generation
A single DiT-based diffusion model unifies video-to-audio, text-to-audio, and joint video-text-to-audio generation, supported by a new 470k-pair dataset and three-stage progressive training that resolves task competition.
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MVAD: A Benchmark Dataset for Multimodal AI-Generated Video-Audio Detection
MVAD is the first comprehensive benchmark dataset for AI-generated multimodal video-audio detection, with three realistic forgery patterns, high-quality outputs from state-of-the-art models, and diversity across visual styles and content categories.
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StereoFoley: Object-Aware Stereo Audio Generation from Video
StereoFoley is an end-to-end video-to-stereo-audio framework that uses a base generative model fine-tuned on synthetic object-tracked data with panning and distance controls to achieve object-aware spatial sound.
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Audio Interaction Model
Audio-Interaction unifies offline and online audio tasks into one streaming model via the SoundFlow framework and a new 2.6M-item streaming corpus, enabling real-time instruction following and proactive responses.
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Foley-Omni: A Unified Multimodal Generation Model from Task-Level Audio Synthesis to Complete Video Soundtrack Generation
Foley-Omni extends isolated audio synthesis to joint generation of full video soundtracks across speech, effects, and music, with a new V2ST-Bench for evaluation showing competitive single-task results and gains in mixed-track consistency.
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JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions
JenBridge pretrains a flow-matching Transformer on text-audio data then adapts it with video conditioning and an LLM director to select transitions, claiming better coherence than prior methods on a new LVS benchmark.
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Dasheng AudioGen: A Unified Model for Generating Coherent Audio Scenes from Text
Dasheng AudioGen uses multi-view captions and a unified semantic-acoustic representation to enable end-to-end generation of mixed audio scenes from text descriptions.
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WavFlow: Audio Generation in Waveform Space
WavFlow performs direct waveform audio generation via flow matching on 2D token grids from raw patches plus amplitude lifting, matching latent-based methods on VGGSound and AudioCaps without intermediate compression.
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ControlFoley: Unified and Controllable Video-to-Audio Generation with Cross-Modal Conflict Handling
ControlFoley introduces a unified framework for controllable video-to-audio generation using joint visual encoding, temporal-timbre decoupling, and robust multimodal training to handle cross-modal conflicts.
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EntangleCodec: A Unified Discrete Audio Tokenizer via Semantic-Acoustic Entanglement
EntangleCodec unifies semantic and acoustic audio tokenization via caption alignment and flow-matching decoding, reporting competitive reconstruction, +7.4% gains on MMAR understanding, and 0.6B-parameter ALMs surpassing 13B-parameter continuous baselines.