OmniNFT introduces modality-wise advantage routing, layer-wise gradient surgery, and region-wise loss reweighting in an online diffusion RL framework to improve audio-video quality, alignment, and synchronization.
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Meta audiobox aesthetics: Unified automatic quality assessment for speech, music, and sound.arXiv preprint arXiv:2502.05139
12 Pith papers cite this work. Polarity classification is still indexing.
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TMD-Bench is a multi-level benchmark that measures music-dance co-generation quality including beat-level rhythmic synchronization, supported by a new dataset and Music Captioner, and shows commercial models lag in rhythm while a new baseline performs competitively.
Hallo-Live achieves 20.38 FPS real-time text-to-audio-video avatar generation with 0.94s latency using asynchronous dual-stream diffusion and HP-DMD preference distillation, matching teacher model quality at 16x higher throughput.
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.
AuDirector is a self-reflective closed-loop multi-agent framework that generates immersive audio narratives with improved structural coherence, emotional expressiveness, and acoustic fidelity via identity-aware voice adaptation and iterative correction.
VocalParse applies interleaved and Chain-of-Thought prompting to a Large Audio Language Model to jointly transcribe lyrics, melody and word-note alignments, achieving state-of-the-art results on multiple singing datasets.
JASTIN is an instruction-driven audio evaluation system that achieves state-of-the-art correlation with human ratings on speech, sound, music, and out-of-domain tasks without task-specific retraining.
APEX jointly predicts engagement-based popularity and five aesthetic quality dimensions for AI-generated music, improving human preference prediction on out-of-distribution generative systems.
OmniHuman is a new large-scale multi-scene dataset with video-, frame-, and individual-level annotations for human-centric video generation, accompanied by the OHBench benchmark that adds metrics aligned with human perception.
SongBench is a new fine-grained benchmark for song quality assessment with seven dimensions and an expert-annotated dataset of 11,717 samples showing high correlation with professional ratings.
SA-SLM uses variational information bottleneck for intent-aware bridging and self-criticism for realization-aware alignment to close the semantic-acoustic gap, outperforming open-source models and nearing GPT-4o-Audio expressiveness on EchoMind after training on 800 hours of data.
Continuous diffusion spoken language models follow scaling laws for loss and phoneme divergence and generate emotive multi-speaker speech at 16B scale, though long-form coherence stays difficult.
citing papers explorer
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OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation
OmniNFT introduces modality-wise advantage routing, layer-wise gradient surgery, and region-wise loss reweighting in an online diffusion RL framework to improve audio-video quality, alignment, and synchronization.
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TMD-Bench: A Multi-Level Evaluation Paradigm for Music-Dance Co-Generation
TMD-Bench is a multi-level benchmark that measures music-dance co-generation quality including beat-level rhythmic synchronization, supported by a new dataset and Music Captioner, and shows commercial models lag in rhythm while a new baseline performs competitively.
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Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation
Hallo-Live achieves 20.38 FPS real-time text-to-audio-video avatar generation with 0.94s latency using asynchronous dual-stream diffusion and HP-DMD preference distillation, matching teacher model quality at 16x higher throughput.
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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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AuDirector: A Self-Reflective Closed-Loop Framework for Immersive Audio Storytelling
AuDirector is a self-reflective closed-loop multi-agent framework that generates immersive audio narratives with improved structural coherence, emotional expressiveness, and acoustic fidelity via identity-aware voice adaptation and iterative correction.
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VocalParse: Towards Unified and Scalable Singing Voice Transcription with Large Audio Language Models
VocalParse applies interleaved and Chain-of-Thought prompting to a Large Audio Language Model to jointly transcribe lyrics, melody and word-note alignments, achieving state-of-the-art results on multiple singing datasets.
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JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions
JASTIN is an instruction-driven audio evaluation system that achieves state-of-the-art correlation with human ratings on speech, sound, music, and out-of-domain tasks without task-specific retraining.
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APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music
APEX jointly predicts engagement-based popularity and five aesthetic quality dimensions for AI-generated music, improving human preference prediction on out-of-distribution generative systems.
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OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation
OmniHuman is a new large-scale multi-scene dataset with video-, frame-, and individual-level annotations for human-centric video generation, accompanied by the OHBench benchmark that adds metrics aligned with human perception.
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SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment
SongBench is a new fine-grained benchmark for song quality assessment with seven dimensions and an expert-annotated dataset of 11,717 samples showing high correlation with professional ratings.
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Bridging What the Model Thinks and How It Speaks: Self-Aware Speech Language Models for Expressive Speech Generation
SA-SLM uses variational information bottleneck for intent-aware bridging and self-criticism for realization-aware alignment to close the semantic-acoustic gap, outperforming open-source models and nearing GPT-4o-Audio expressiveness on EchoMind after training on 800 hours of data.
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Scaling Properties of Continuous Diffusion Spoken Language Models
Continuous diffusion spoken language models follow scaling laws for loss and phoneme divergence and generate emotive multi-speaker speech at 16B scale, though long-form coherence stays difficult.