arXiv AI
VIBE: Video Instruction-aligned Background music gEneration
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JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions
arXiv:2606. 01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions.
arXiv Computer Vision
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VA-Judger: Reward Modeling from Human Preference Feedback for Joint Video-Audio Generation
arXiv:2608. 18607v2 Announce Type: replace Abstract: Using reinforcement learning to post-train joint video-audio generation models requires a reward signal.
arXiv AI
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CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction
arXiv:2603. 00610v3 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind.
arXiv AI
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LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training
arXiv:2606. 30642v1 Announce Type: cross Abstract: Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts.
arXiv Computer Vision
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StreamAV-Bench: A Comprehensive Benchmark for Streaming Audio-Video Generation
StreamAV-Bench is the first comprehensive benchmark designed for streaming audio‑video generation, addressing the limitations of existing benchmarks that focus on completed sequences. It introduces a unified evaluation framework with a progressive track for instruction adherence and long‑horizon stability, and an interactive track for responsive interaction and state retention. The benchmark includes 32 fine‑grained, expert‑verified evaluation cases and evaluates 13 representative systems, revealing temporal drift in progressive generation and responsiveness bottlenecks in interactive control.
arXiv AI
Jul 24
AG-REPA: Causal Layer Selection for Representation Alignment in Audio Flow Matching
arXiv:2603. 01006v3 Announce Type: replace-cross Abstract: REPresentation Alignment (REPA) improves the training of generative flow models by aligning intermediate hidden states with pretrained teacher features, but its effectiveness in token-conditioned audio Flow Matching critically depends on the choice of supervised layers, which is typically made heuristically based on the depth.