MSAVBench is the first comprehensive benchmark for multi-shot audio-video generation, spanning video, audio, shot, and reference dimensions with an adaptive evaluation framework that reaches 91.5% Spearman correlation with human judgments.
Corresponding authors: Xie Chen and Xipeng Qiu
7 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 7years
2026 7representative citing papers
InstructAV2AV is an end-to-end instruction-guided audio-video joint editing model that adapts a pre-trained backbone with gated attention and two-stage training, outperforming prior methods on 11 metrics after building the InsAVE-80K dataset.
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.
Talker-T2AV achieves better lip-sync accuracy, video quality, and audio quality than dual-branch baselines by separating high-level shared autoregressive modeling from modality-specific low-level diffusion refinement in a joint audio-video generation framework.
Unison introduces a unified framework using semantic-guided harmonization and bidirectional cross-modal forcing to generate human-centric videos with improved synchronization between motion, speech, and sound effects.
MMControl adds multi-modal controls for identity, timbre, pose, and layout to unified audio-video diffusion models via dual-stream injection and adjustable guidance scaling.
Omni-Customizer proposes an end-to-end framework using Omni-Context Fusion, Masked TTS Cross-Attention, Semantic-Anchored Multimodal RoPE, and specialized training curricula to achieve precise multimodal identity binding in joint audio-video generation.
citing papers explorer
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MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation
MSAVBench is the first comprehensive benchmark for multi-shot audio-video generation, spanning video, audio, shot, and reference dimensions with an adaptive evaluation framework that reaches 91.5% Spearman correlation with human judgments.
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InstructAV2AV: Instruction-Guided Audio-Video Joint Editing
InstructAV2AV is an end-to-end instruction-guided audio-video joint editing model that adapts a pre-trained backbone with gated attention and two-stage training, outperforming prior methods on 11 metrics after building the InsAVE-80K dataset.
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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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Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling
Talker-T2AV achieves better lip-sync accuracy, video quality, and audio quality than dual-branch baselines by separating high-level shared autoregressive modeling from modality-specific low-level diffusion refinement in a joint audio-video generation framework.
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Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation
Unison introduces a unified framework using semantic-guided harmonization and bidirectional cross-modal forcing to generate human-centric videos with improved synchronization between motion, speech, and sound effects.
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MMControl: Unified Multi-Modal Control for Joint Audio-Video Generation
MMControl adds multi-modal controls for identity, timbre, pose, and layout to unified audio-video diffusion models via dual-stream injection and adjustable guidance scaling.
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Omni-Customizer: End-to-End MultiModal Customization for Joint Audio-Video Generation
Omni-Customizer proposes an end-to-end framework using Omni-Context Fusion, Masked TTS Cross-Attention, Semantic-Anchored Multimodal RoPE, and specialized training curricula to achieve precise multimodal identity binding in joint audio-video generation.