HoliDubber introduces a patch-based autoregressive diffusion transformer for joint text-guided synthesis of speech and ambient audio in video dubbing, with a new benchmark showing outperformance over prior speech-only methods.
Audiogen-omni: A unified multimodal diffusion transformer for video-synchronized audio, speech, and song generation.ArXiv, abs/2508.00733
5 Pith papers cite this work. Polarity classification is still indexing.
years
2026 5verdicts
UNVERDICTED 5representative citing papers
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.
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.
SwanSphere introduces a causal autoregressive diffusion transformer architecture with SVAC contrastive learning and ODPO optimization for streaming spatial audio generation from video and text.
citing papers explorer
-
HoliDubber: Holistic Video Dubbing for Complex Acoustic Scenes via Text-Guided Audio Synthesis
HoliDubber introduces a patch-based autoregressive diffusion transformer for joint text-guided synthesis of speech and ambient audio in video dubbing, with a new benchmark showing outperformance over prior speech-only methods.
-
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.
-
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.
-
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.
-
Towards Streaming Synchronized Spatial Audio Generation via Autoregressive Diffusion Transformer
SwanSphere introduces a causal autoregressive diffusion transformer architecture with SVAC contrastive learning and ODPO optimization for streaming spatial audio generation from video and text.