TaoMate-HD streams high-resolution audio-video with progressive denoising
Authors report a 1.6x speedup versus direct high-resolution generation and an 11.18-point drop in character error rate.
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Authors report a 1.6x speedup versus direct high-resolution generation and an 11.18-point drop in character error rate.
Each branch first learns from unimodal data; scarce paired data is reserved for cross-modal coupling.
AVC-Reward scores complex audio-video consistency at event level and aligns with human judgments better than prior metrics.
Authors say the reward model aligns best with human judgments and can steer a joint generator via online reinforcement learning.
SepGen jointly generates video, a mixed soundtrack, and per-source audio stems, and can also separate mixes from captions.
Prism uses dynamic sparse attention to train joint 2K video-audio models 2.5× faster than full attention.
Kandinsky 6.0 Video releases open 3B and 29B models for synchronized text- and image-to-audio-video.
Salt++ post-trains few-step streaming audio-video models, beating OmniForcing on quality and LTX-2 on most metrics.
RecCAR regularizes cross-attention so companion modalities better constrain joint video generation.
Researchers analyze the 'attention triangle' in audio-video diffusion models, showing bias-driven cross-attention routing causes semantic leakage and proposing inference-time interventions that improve grounding.
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