TaoMate-HD: Progressive-Resolution Autoregressive Audio-Video Generation

Qijun Gan, Chenwei Zhang, xinyang, Meiguang Jin, Junfeng Ma, Shen Qiu

TaoMate-HD: Progressive-Resolution Autoregressive Audio-Video Generation: 2 upvotes on Hugging Face Daily Papers, #35 of 38 papers on 2026-11-05. Day-by-day upvote history.

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It had 0 upvotes by its first day, 2 upvotes by the next day, 2 upvotes by day 3, 2 upvotes by day 7, and has 2 now. It ranked #187 of 207 papers in its week and #296 of 340 in its month. Its code repository has 33 GitHub stars.

Streaming digital humans produce detailed video while continuing speech as new text arrives. High-resolution denoising is costly, and text updates can cause repeated or interrupted speech. We present TaoMate-HD, an autoregressive model for high-resolution audio-video streaming. For each short segment, or chunk, two denoising steps establish coarse video structure at low resolution. A final step refines the video to high resolution, with audio generated throughout the same trajectory. Each stage passes its audiovisual predictions to the same stage of the next chunk as history. Across text updates, the model retains this history to infer what has already been spoken. Continuation training utilizes asymmetric text conditioning, with separate text windows for generation and distillation. Experiments show a 1.6x speedup over direct high-resolution generation with the same model and hardware. Optimized stage-parallel deployment provides a further 1.9x overall speedup over serial progressive generation. Our continuation training reduces character error rate by 11.18 percentage points relative to the baseline. The model also supports hour-long audiovisual generation. Our project page is https://taoliveaigc.github.io/TaoMate-HD.

Paper page on Hugging Face · arXiv

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