字節開源嘴型同步模型LatentSync，實現超真實口型同步
近日，字節跳動發佈了名爲 LatentSync 的新型口型同步框架，旨在利用音頻條件潛在擴散模型實現更精確的口型同步。該框架基於Stable Diffusion，針對時間一致性做了優化。與以往的基於像素空間擴散或兩階段生成的方法不同，LatentSync 採用端到端的方式，無需中間運動表示，能夠直接建模複雜的音頻與視覺之間的關係。在 LatentSync 的框架中，首先使用 Whisper 將音頻頻譜圖轉換爲音頻嵌入，並通過交叉注意力層將其集成到 U-Net 模型中。框架通過將參考幀和掩碼幀與噪聲潛在變量進行通道級拼接
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