Catnip推出MaineCoon,一个22B参数的实时音频-视觉基础模型,能将文本提示词转化为带同步语音、动作和表情的实时角色流,支持无限时长交互。作为首个流式原生模型,MaineCoon实现亚秒级首帧,单张H100上达47.5FPS,单张RTX Pro 6000上达30FPS,内部测试吞吐量比同类音频-视觉系统快约7倍。与被动视频生成不同,它能因果性地实时响应,记住自身不完美的过去,并保持角色身份、声音和节奏的连贯一致,让AI从轮次式应答变为“与你同在”的实时存在。
Catnip just dropped MaineCoon, a 22B real-time audio-visual foundation model that turns text prompts into a live character stream with synced speech, motion, and expression.
The first streaming-native model of its kind.
sub-second first frame, 47.5FPS on one H100, 30FPS on one RTX Pro 6000, and about 7x faster throughput than comparable audio-visual systems in its internal tests.
The big deal is that a normal video generator can wait, revise, and render a finished clip, but a social interface has to move causally, remember its own imperfect past, and stay ahead of playback without breaking identity, voice, or rhythm.