Really interesting architecture choice separating motion from rendering. That feels like the right abstraction boundary if you want identity generalisation without retraining.
The latency numbers are what stood out to me though. ~70ms time-to-first-frame is genuinely impressive for an interactive loop. In real conversations, responsiveness dominates perceived realism way more than visual fidelity, so that correlation result makes intuitive sense.
Curious how robust the audio-to-motion mapping is under messy real-world input (overlapping speech, accents, background noise, etc.). Does the flow-matching variant help mostly with stability during training, or also temporal consistency during inference?
The latency numbers are what stood out to me though. ~70ms time-to-first-frame is genuinely impressive for an interactive loop. In real conversations, responsiveness dominates perceived realism way more than visual fidelity, so that correlation result makes intuitive sense.
Curious how robust the audio-to-motion mapping is under messy real-world input (overlapping speech, accents, background noise, etc.). Does the flow-matching variant help mostly with stability during training, or also temporal consistency during inference?