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ARDY introduces a streaming generation framework for real-time, high-fidelity 3D human motion generation controlled by text and kinematic constraints, using a hybrid representation and two-stage autoregressive transformer denoiser.
This paper introduces Lip Forcing, the first autoregressive diffusion method for real-time video-to-video lip synchronization. By distilling a 14B teacher into causal students and using only two denoising steps, it achieves 31 FPS streaming at 1.3B scale, 17.6x faster than same-scale bidirectional models.