Songchun Zhang

Decoupling denoising and context writing parameters in autoregressive video models unlocks unprecedented long-horizon generation capabilities, extending continuous output to 24 hours from just 5-second training rollouts.

Spatio-temporal masking during student self-rollouts breaks the reverse-KL mode-collapse trap in autoregressive video diffusion, eliminating visual over-smoothing without relying on real video data.

A groundbreaking pipeline has generated over 8,000 hours of action-conditioned video, overcoming the limitations of real-world data collection.

Achieving synchronized audio-visual generation and trajectory control, EchoWM sets a new standard for immersive media experiences in generative models.

Achieving top performance in long-horizon audio-visual generation, JoyAI-Echo-1.5 sets a new standard for coherent storytelling and interactive environments.