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Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model, is presented and the feasibility of real-time spatial video generation for both Vidu S2-Avatar and Vidu S2-Editing is explored.
Voice-controlled video generation just got a major upgrade with Vidu S1, achieving real-time performance without visual distortion.
Real-time robot control just got a 50x speed boost thanks to MotuBrain's efficient world action model.
SpargeAttention2 achieves 95% attention sparsity in video diffusion models with a 16.2x speedup, proving that trainable sparse attention can significantly outperform training-free methods without sacrificing generation quality.
Achieve significantly more stable and consistent video world models by encoding camera-ray geometry directly into the self-attention mechanism, outperforming screen-space positional embeddings.
