Diffusion models have achieved remarkable success in generative modeling, with their sampling procedures routinely modified to control generation and improve efficiency. These modifications introduce perturbations along the sampling trajectory, raising a central question: how do such perturbations affect generated outp...
Wei Guo, Yao-Wen Zhang, Xingtong Ge et al.· 0 citations
Few-step autoregressive video generation enables efficient streaming synthesis, but errors introduced in early temporal blocks are reused as context and can propagate through subsequent rollouts, leading to detail degradation, structural drift, and unstable motion. Existing distribution matching distillation (DMD) prim...
Fang-Yu Lin, Xing-Tong Ge, Lu Zhu et al.· 0 citations
Omni-LiveAvatar is presented, the first framework for minute-level, real-time streaming joint audio-video avatar generation and proposes a progressive autoregressive distillation pipeline that transfers a large bidirectional joint audio-video diffusion model into a few-step autoregressive generator without auxiliary st...
Lu Zhu, Xing-Tong Ge, Fang-Yu Lin et al.· 1 citation
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