4 papers indexed
Xujie Zhang, Runyan Du, Song Chang, Jiang Li, Dongliang Shao, Liping Wu, et al.
Synthesizing native 2K multi-garment virtual try-on is a formidable frontier in digital fashion, critically bottlenecked by two fundamental limitations: the O(N^2) memory explosion induced by 2k conditions, and the spectral bias of diffusion models that over-smooths high-frequenc…
Han Wang, Zijun Wang, Shuoshuo Xue, Rui Cao, Fengjiao Cheng, Xiaodan Liang, et al.
Action-conditioned world models are a key component of embodied AI, serving as scalable policy evaluators that reduce reliance on expensive real-world rollouts. To accurately capture diverse action-induced dynamics, such models should satisfy three key objectives-Physical Plausib…
Zhihao Liu, Qiuyi Gu, Yitao Wang, Dongming Qiao, Yixian Zhang, Shuaihang Chen, et al.
Real-world robot learning increasingly relies on heterogeneous data, but demonstrations and rollouts often mix useful progress with stalls, corrections, and suboptimal behavior. Effective policy learning therefore requires frame-level advantages that distinguish reliable local pr…
Jing Wang, Xiangxin Zhou, Jiajun Liang, Kaiqi Liu, Wanyuan Pang, Zhenyu Xie, et al.
Autoregressive (AR) video diffusion models enable low-latency streaming generation by synthesizing videos chunk by chunk with cached visual context, but this chunk-wise formulation makes temporal instruction following ambiguous. A single global prompt does not specify which sub-e…