arXiv:2607. 24560v1 Announce Type: cross Abstract: We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data built primarily from Ego4D.
By Jinjie Mai, Gordon Guocheng Qian, Willi Menapace, Arpit Sahni, Chaoyang Wang, Ashkan Mirzaei, Runjia Li, Sergey Tulyakov, Bernard Ghanem, Peter Wonka, Rameen Abdal
arXiv:2610.01039v1 Announce Type: new
Abstract: While recent video generative models can synthesize high-fidelity videos, they struggle to portray plausible physical interactions and the resulting st...
By Jiho Jang, Jinyoung Kim, Nojun Kwak, Kyungjune Kim
arXiv:2609.38839v1 Announce Type: new
Abstract: Long-horizon video generation requires models to effectively leverage an increasingly long generation history. As the generated history grows, retainin...
By Bo Yin, Xiaobin Hu, Jiaqi Zhao, Shuicheng Yan
We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data built primarily from Ego4D. Given a monocular video and an event-triggered prompt of the form "when X happens, do Y," EgoPlay infers whether and when event X occurs, preserves pre-event frames, and applies edit Y only to the post-event continuation.
LiveProBench evaluates streaming video models on their ability to interact proactively, assessing whether they respond at appropriate times without explicit cues. The benchmark tests models at one‑second intervals across six subtasks that vary trigger ambiguity and timing tolerance, measuring response accuracy, silence rates, and duplicate responses. Results show that many models issue premature responses more often than missed ones, highlighting a significant shortfall in human‑like temporal decision making.
By Kaixuan Du, Xin Wan, Hang Zhang, Meng Cao, Dai Guan, Ming Chen, YuKun Wang
ProactiveBench evaluates streaming video models on their ability to interact proactively, rather than reactively. It tests models at one‑second intervals without explicit cues, using six subtasks that vary trigger ambiguity, timing tolerance, and response patterns. The benchmark measures both response and silence rates, distinguishing early, in‑window, and missed responses, and penalizes omissions and repetitions.
By Kaixuan Du, Xin Wan, YuKun Wang, Hang Zhang, Meng Cao, Dai Guan, Ming Chen, Ni Li