Recent image editing systems have achieved impressive semantic understanding, visual fidelity, and instruction-following ability, while video editing remains substantially more difficult and costly. In this paper, we present a simple alternative to end-to-end video editing: instead of training a monolithic video editor...
Feng Wang, Zi-Jie Li, Ce-Yuan Yang et al.· 0 citations
Current controllable video generation systems often rely on 2D motion trajectories or sparse drag signals for object motion. These controls are ambiguous because the same 2D trajectory can correspond to different 3D motions, especially when the camera and objects move simultaneously. We present Generative Cinematograph...
Jia-Han Zhang, Chao-Hao Yang, N. Guruprasad et al.· 0 citations
This work introduces WROP (World Reasoning with Object Permanence), a data infrastructure of 150 hand-designed cognitive science inspired tasks, divided into six cognitive categories, and builds Blender generators that randomize speed, lighting, camera angle, and other nuisance parameters while preserving each task's c...
Hao-Tian Zhang, Feng-Yuan Yu, Dezhi Luo et al.· 0 citations
This work introduces VBVR-Pro, a closed-loop testbed that makes native visual reasoning through generation trainable, verifiable, optimizable, and experimentally controllable, and identifies recurring failure modes of the prevalent VLM-as-a-judge paradigm.
Junhua Xu, Rui-Si Wang, Fanyi Pu et al.· 5 citations
This work introduces a unified formulation for vision models, where diverse forms of visual information beyond natural images, such as masks, depth maps, and other structured visual signals, are all represented as RGB images, while general visual tasks can be converted into a common RGB-to-RGB image editing problem. In...
Ti-Ming Yang, Jinrui Yang, Xinlong Li et al.· 0 citations
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