7 papers
VideoWorld 2: Learning Transferable Knowledge from Real-world Videos
Zhongwei Ren, Yunchao Wei, Xiao Yu +5
Learning transferable knowledge from unlabeled video data and applying it in new environments is a fundamental capability of intelligent agents. This work presents VideoWorld 2, wh…
Depth Anything 3: Recovering the Visual Space from Any Views
Haotong Lin, Sili Chen, Junhao Liew +5
We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of…
Trace Anything: Representing Any Video in 4D via Trajectory Fields
Xinhang Liu, Yuxi Xiao, Donny Y. Chen +4
Effective spatio-temporal representation is fundamental to modeling, understanding, and predicting dynamics in videos. The atomic unit of a video, the pixel, traces a continuous 3D…
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
Sili Chen, Hengkai Guo, Shengnan Zhu +4
Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal inconsistency in videos, hinderin…
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Zhongwei Ren, Yunchao Wei, Xun Guo +4
This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like large language…
Image Understanding Makes for A Good Tokenizer for Image Generation
Luting Wang, Yang Zhao, Zijian Zhang +3
Abstract Modern image generation (IG) models have been shown to capture rich semantics valuable for image understanding (IU) tasks. However, the potential of IU models to improve I…