23 citations · 50 across the 9 of their papers we have counts for
4 papers · 1 filter
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…