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20182026
most citedEarthGen: Generating the World from Top-Down Views

2 citations · 2 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CV2025

Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video

David Yifan Yao, Albert J. Zhai, Shenlong Wang

This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth predictio…

cs.CV2024

AutoVFX: Physically Realistic Video Editing from Natural Language Instructions

Hao-Yu Hsu, Zhi-Hao Lin, Albert Zhai +2

Modern visual effects (VFX) software has made it possible for skilled artists to create imagery of virtually anything. However, the creation process remains laborious, complex, and…

cs.CV2024

CropCraft: Complete Structural Characterization of Crop Plants From Images

Albert J. Zhai, Xinlei Wang, Kaiyuan Li +6

The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, cur…

cs.CV2024★ 2 cited

EarthGen: Generating the World from Top-Down Views

Ansh Sharma, Albert Xiao, Praneet Rathi +4

In this work, we present a novel method for extensive multi-scale generative terrain modeling. At the core of our model is a cascade of superresolution diffusion models that can be…

cs.CV2024

Physical Property Understanding from Language-Embedded Feature Fields

Albert J. Zhai, Yuan Shen, Emily Y. Chen +5

Can computers perceive the physical properties of objects solely through vision? Research in cognitive science and vision science has shown that humans excel at identifying materia…

cs.CV2023

On the Overconfidence Problem in Semantic 3D Mapping

Joao Marcos Correia Marques, Albert Zhai, Shenlong Wang +1

Semantic 3D mapping, the process of fusing depth and image segmentation information between multiple views to build 3D maps annotated with object classes in real-time, is a recent…