14 citations · 20 across the 17 of their papers we have counts for
19 papers
Kirin: Animal Motion Generation from In-the-Wild Video
Brian Nlong Zhao, Zhuoyang Pan, James M. Rehg +2
Understanding animal motion is fundamental to modeling animal behavior and biomechanics, yet progress in this area lags far behind human motion research due to the scarcity of high…
Visual General Intelligence: A White Paper
Hirokatsu Kataoka, Yoshihiro Fukuhara, Yonglong Tian +18
This paper reconsiders intelligence from a vision-centered perspective and examines whether intelligence emerging from visual experience and learning may provide a pathway toward A…
RigidBench: Evaluating Rigid-Body Physics in Video Generation Models
Swarnim Jain, Shangzhe Wu
Video models are increasingly used to predict what happens next in a scene, yet the metrics commonly used to compare their outputs say little about whether the predicted objects mo…
NeuROK: Generative 4D Neural Object Kinematics
Chen Geng, Guangzhao He, Yue Gao +3
Data-driven approaches have revolutionized 3D vision, enabling transformers to effectively reconstruct and generate static 3D objects. However, generating simulative 4D dynamics --…
RareSpot+: A Benchmark, Model, and Active Learning Framework for Small and Rare Wildlife in Aerial Imagery
Bowen Zhang, Jesse T. Boulerice, Charvi Mendiratta +4
Automated wildlife monitoring from aerial imagery is vital for conservation but remains limited by two persistent challenges: the difficulty of detecting small, rare species and th…
Choreographing a World of Dynamic Objects
Yanzhe Lyu, Chen Geng, Karthik Dharmarajan +4
Dynamic objects in our physical 4D (3D + time) world are constantly evolving, deforming, and interacting with other objects, leading to diverse 4D scene dynamics. In this paper, we…
Web-Scale Collection of Video Data for 4D Animal Reconstruction
Brian Nlong Zhao, Jiajun Wu, Shangzhe Wu
Computer vision for animals holds great promise for wildlife research but often depends on large-scale data, while existing collection methods rely on controlled capture setups. Re…
Category-Agnostic Neural Object Rigging
Guangzhao He, Chen Geng, Shangzhe Wu +1
The motion of deformable 4D objects lies in a low-dimensional manifold. To better capture the low dimensionality and enable better controllability, traditional methods have devised…
Anymate: A Dataset and Baselines for Learning 3D Object Rigging
Yufan Deng, Yuhao Zhang, Chen Geng +2
Rigging and skinning are essential steps to create realistic 3D animations, often requiring significant expertise and manual effort. Traditional attempts at automating these proces…
Birth and Death of a Rose
Chen Geng, Yunzhi Zhang, Shangzhe Wu +1
We study the problem of generating temporal object intrinsics -- temporally evolving sequences of object geometry, reflectance, and texture, such as a blooming rose -- from pre-tra…
The Scene Language: Representing Scenes with Programs, Words, and Embeddings
Yunzhi Zhang, Zizhang Li, Matt Zhou +2
We introduce the Scene Language, a visual scene representation that concisely and precisely describes the structure, semantics, and identity of visual scenes. It represents a scene…
Hearing Anything Anywhere
Mason Wang, Ryosuke Sawata, Samuel Clarke +3
Recent years have seen immense progress in 3D computer vision and computer graphics, with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (…
Learning the 3D Fauna of the Web
Zizhang Li, Dor Litvak, Ruining Li +6
Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan…
Ponymation: Learning Articulated 3D Animal Motions from Unlabeled Online Videos
Keqiang Sun, Dor Litvak, Yunzhi Zhang +3
We introduce a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion synthesis, ou…
Language-Informed Visual Concept Learning
Sharon Lee, Yunzhi Zhang, Shangzhe Wu +1
Our understanding of the visual world is centered around various concept axes, characterizing different aspects of visual entities. While different concept axes can be easily speci…
Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
Zhengfei Kuang, Yunzhi Zhang, Hong-Xing Yu +3
We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D c…
Seeing a Rose in Five Thousand Ways
Yunzhi Zhang, Shangzhe Wu, Noah Snavely +1
What is a rose, visually? A rose comprises its intrinsics, including the distribution of geometry, texture, and material specific to its object category. With knowledge of these in…
DOVE: Learning Deformable 3D Objects by Watching Videos
Shangzhe Wu, Tomas Jakab, Christian Rupprecht +1
Learning deformable 3D objects from 2D images is often an ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template…
De-rendering the World's Revolutionary Artefacts
Shangzhe Wu, Ameesh Makadia, Jiajun Wu +3
Recent works have shown exciting results in unsupervised image de-rendering -- learning to decompose 3D shape, appearance, and lighting from single-image collections without explic…