6 citations · 23 across the 22 of their papers we have counts for
23 papers
EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras
Kush Hari, Justin Kerr, Nidhya Shivakumar +7
Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. EyeRobot 2.0 physically attends…
UniMate: One Unified Model to Animate Diverse Skeletons
Linzhan Mou, Jiahui Lei, Zhiyang Dou +4
Recent advances in automatic rigging now deliver animation-ready 3D assets at scale, yet generating the motion to drive them remains a bottleneck. Existing learned animators are to…
T-Rex: Tactile-Reactive Dexterous Manipulation
Dantong Niu, Zhuoyang Liu, Zekai Wang +31
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…
Surflo: Consistent 3D Surface Flow Model with Global State
Antoine Guédon, Shu Nakamura, Nicolas Dufour +3
Geometry is invariant to viewpoint, which makes any collection of images a redundant encoding of a single 3D state. Existing feed-forward reconstruction models fail to exploit this…
Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots
Junyao Shi, Rujia Yang, Kaitian Chao +9
Today's best-explored routes towards generalist robots center on collecting ever larger "observations-in actions-out" robotics datasets to train large end-to-end models, copying a…
DIMO: Diverse 3D Motion Generation for Arbitrary Objects
Linzhan Mou, Jiahui Lei, Chen Wang +2
We present DIMO, a generative approach capable of generating diverse 3D motions for arbitrary objects from a single image. The core idea of our work is to leverage the rich priors…
MoMaps: Semantics-Aware Scene Motion Generation with Motion Maps
Jiahui Lei, Kyle Genova, George Kopanas +2
This paper addresses the challenge of learning semantically and functionally meaningful 3D motion priors from real-world videos, in order to enable prediction of future 3D scene mo…
StereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation
Haodong Li, Chen Wang, Jiahui Lei +2
Recent video depth estimation methods achieve great performance by following the paradigm of image depth estimation, i.e., typically fine-tuning pre-trained video diffusion models…
Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation
Xuyi Meng, Chen Wang, Jiahui Lei +3
Recent advances in 2D image generation have achieved remarkable quality,largely driven by the capacity of diffusion models and the availability of large-scale datasets. However, di…
ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking
Tingyang Zhang, Chen Wang, Zhiyang Dou +4
We propose ProTracker, a novel framework for accurate and robust long-term dense tracking of arbitrary points in videos. Previous methods relying on global cost volumes effectively…
VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment
Wenyan Cong, Hanqing Zhu, Kevin Wang +6
Efficiently reconstructing 3D scenes from monocular video remains a core challenge in computer vision, vital for applications in virtual reality, robotics, and scene understanding.…
HDGS: Textured 2D Gaussian Splatting for Enhanced Scene Rendering
Yunzhou Song, Heguang Lin, Jiahui Lei +2
Recent advancements in neural rendering, particularly 2D Gaussian Splatting (2DGS), have shown promising results for jointly reconstructing fine appearance and geometry by leveragi…
MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds
Jiahui Lei, Yijia Weng, Adam Harley +2
We introduce 4D Motion Scaffolds (MoSca), a modern 4D reconstruction system designed to reconstruct and synthesize novel views of dynamic scenes from monocular videos captured casu…
Track Everything Everywhere Fast and Robustly
Yunzhou Song, Jiahui Lei, Ziyun Wang +2
We propose a novel test-time optimization approach for efficiently and robustly tracking any pixel at any time in a video. The latest state-of-the-art optimization-based tracking t…
DynMF: Neural Motion Factorization for Real-time Dynamic View Synthesis with 3D Gaussian Splatting
Agelos Kratimenos, Jiahui Lei, Kostas Daniilidis
Accurately and efficiently modeling dynamic scenes and motions is considered so challenging a task due to temporal dynamics and motion complexity. To address these challenges, we p…
GART: Gaussian Articulated Template Models
Jiahui Lei, Yufu Wang, Georgios Pavlakos +2
We introduce Gaussian Articulated Template Model GART, an explicit, efficient, and expressive representation for non-rigid articulated subject capturing and rendering from monocula…
Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
Congyue Deng, Jiahui Lei, Bokui Shen +2
Equivariance has gained strong interest as a desirable network property that inherently ensures robust generalization. However, when dealing with complex systems such as articulate…
NAP: Neural 3D Articulation Prior
Jiahui Lei, Congyue Deng, Bokui Shen +2
We propose Neural 3D Articulation Prior (NAP), the first 3D deep generative model to synthesize 3D articulated object models. Despite the extensive research on generating 3D object…
EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision
Jiahui Lei, Congyue Deng, Karl Schmeckpeper +2
We introduce Equivariant Neural Field Expectation Maximization (EFEM), a simple, effective, and robust geometric algorithm that can segment objects in 3D scenes without annotations…
Equivariant Light Field Convolution and Transformer
Yinshuang Xu, Jiahui Lei, Kostas Daniilidis
3D reconstruction and novel view rendering can greatly benefit from geometric priors when the input views are not sufficient in terms of coverage and inter-view baselines. Deep lea…
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces
Yinshuang Xu, Jiahui Lei, Edgar Dobriban +1
We introduce a unified framework for group equivariant networks on homogeneous spaces derived from a Fourier perspective. We consider tensor-valued feature fields, before and after…
CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphism
Jiahui Lei, Kostas Daniilidis
While neural representations for static 3D shapes are widely studied, representations for deformable surfaces are limited to be template-dependent or lack efficiency. We introduce…
Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
Jiahui Lei, Srinath Sridhar, Paul Guerrero +3
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. Previous work on learning shape…