174 citations · 196 across the 14 of their papers we have counts for
14 papers
Geometry-Aware Motion Latents for Learning Robust Manipulation Policies
Yunchao Zhang, Yijia Weng, Ruizhe Liu +3
Learning motion latents for robotic manipulation heavily relies on extracting motion patterns from visual sequences, yet effective action abstractions require understanding three-d…
Robot Learning from Any Images
Siheng Zhao, Jiageng Mao, Wei Chow +11
We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a…
GaussianLens: Localized High-Resolution Reconstruction via On-Demand Gaussian Densification
Yijia Weng, Zhicheng Wang, Songyou Peng +3
We perceive our surroundings with an active focus, paying more attention to regions of interest, such as the shelf labels in a grocery store. When it comes to scene reconstruction,…
Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields
Shijie Zhou, Hui Ren, Yijia Weng +8
Recent advancements in 2D and multimodal models have achieved remarkable success by leveraging large-scale training on extensive datasets. However, extending these achievements to…
Real2Code: Reconstruct Articulated Objects via Code Generation
Zhao Mandi, Yijia Weng, Dominik Bauer +1
We present Real2Code, a novel approach to reconstructing articulated objects via code generation. Given visual observations of an object, we first reconstruct its part geometry usi…
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…
Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects
Yijia Weng, Bowen Wen, Jonathan Tremblay +4
We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states. We decompose the problem into tw…
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks
Yijia Weng, Kaichun Mo, Ruoxi Shi +2
Some extremely low-dimensional yet crucial geometric eigen-lengths often determine the success of some geometric tasks. For example, the height of an object is important to measure…
AO-Grasp: Articulated Object Grasp Generation
Carlota Parés Morlans, Claire Chen, Yijia Weng +6
We introduce AO-Grasp, a grasp proposal method that generates 6 DoF grasps that enable robots to interact with articulated objects, such as opening and closing cabinets and applian…
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Yinzhen Xu, Weikang Wan, Jialiang Zhang +10
In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up obje…
Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild
Jiayi Chen, Mi Yan, Jiazhao Zhang +6
In this work, we tackle the challenging task of jointly tracking hand object pose and reconstructing their shapes from depth point cloud sequences in the wild, given the initial po…
Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation
Xiaolong Li, Yijia Weng, Li Yi +4
Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models. To reduce the hu…
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
Yijia Weng, He Wang, Qiang Zhou +6
In this work, we tackle the problem of category-level online pose tracking of objects from point cloud sequences. For the first time, we propose a unified framework that can handle…
Unpaired Motion Style Transfer from Video to Animation
Kfir Aberman, Yijia Weng, Dani Lischinski +2
Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most e…