4 citations · 9 across the 15 of their papers we have counts for
18 papers
Learning Dynamic Grasping
Nga Kiu Kelly Ho, Yilin Liu, Hao Xu +1
Synthesizing believable 4D hand-object grasping sequences is important for character animation, extended reality, and embodied agents. Grasping requires a hand to approach, contact…
TopoGS: Planar Reconstruction via Topology-aware 3D Gaussian Splatting
Shanshan Pan, Jiale Chen, Yilin Liu +1
Extracting structured, parametric 3D representations from raw images remains a fundamental challenge in computer vision and graphics. While recent advancements in the 3D Gaussian S…
DualBrep: A Dual-Field Continuous Representation for B-rep Modelling
Yilin Liu, Pradeep Jayaraman, Chinthala Reddy +2
Boundary Representation (B-rep) is the most commonly used data format in Computer-Aided Design (CAD) due to its analytical precision and direct support for parametric editing. Howe…
Open-CHOIR: Open-World Contact-Aware 4D Hand-Object Interaction Reconstruction
Hao Xu, Yilin Liu, Yinqiao Wang +2
We ask whether everyday open-world monocular videos can be turned into reusable 4D interaction primitives: articulated hand motion, object shape with 6D pose over time, and the whe…
AutoBrep: Autoregressive B-Rep Generation with Unified Topology and Geometry
Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne +3
The boundary representation (B-Rep) is the standard data structure used in Computer-Aided Design (CAD) for defining solid models. Despite recent progress, directly generating B-Rep…
Learning to Solve PDEs on Neural Shape Representations
Lilian Welschinger, Yilin Liu, Zican Wang +1
Solving partial differential equations (PDEs) on shapes underpins many shape analysis and engineering tasks; yet, prevailing PDE solvers operate on polygonal/triangle meshes while…
SAGE: Structure-Aware Generative Video Transitions between Diverse Clips
Mia Kan, Yilin Liu, Niloy Mitra
Video transitions aim to synthesize intermediate frames between two clips, but naive approaches such as linear blending introduce artifacts that limit professional use or break tem…
B-repLer: Language-guided Editing of CAD Models
Yilin Liu, Niladri Shekhar Dutt, Changjian Li +1
Computer-Aided Design (CAD) models, given their compactness and precision, remain the industry standard for designing and fabricating engineering objects. However, language-guided…
Building LOD Representation for 3D Urban Scenes
Shanshan Pan, Runze Zhang, Yilin Liu +2
The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevert…
HoLa: B-Rep Generation using a Holistic Latent Representation
Yilin Liu, Duoteng Xu, Xingyao Yu +4
We introduce a novel representation for learning and generating Computer-Aided Design (CAD) models in the form of (B-Reps). Our representation u…
CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generation
Xueqi Ma, Yilin Liu, Tianlong Gao +2
We introduce CLR-Wire, a novel framework for 3D curve-based wireframe generation that integrates geometry and topology into a unified Continuous Latent Representation. Unlike conve…
T2M-X: Learning Expressive Text-to-Motion Generation from Partially Annotated Data
Mingdian Liu, Yilin Liu, Gurunandan Krishnan +2
The generation of humanoid animation from text prompts can profoundly impact animation production and AR/VR experiences. However, existing methods only generate body motion data, e…
Generating 3D House Wireframes with Semantics
Xueqi Ma, Yilin Liu, Wenjun Zhou +2
We present a new approach for generating 3D house wireframes with semantic enrichment using an autoregressive model. Unlike conventional generative models that independently proces…
Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning
Yilin Liu, Jiale Chen, Shanshan Pan +3
We introduce a novel method for acquiring boundary representations (B-Reps) of 3D CAD models which involves a two-step process: it first applies a spatial partitioning, referred to…
CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment and Reconstruction
Yilin Liu, Xuezhou Guo, Xinqi Wang +1
Our project proposes an end-to-end 3D face alignment and reconstruction network. The backbone of our model is built by Bottle-Neck structure via Depth-wise Separable Convolution. W…
Learning Reconstructability for Drone Aerial Path Planning
Yilin Liu, Liqiang Lin, Yue Hu +4
We introduce the first learning-based reconstructability predictor to improve view and path planning for large-scale 3D urban scene acquisition using unmanned drones. In contrast t…
Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset
Liqiang Lin, Yilin Liu, Yue Hu +3
We present UrbanScene3D, a large-scale data platform for research of urban scene perception and reconstruction. UrbanScene3D contains over 128k high-resolution images covering 16 s…
VGF-Net: Visual-Geometric Fusion Learning for Simultaneous Drone Navigation and Height Mapping
Yilin Liu, Ke Xie, Hui Huang
The drone navigation requires the comprehensive understanding of both visual and geometric information in the 3D world. In this paper, we present a Visual-Geometric Fusion Network(…