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20212026
most citedLearning Reconstructability for Drone Aerial Path Planning

3 citations · 5 across the 14 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

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…

cs.CV2026

CHOIR: 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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

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…

cs.CV2024

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…