Hong-Xing Yu

Stanford University

Papers

1

Total Citations

11

H-Index

1

About

Hong-Xing Yu is a rising researcher at the forefront of computer vision and graphics, whose work bridges the gap between static 3D reconstruction and dynamic physical simulation. His primary research areas include 3D scene understanding, neural rendering, and physically based modeling of deformable objects. Yu’s most notable contribution is the introduction of "Spring-Mass 3D Gaussians," a groundbreaking framework that enables the reconstruction and simulation of elastic objects from multi-view video. This work, published in 2024, has already garnered 11 citations, signaling its rapid impact on the field. By integrating spring-mass systems with 3D Gaussian splatting, Yu has pioneered a method that not only captures the geometry and appearance of soft materials but also predicts their physical behavior under external forces—a critical advance for applications in robotics, virtual reality, and special effects. His approach stands out for its efficiency and realism, offering a practical alternative to traditional physics-based simulation pipelines. As a young scholar, Yu’s research exemplifies a growing trend toward end-to-end differentiable physics in vision, positioning him as a key voice in the next generation of computer graphics innovators.

Research Focus

Key Achievements

1
H-Index
1
Papers
11
Total Citations
11
Avg Citations/Paper
🏆 Most Cited Paper
Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians
11 citations · 2024
📈 Most Prolific Year: 2024 (1 Papers)
🤝 Key Collaborators: 3
🏛 Institutions: Stanford University

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago