6 papers
Learning-guided Kansa collocation for forward and inverse PDEs beyond linearity
Zheyuan Hu, Weitao Chen, Cengiz Ãztireli +2
Partial Differential Equations are precise in modelling the physical, biological and graphical phenomena. However, the numerical methods suffer from the curse of dimensionality, hi…
Implicit neural representation of textures
Albert Kwok, Zheyuan Hu, Dounia Hammou
Implicit neural representation (INR) has proven to be accurate and efficient in various domains. In this work, we explore how different neural networks can be designed as a new tex…
Machine Learning for Energy-Performance-aware Scheduling
Zheyuan Hu, Yifei Shi
In the post-Dennard era, optimizing embedded systems requires navigating complex trade-offs between energy efficiency and latency. Traditional heuristic tuning is often inefficient…
M^3ashy: Multi-Modal Material Synthesis via Hyperdiffusion
Chenliang Zhou, Zheyuan Hu, Alejandro Sztrajman +3
High-quality material synthesis is essential for replicating complex surface properties to create realistic scenes. Despite advances in the generation of material appearance based…
FreNBRDF: A Frequency-Rectified Neural Material Representation
Chenliang Zhou, Zheyuan Hu, Cengiz Oztireli
Accurate material modeling is crucial for achieving photorealistic rendering, bridging the gap between computer-generated imagery and real-world photographs. While traditional appr…
CHOrD: Generation of Collision-Free, House-Scale, and Organized Digital Twins for 3D Indoor Scenes with Controllable Floor Plans and Optimal Layouts
Chong Su, Yingbin Fu, Zheyuan Hu +6
We introduce CHOrD, a novel framework for scalable synthesis of 3D indoor scenes, designed to create house-scale, collision-free, and hierarchically structured indoor digital twins…