38 citations · 38 across the 9 of their papers we have counts for
4 papers · 1 filter
NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation
Yu Zeng, Charles Ochoa, Mingyuan Zhou +3
Standard diffusion corrupts data using Gaussian noise whose Fourier coefficients have random magnitudes and random phases. While effective for unconditional or text-to-image genera…
"PhyWorldBench": A Comprehensive Evaluation of Physical Realism in Text-to-Video Models
Jing Gu, Xian Liu, Yu Zeng +8
Video generation models have achieved remarkable progress in creating high-quality, photorealistic content. However, their ability to accurately simulate physical phenomena remains…
Category-based Galaxy Image Generation via Diffusion Models
Xingzhong Fan, Hongming Tang, Yue Zeng +2
Conventional galaxy generation methods rely on semi-analytical models and hydrodynamic simulations, which are highly dependent on physical assumptions and parameter tuning. In cont…
A Survey on Physics-based Differentiable Rendering
Yunfan Zeng, Guangyan Cai, Shuang Zhao
Physics-based differentiable rendering has emerged as a powerful technique in computer graphics and vision, with a broad range of applications in solving inverse rendering tasks. A…