6 papers
From Particles to Fields: Reframing Photon Mapping with Continuous Gaussian Photon Fields
Jiachen Tao, Benjamin Planche, Van Nguyen Nguyen +13
Accurately modeling light transport is essential for realistic image synthesis. Photon mapping provides physically grounded estimates of complex global illumination effects such as…
Distill Video Datasets into Images
Zhenghao Zhao, Haoxuan Wang, Kai Wang +3
Dataset distillation aims to synthesize compact yet informative datasets that allow models trained on them to achieve performance comparable to training on the full dataset. While…
Consistent Instance Field for Dynamic Scene Understanding
Junyi Wu, Van Nguyen Nguyen, Benjamin Planche +11
We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding. Unlike prior methods that rely on discrete tr…
Efficient Multimodal Dataset Distillation via Generative Models
Zhenghao Zhao, Haoxuan Wang, Junyi Wu +3
Dataset distillation aims to synthesize a small dataset from a large dataset, enabling the model trained on it to perform well on the original dataset. With the blooming of large l…
CaO: Rectifying Inconsistencies in Diffusion-Based Dataset Distillation
Haoxuan Wang, Zhenghao Zhao, Junyi Wu +3
The recent introduction of diffusion models in dataset distillation has shown promising potential in creating compact surrogate datasets for large, high-resolution target datasets,…
Distilling Long-tailed Datasets
Zhenghao Zhao, Haoxuan Wang, Yuzhang Shang +2
Dataset distillation aims to synthesize a small, information-rich dataset from a large one for efficient model training. However, existing dataset distillation methods struggle wit…