activity
20172022
most citedFourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

1.2k citations · 1.7k across the 16 of their papers we have counts for

collaborators

26 papers

cs.GR20222 cited

Decorrelating ReSTIR Samplers via MCMC Mutations

Rohan Sawhney, Daqi Lin, Markus Kettunen +4

Monte Carlo rendering algorithms often utilize correlations between pixels to improve efficiency and enhance image quality. For real-time applications in particular, repeated reser…

cs.CV20212 cited

Learning Neural Transmittance for Efficient Rendering of Reflectance Fields

Mohammad Shafiei, Sai Bi, Zhengqin Li +3

Recently neural volumetric representations such as neural reflectance fields have been widely applied to faithfully reproduce the appearance of real-world objects and scenes under…

cs.CV20211 cited

NeLF: Neural Light-transport Field for Portrait View Synthesis and Relighting

Tiancheng Sun, Kai-En Lin, Sai Bi +2

Human portraits exhibit various appearances when observed from different views under different lighting conditions. We can easily imagine how the face will look like in another set…

cs.CV20211 cited

Modulated Periodic Activations for Generalizable Local Functional Representations

Ishit Mehta, Michaël Gharbi, Connelly Barnes +3

Multi-Layer Perceptrons (MLPs) make powerful functional representations for sampling and reconstruction problems involving low-dimensional signals like images,shapes and light fiel…

cs.GR202112 cited

NeuMIP: Multi-Resolution Neural Materials

Alexandr Kuznetsov, Krishna Mullia, Zexiang Xu +2

We propose NeuMIP, a neural method for representing and rendering a variety of material appearances at different scales. Classical prefiltering (mipmapping) methods work well on si…

cs.GR2020

Light Stage Super-Resolution: Continuous High-Frequency Relighting

Tiancheng Sun, Zexiang Xu, Xiuming Zhang +6

The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subj…