Product lighting design is a laborious and time-consuming task. With product illustrations being increasingly rendered, the lighting challenge transferred to the virtual realm. Our approach targets lighting design in the ...

Pre-computed models of sky radiance are a tool to rapidly determine incident solar irradiance in applications as diverse as movie VFX, lighting simulation for architecture, experimental biology, and flight simulators. ...

Evaluating shading functions on geometry surfaces dominates the rendering computation. A high-quality but time-consuming estimate is usually achieved with a dense sampling rate for pixels or sub-pixels. In this paper, we ...

We present a learning based temporal supersampling algorithm for real-time rendering. Different from existing learning-based approaches that adopt an end-to-end training of a 'black-box' neural network, we design a 'white-box' ...

In this paper we propose a novel and efficient rasterization-based approach for direct rendering of isosurfaces. Our method exploits the capabilities of task and mesh shader pipelines to identify subvolumes containing ...

Drones became popular video capturing tools. Drone videos in the wild are first captured and then edited by humans to contain aesthetically pleasing camera motions and scenes. Therefore, edited drone videos have extremely ...

High-quality portrait image editing has been made easier by recent advances in GANs (e.g., StyleGAN) and GAN inversion methods that project images onto a pre-trained GAN's latent space. However, extending the existing image ...

We propose USTNet, a novel deep learning approach designed for learning shape-to-shape translation from unpaired domains in an unsupervised manner. The core of our approach lies in disentangled representation learning that ...

The intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Although using multi-scale patches to encode the ...

Implicit neural representations, such as MLP, can well recover the topology of watertight object. However, MLP fails to recover geometric details of watertight object and complicated topology due to dealing with point cloud ...