deep learning

We incorporate inductive biases useful for multiple view geometry into this generalist model without having to touch its architecture, by instead encoding them directly as additional inputs.

Fueled by the proliferation of consumer-level 3D acquisition devices and the growing accessibility of shape modeling applications for …

We extend classic displacement mapping to the neural implicit framework. The resulting novel implicit representation demonstrates superior reconstruction accuracy, parameter efficiency and enable implicit shape editing such as detail transfer.

According to one implementation, a video processing system includes a computing platform having a hardware processor and a system …

Inter-dependent explicit representations for optimizing neural implicit surfaces

A talk summarizing pretty much everything I’ve done in my IGL PhD.

Geometric details in 3D shapes is a defining factor in many industries such as AR/VR, VFX and design. However, creating high-fidelity …

We propose a novel learnable representation for detail-preserving shape deformation extending a traditional cage-based deformation technique. We demonstrate the utility of our method for synthesizing shape variations and deformation transfer.