Dalton Omens

I'm a Research Engineer at Epic Games, working on the next generation of AI-driven virtual characters and humans. I recieved my PhD in Computer Science from the Stanford University AI Lab, advised by Ron Fedkiw. I received my B.S. in Electrical Engineering and Computer Science at UC Berkeley in 2020.

I have worked with research teams at Epic Games, SONY, Unity, and Blizzard on cutting-edge computer graphics and AI initiatives. I have also interned at NVIDIA.

Email  /  CV  /  Bio  /  Scholar  /  LinkedIn  /  Github  /  Bluesky

Research

I'm currently interested in the reconstruction and animation of digital humans and other virtual characters, especially involving the use of novel AI techniques. More broadly, I love creating spectacular experiences by realizing virtual worlds. Most of all, I enjoy working with artists to help them realize their vision.

Using carefully captured calibration expressions can improve the semantic accuracy of facial retargeting. Implicit differentiation can be applied to black-box or non-differentiable trackers to fine-tune retargeting results for an even better result.

To simulate loose-fitting clothing, separating physically simulated dynamic modes from neurally simulated quasistatic modes aids generalization when paired with a well-crafted coarse physics model such as rope chains.

Obtaining geometry and texture from a couple selfies to create a personalized animation rig. A Simon Says process is used to match the user's expressions.

A differentiable physics engine that supports Lagrangian dynamics and hard contact constraints for articulated rigid body simulation, offering analytic gradients through features typically only available in non-differentiable simulators.

A method using convolutional neural networks for approximating the mesh deformation of complex facial rigs. Fast evaluation allows for interactive inverse kinematics and real-time performance capture.

Education