Cyrus A Wilson

Director, Production Engineering
Activision

I am interested in quantitative representations of motion, whether mechanical movement over various spatial scales, or the flow of information. I am also intrigued by complex systems—especially those in which components with simple rules can give rise to sophisticated emergent behaviors on a larger scale.

Brief bio

Projects

Most recently I have been exploring how modern graphics architectures can enable fundamentally new interactive techniques for artists.

Here are some recent and previous projects, in order of roughly decreasing spatial scale: from environments to faces to cells to proteins & DNA. In my case that corresponds to reverse-chronological order.

An Interactive System for Set Reconstruction from Multiple Input Sources

A fundamental challenge of visual effects is to integrate the real world with the digital world, seamlessly. Easier said than done! Among other things, you'll want digital representations of the geometry of real-world elements, such as the on-set environment. (After all, your digital characters will have to "perform" on this set!) So, do you model it by hand? For real production use, that's actually the most reliable approach... for man-made structures. For organic, natural environments—like rocks, hills, mountains—not so much! Do you try your luck at a fully automatic geometry reconstruction method? Too bad you don't have the option of going back on set to collect more data when that automated reconstruction fails!

How about an interactive approach which incorporates your direction to computationally refine a model to conform to input data? That's one of the capabilities of the system implemented by Mikhail Smirnov and teammates. Website.

Facial Cartography

So, you want to create a realistic animated face based on a real person?

  • Step 1: scan the subject’s face in the key poses you’ll use to build your blendshape rig. No problem: current scanning technologies provide tremendous detail.
  • Step 2: correspond the different facial expression scans. Crap! Automatic correspondence methods come in many flavors, but they all have one thing in common: they sort-of almost work. So... do you clean up a little here, sacrifice some detail there? ...no, wait, the mouth’s not quite right; relax this, smooth that... Or... instead just sculpt the key poses using the scans as reference? Sure, you’re discarding interesting details from all but the neutral scan, but at least you’re in control...

There must be a better way! What if we combine the precision that computation can give us, with the skilled direction the artist can provide? That’s the motivation behind Facial Cartography. Artist and computer work together to correspond each non-neutral expression scan to the neutral data. They do this simultaneously, interactively, finding a solution which best lines up fine features in the detail maps like skin pores, for a desired animation mesh which may be at much lower resolution. That means that you don’t just get morph targets for mesh vertices; you get high-resolution detail maps for each expression, precisely mapped into a common domain so you can blend them together in the animation rig!

And by the way: no dots!

Website.

Circularly Polarized Spherical Illumination Reflectometry

Polarization of light: Some days you think you finally understand the phenomenon. Those are usually followed by days when you realize you still don't. Fortunately there are some useful mathematical tools for describing polarization effects, and they work on both types of days! Polarization state can be completely described by a Stokes vector: Conveniently the components are in a linear basis, which means changes to polarization state (due to reflection off a surface, transmission through a polarizing filter, etc.) can be expressed as multiplication with a 4x4 Mueller matrix. Neat! Well, except the matrix will vary with incoming and outgoing light directions (and let's not even talk about wavelength!). So how can we apply these tools in real world scenes where we're observing an integral? Work led by Abhijeet Ghosh shows that if we use a spherical field of circularly polarized illumination, then it becomes practical to relate the combined effect of that illumination field to the Stokes vector of the observed radiance, as a function of surface orientation, index of refraction, and a specular roughness parameter. What? Does this mean we can measure per-pixel refractive index of an object? Yes it does! And more! Website.

Temporal Upsampling of Performance Geometry using Photometric Alignment

When we need to compute motion in image sequences, one of the techniques we might apply from our toolbox is optical flow. Well, if only it weren’t for that brightness constancy assumption. This is a real problem for photometric methods which need to measure a subject under multiple illumination conditions, given that the subject might be moving. In particular, a live subject, such as a human face. So do we put optical flow away and try something else? “Sorry, optical flow; better luck next time.” Not necessarily. What if we have control over the illumination conditions? Could we design them such that the sum of two conditions is equal to a third? If so, we can simultaneously compute two optical flows, aligning each of the first two complementary illumination conditions to the sum (the third). We call this the complementation constraint, and we’ve applied it to several tasks, including the capture of multiple modes of data during a performance without requiring insanely fast capture frame rates. Website.

Myosin II Contributes to Cell-scale Actin Network Treadmilling Through Network Disassembly

Okay, this one breaks from the order somewhat, but it’s been in the works for a long time. Let’s get back to the problem of cell motility: below we look at organization of actin filament growth at the front; but if a cell is to keep going, the actin network needs to be taken apart somewhere, in order to recycle the actin subunits. How and where does that happen? What’s going on at the rear of the cell? The problem of whole cell scale coordination was the question for me. Conventional wisdom says that the cell rear is pulled forward by the myosin II motor acting on actin filaments much like it does in muscle contraction. But when I inhibited that process in keratocytes, they kept going. That wasn’t supposed to happen! If I stabilized actin filaments (inhibiting disassembly) and then inhibited myosin contraction, then they stopped. That really wasn’t supposed to happen. What was going on? To get to the bottom of this, I combined the molecular manipulations with computer vision analysis of the movies taken through the microscope; all evidence pointed to myosin disassembling the actin network in the rear of the cells. Really? To make sure it wasn’t something else, my colleague Mark Tsuchida actually ripped open the cells to get direct access to the actomyosin network in relative isolation. The experiments again showed myosin disassembling the actin network; and furthermore, they recapitulated the spatial organization of myosin mediated disassembly from live cells. (In other words, in the rear.) Could it be that myosin-mediated actin network disassembly could help orchestrate the whole cell scale organization needed for coordinated movement? In case you hadn’t guessed from the excessive use of italics, this is a big deal. That’s why it’s published in Nature. And here's a longer description.

Estimating Specular Roughness and Anisotropy from Second Order Spherical Gradient Illumination

Why decompose reflectance functions in silico when the computation can be done in situ? Let light (and physics) do the math for you! Previous work demonstrated that zeroth-order and first-order (linear gradients) computational illumination can be applied to recover albedo and photometric surface normal measurements from zeroth- and first-order statistics of a reflectance function, respectively. In this work, led by Abhijeet Ghosh, second-order gradients are applied in a computational illumination approach to recover second-order statistics of a reflectance function, yielding an estimate of specular roughness, assuming that the specular lobe of a BRDF can be approximated as a Gaussian distribution. Anisotropy? No problem! The second-order real-valued spherical harmonics form a steerable basis which can be used to determine major and minor axes of anisotropy (and associated spherical roughness values) after the fact. This approach leverages calculations performed in both the physical and computational realms to obtain per-pixel specular roughness estimates, even for anisotropic materials, from only 9 input photographs. Website.

Glare Aware Photography: 4D Ray Sampling for Reducing Glare Effects of Camera Lenses

Lens flare caused by a bright light is a low spatial frequency phenomenon, right? Not quite. While it is low-frequency in the 2D integral projection measured by the sensor (or film; remember film?) of a conventional camera, certain components of lens flare, resulting from reflections off of elements in the lens, are in fact high-frequency in the 4D ray-space inside the camera body. In work I performed at Mitsubishi Electric Research Labs, we showed that by sampling said 4D ray-space at the sensor (whether using a lightfield camera or other approach) we could distinguish contributions due to glare from those of the true scene outside the camera. Video. Website. Patent.

Decomposing non-rigid cell motion via kinematic skeletonization

When we study the spatial organization of molecular processes inside a moving cell, we are confronted by the question: in which moving reference frame should we describe and analyze said processes? If we can approximate the cells as rigid objects (see below), then the problem is not hard. Well, not so hard. Well, not SO hard (see below). Anyway, given that most moving eukaryotic (non-bacterial) cells have no interest in approximating rigidity (and why should they, given that at the cellular spatial scale there’s nothing rigid about actin-polymerization-based cell motility?), what do we do? Can we find a compromise: a representation of non-rigid cell motion which is easy to understand yet faithfully reconstructs the underlying reality? This is a subject I started to explore in research presented in a SIGGRAPH 2007 poster, and would be happy to develop further, one day. Video.

Actin-myosin network reorganization breaks symmetry at the cell rear to initiate polarized cell motility

The actin polymerization engine pushes forward the leading edge of polarized, moving keratocytes. The actin polymerization engine is running in symmetric, stationary keratocytes. So, what’s different? Work led by Patricia Yam details the sequence of changes, with regard to both molecular processes and larger scale spatial reorganizations, that engage the machinery in an idling keratocyte to give rise to concerted directional motion. Paper.

Emergence of Large-Scale Cell Morphology and Movement from Local Actin Filament Growth Dynamics

The leading edge of a crawling cell is pushed forward by the addition of actin subunits to growing filaments, right? Right. But we’re talking thousands of filaments, and millions of molecules of the protein actin. <Expletive!> How is this assembly process organized into architecture, and not chaos? Work led by Catherine Lacayo explores one of the molecular mechanisms responsible for orchestrating the filament meshwork construction process, and the larger scale phenomena that emerge as a result. Paper.

A Correlation-based Approach to Calculate Rotation and Translation of Moving Cells

One of the reasons we use fish keratocytes as a model system for studying actin-polymerization-based cell motility is that these cells are able to move in a directionally persistent manner (well, apart from turns) and preserve their overall shape as they do. Wait a second, that’s somewhat like movement of a rigid object! Not entirely, but it can be approximated as such. Read the paper to find out how I managed this approximation, and worked out a method to track these cells, globally, quickly, and non-iteratively. By computing the relationship between the stationary reference frame observed through the microscope and the moving reference frame of the cell, I was then able in later work to analyze various processes, especially those relevant to dynamic spatial organization, in both contexts. Paper.

Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic

Some proteins undergo intramolecular motions as part of their function, or changes of state, etc. Others might not experience conformational changes themselves, but might differ from related proteins by a similar change in shape. Werner Krebs led work to compute principle modes of these deformations, and then assessed the suitability of such modes as a way to classify these proteins by type of motion. Paper. Website.

PartsList: a web-based system for dynamically ranking protein folds based on disparate attributes, including whole-genome expression and interaction information

When comparing proteins which aren’t all that closely related, must we assume them to be different from the ground (the sequence level) up? Not necessarily. Though the overall diversity of proteins is massive, they share a smaller library of protein “folds”: subassemblies that are combined and specialized in different ways to give rise to said diversity. It could therefore be quite useful in our study of various proteins to be able to consider them in terms of these mid-level “parts”. Jiang Qian and Brad Stenger led an effort to inventory and categorize these parts. Paper. Website.

Assessing Annotation Transfer for Genomics: Quantifying the relations between sequence, structure and function through traditional and probabilistic scores

What’s in a gene? What does its sequence tell us about the role of the product (usually a protein) that it codes for? Yes, protein structure is specified by amino acid sequence (in turn coded by the nucleotide sequence of the gene), and function is ultimately determined by structure and sequence. But computing the structure that a sequence will assume (the protein folding problem) is a significant challenge and will remain that way for some time. In the mean time, can we say something about a protein’s structure and function by analogy to a protein of similar sequence with a known (measured) structure and (experimentally characterized) function? Or more specifically, by homology? If related proteins have not diverged too much, it is likely that they share the same structure and function. But how much is “too much”? See figure 7. In this work I found similarity thresholds, beyond which the predictive value of sequence similarity to indicate functional and structural similarity drops off considerably. Paper. Website.