Me

I'm currently in my fourth year of graduate study at Columbia University, co-advised by Profs. Eitan Grinspun and Ravi Ramamoorthi in the Computer Graphics Group. Before this, I did my undergrad at MIT and also spent some time as a pirate.

I am interested in computer graphics; specifically, in finding elegant representations and algorithms that work well across many visual scales.

If you're still reading this section, you must be looking for my CV.

Publications

Multiscale Texture Synthesis Charles Han, Eric Risser, Ravi Ramamoorthi, Eitan Grinspun SIGGRAPH 2008 [Project] [PDF] [BibTeX] [Video] Example-based texture synthesis algorithms have gained widespread popularity for their ability to take a single input image and create a perceptually similar non-periodic texture. However, previous methods rely on single input exemplars that can capture only a limited band of spatial scales. For example, synthesizing a continent-like appearance at a variety of zoom levels would require an impractically high input resolution. In this paper, we develop a multiscale texture synthesis algorithm. We propose a novel example-based representation, which we call an exemplar graph, that simply requires a few low-resolution input exemplars at different scales. Moreover, by allowing loops in the graph, we can create infinite zooms and infinitely detailed textures that are impossible with current example-based methods. We also introduce a technique that ameliorates inconsistencies in the user’s input, and show that the application of this method yields improved interscale coherence and higher visual quality. We demonstrate optimizations for both CPU and GPU implementations of our method, and use them to produce animations with zooming and panning at multiple scales, as well as static gigapixel-sized images with features spanning many spatial scales. Frequency Domain Normal Map Filtering Charles Han, Bo Sun, Ravi Ramamoorthi, Eitan Grinspun SIGGRAPH 2007 [Project] [PDF] [BibTeX] [Video] [Trailer] Filtering is critical for representing image-based detail, such as textures or normal maps, across a variety of scales. While mipmapping textures is commonplace, accurate normal map filtering remains a challenging problem because of nonlinearities in shading--we cannot simply average nearby surface normals. In this paper, we show analytically that normal map filtering can be formalized as a spherical convolution of the normal distribution function (NDF) and the BRDF, for a large class of common BRDFs such as Lambertian, microfacet and factored measurements. This theoretical result explains many previous filtering techniques as special cases, and leads to a generalization to a broader class of measured and analytic BRDFs. Our practical algorithms leverage a significant body of previous work that has studied lighting-BRDF convolution. We show how spherical harmonics can be used to filter the NDF for Lambertian and low-frequency specular BRDFs, while spherical von Mises-Fisher distributions can be used for high-frequency materials.