We exploit the falloff of acuity in the visual periphery to accelerate graphics computation by a factor of 5-6 on a desktop HD display (1920x1080). Our method tracks the user's gaze point and renders three image layers around it at progressively higher angular size but lower sampling rate. The three layers are then magnified to display resolution and smoothly composited. We develop a general and efficient antialiasing algorithm easily retrofitted into existing graphics code to minimize "twinkling" artifacts in the lower-resolution layers. A standard psychophysical model for acuity falloff assumes that minimum detectable angular size increases linearly as a function of eccentricity. Given the slope characterizing this falloff, we automatically compute layer sizes and sampling rates. The result looks like a full-resolution image but reduces the number of pixels shaded by a factor of 10-15. We performed a user study to validate these results. It identifies two levels of foveation quality: a more conservative one in which users reported foveated rendering quality as equivalent to or better than non-foveated when directly shown both, and a more aggressive one in which users were unable to correctly label as increasing or decreasing a short quality progression relative to a high-quality foveated reference. Based on this user study, we obtain a slope value for the model of 1.32-1.65 arc minutes per degree of eccentricity. This allows us to predict two future advantages of foveated rendering: (1) bigger savings with larger, sharper displays than exist currently (e.g. 100 times speedup at a field of view of 70° and resolution matching foveal acuity), and (2) a roughly linear (rather than quadratic or worse) increase in rendering cost with increasing display field of view, for planar displays at a constant sharpness.

Functions with densely interconnected expression graphs, which arise in computer graphics applications such as dynamics, space-time optimization, and PRT, can be difficult to efficiently differentiate using existing symbolic or automatic differentiation techniques. Our new algorithm, D* , computes efficient symbolic derivatives for these functions by symbolically executing the expression graph at compile time to eliminate common subexpressions and by exploiting the special nature of the graph that represents the derivative of a function. This graph has a sum of products form; the new algorithm computes a factorization of this derivative graph along with an efficient grouping of product terms into subexpressions. For the problems in our test suite D* generates symbolic derivatives which are up to 4.6 x 10 3 times faster than those computed by the symbolic math program Mathematica and up to 2.2x10 5 times faster than the non-symbolic automatic differentiation program CppAD. In some cases the D* derivatives rival the best manually derived solutions.

High contrast images are common in night scenes and other scenes that include dark shadows and bright light sources. These scenes are difficult to display because their contrasts greatly exceed the range of most display devices for images. As a result, the image constrasts are compressed or truncated, obscuring subtle textures and details. Humans view and understand high contrast scenes easily, “adapting” their visual response to avoid compression or truncation with no apparent loss of detail. By imitating some of these visual adaptation processes, we developed methods for the improved display of high-contrast images. The first builds a display image from several layers of lighting and surface properties. Only the lighting layers are compressed, drastically reducing contrast while preserving much of the image detail. This method is practical only for synthetic images where the layers can be retained from the rendering process. The second method interactively adjusts the displayed image to preserve local contrasts in a small “foveal” neighborhood. Unlike the first method, this technique is usable on any image and includes a new tone reproduction operator. Both methods use a sigmoid function for contrast compression. This function has no effect when applied to small signals but compresses large signals to fit within an asymptotic limit. We demonstrate the effectiveness of these approaches by comparing processed and unprocessed images.

We have created a system for capturing both the three-dimensional geometry and color and shading information for human facial expressions.We use this data to reconstruct photorealistic, 3D animations of the captured expressions.The system uses a large set of sampling points on the face to accurately track the three dimensional deformations of the face.Simultaneously with the tracking of the geometric data, we capture multiple high resolution, registered video images of the face.These images are used to create a texture map sequence for a three dimensional polygonal face model which can then be rendered on standard 3D graphics hardware.The resulting facial animation is surprisingly life-like and looks very much like the original live performance.Separating the capture of the geometry from the texture images eliminates much of the variance in the image data due to motion, which increases compression ratios.Although the primary emphasis of our work is not compression we have investigated the use of a novel method to compress the geometric data based on principal components analysis.The texture sequence is compressed using an MPEG4 video codec.Animations reconstructed from 512x512 pixel textures look good at data rates as low as 240 Kbits per second.

This article presents a new lossless compression algorithm for computer animation image sequences. The algorithm uses transformation information available in the animation script and floating point depth and object number information at each pixel to perform highly accurate motion prediction with vary low computation. The geometric data (i.e., the depth and object number) can either be computed during the original rendering process and stored with the image or computed on the fly during compression and decompression. In the former case the stored geometric data are very efficientlycomporessed using motion prediction and a new technique called direction coding, typically to 1 to 2 bits per pixel. The geometric data are also useful in z-buffer image compsiting and this new compression algorthm offers a very low storage overhead method for saving the information needed for this comoositing. The overall compression ratio of the new algorithm, including the geometic data overhead, in compared to conventional spatial linear prediction compression and block-matching motion. The algorithm improves on a previous motion prediction algorithm by incorporating block predictor switching and color ratio predition. The combination of thes techniques gives compression ratios 30% better than those reported previously.

This paper describes the application of space time constraints to creating transitions between segments of human body motion.The motion transition generation uses a combination of spacetime constraints and inverse kinematic constraints to generate seamless and dynamically plausible transitions between motion segments.We use a fast recursive dynamics formulation which makes it possible to use spacetime constraints on systems with many degrees of freedom, such as human figures.The system uses an interpreter of a motion expression language to allow the user to manipulate motion data, break it into pieces, and reassemble it into new, more complex, motions.We have successfully used the system to create basis motions, cyclic data, and seamless motion transitions on a human body model with 44 degrees of freedom.

This article introduces quadrature prefiltering, an accurate, efficient, and fairly simple algorithm for prefiltering polygons for scanline rendering. It renders very high quality images at reasonable cost, strongly suppressing aliasing artifacts. For equivalent RMS error, quadrature prefiltering is significantly faster than either uniform or jittered supersampling. Quadrature prefiltering is simple to implement and space-efficient; it needs only a small two-dimensional lookup table, even when computing nonradially symmetric filter kernels. Previous algorithms have required either three-dimensional tables or a restriction to radially symmetric filter kernels. Though only slightly more complicated to implement than the widely used box prefiltering method, quadrature prefiltering can generate images with much less visible aliasing artifacts.

We have developed a system for interactive manipulation of shading parameters for three dimensional rendering.The system takes as input user-defined shaders, written in a subset of C, which are then specialized for interactive use.Since users typically experiment with different values of a single shader parameter while leaving the others constant, we can benefit by automatically generating a specialized shader that performs only those computations depending on the parameter being varied; all other values needed by the shader can be precomputed and cached.The specialized shaders are as much as 95 times faster than the original user defined shader.This dramatic improvement in speed makes it possible to interactively view parameter changes for relatively complex shading models, such as procedural solid texturing.

This paper presents a new lossless compression algorithm for computer animation image sequences.The algorithm uses transformation information available in the animation script and floating point depth and object number information stored at each pixel to perform highly accurate motion prediction with very low computation.The geometric data, i.e., the depth and object number, is very efficiently compressed using motion prediction and a new technique called direction coding, typically to 1 to 2 bits per pixel.The geometric data is also useful in z-buffer image compositing and this new compression algorithm offers a very low storage overhead method for saving the information needed for z-buffer image compositing.The overall compression ratio of the new algorithm, including the geometric data overhead, is compared to conventional spatial linear prediction compression and is shown to be consistently better, by a factor of 1.4 or more, even with large frame-to-frame motion.