Time-varying surface appearance

For computer graphics rendering, we generally assume that the appearance of surfaces remains static over time. Yet, there are a number of natural processes that cause surface appearance to vary dramatically, such as burning of wood, wetting and drying of rock and fabric, decay of fruit skins, and corrosion and rusting of steel and copper. In this paper, we take a significant step towards measuring, modeling, and rendering time-varying surface appearance. We describe the acquisition of the first time-varying database of 26 samples, encompassing a variety of natural processes including burning, drying, decay, and corrosion. Our main technical contribution is a Space-Time Appearance Factorization (STAF). This model factors space and time-varying effects. We derive an overall temporal appearance variation characteristic curve of the specific process, as well as space-dependent textures, rates, and offsets. This overall temporal curve controls different spatial locations evolve at the different rates, causing spatial patterns on the surface over time. We show that the model accurately represents a variety of phenomena. Moreover, it enables a number of novel rendering applications, such as transfer of the time-varying effect to a new static surface, control to accelerate time evolution in certain areas, extrapolation beyond the acquired sequence, and texture synthesis of time-varying appearance.

[1]  Julie Dorsey,et al.  Rendering of Wet Materials , 1999, Rendering Techniques.

[2]  Greg Humphreys,et al.  Physically Based Rendering: From Theory to Implementation , 2004 .

[3]  P. Belhumeur,et al.  Capture, analysis and synthesis of textured surfaces with variation in illumination, viewpoint, and time , 2004 .

[4]  Andrew Gardner,et al.  A lighting reproduction approach to live-action compositing , 2002, SIGGRAPH.

[5]  Hujun Bao,et al.  Visual simulation of weathering by γ-ton tracing , 2005, SIGGRAPH 2005.

[6]  Wojciech Matusik,et al.  A data-driven reflectance model , 2003, ACM Trans. Graph..

[7]  A. Moncrieff,et al.  The colouring, bronzing, and patination of metals , 1982 .

[8]  Jean-Michel Dischler,et al.  Corrosion: Simulating and Rendering , 2001, Graphics Interface.

[9]  Hsiuying Wang Brown's paradox in the estimated confidence approach , 1999 .

[10]  Ivaldo Pontes Jankowsky,et al.  Drying behavior and permeability of Eucalyptus Grandis Lumber , 2005 .

[11]  T. Gasser,et al.  Synchronizing sample curves nonparametrically , 1999 .

[12]  Pat Hanrahan,et al.  Flow and changes in appearance , 2006, SIGGRAPH Courses.

[13]  Alexei A. Efros,et al.  Image quilting for texture synthesis and transfer , 2001, SIGGRAPH.

[14]  Gavin S. P. Miller,et al.  Efficient algorithms for local and global accessibility shading , 1994, SIGGRAPH.

[15]  J. Engel,et al.  Model Estimation in Nonlinear-regression Under Shape Invariance , 1995 .

[16]  Siu Chi Hsu,et al.  Simulating dust accumulation , 1995, IEEE Computer Graphics and Applications.

[17]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[18]  Chen Xu,et al.  Synthesis of material drying history: phenomenon modeling, transferring and rendering , 2005, NPH.

[19]  M. Alex O. Vasilescu,et al.  TensorTextures: multilinear image-based rendering , 2004, SIGGRAPH 2004.

[20]  H. Meinhardt Pattern formation in biology: a comparison of models and experiments , 1992 .

[21]  H. Müller,et al.  Kernels for Nonparametric Curve Estimation , 1985 .

[22]  Wojciech Matusik,et al.  Opacity light fields: interactive rendering of surface light fields with view-dependent opacity , 2003, I3D '03.

[23]  M. Cross,et al.  Pattern formation outside of equilibrium , 1993 .

[24]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.