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Efficient Editing of Aged Object Textures

This paper presents a system for efficient editing of aging effects on object textures, increasing realism and reducing workload for artists. The system allows for interactive editing with no complex parameters.

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Efficient Editing of Aged Object Textures

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  1. Efficient Editing of Aged Object Textures By: Olivier Clément Jocelyn Benoit Eric Paquette Multimedia Lab

  2. Introduction • Realistic image synthesis • Virtual reality, video games, special effects, etc. • Aging (or weathering) • Many effects • Many objects • Time consuming

  3. Introduction Redesign iterations

  4. Outline • Objectives • Previous Work • Aging Editing Process • Segmentation Phase • Elimination Phase • Reproduction Phase • Results and Limitations • Conclusion

  5. Objectives • To build a system • To edit aging effects on textures • To increase realism • To reduce the amount of work • Adapted for artists • adequate control • interactive • no complex parameters

  6. Outline • Objectives • Previous Work • Aging Editing Process • Segmentation Phase • Elimination Phase • Reproduction Phase • Results and Limitations • Conclusion

  7. Previous Work Aging methods • Physically based methods [Dorsey and Hanharan 2000; Merillou et al. 2001; O’Brien et al. 2002; etc.] • Highly realistic results but lengthy calculations • Non-intuitive physical parameters • Empirical methods [Chain et al. 2005; Gobron and Chiba 2001; Paquette et al. 2002; etc.] • More intuitive parameters • Both approaches • Do not provide the control required by artists • Target a single aging effect

  8. Previous Work Aging methods • Image based [Gu et al. 2006; Wang et al. 2006; etc.] • Capture the time-varying aspects of the material • Similar to our approach • Focus of our approach • Simple capture process • Adequate control

  9. Outline • Objectives • Previous Work • Aging Editing Process • Segmentation Phase • Elimination Phase • Reproduction Phase • Results and Limitations • Conclusion

  10. Aging Editing Process Process overview • Source image • Image, photograph • Containing aging effects • Target aging mask • Binary image • Desired pattern • Reproduction image • New aging effects

  11. Aging Editing Process Phase description • Segmentation phase • Semi-automatic • Aged regions • Elimination phase • Automatic • Aging removed • Reproduction phase • Automatic • New aging effects Redesign iterations

  12. Aging Editing Process Images summary

  13. Outline • Objectives • Previous Work • Aging Editing Process • Segmentation Phase • Elimination Phase • Reproduction Phase • Results and Limitations • Conclusion 14

  14. Segmentation Phase • Identifies aged regions • Could be done with • Segmentation tools • Image editing software • Stroke-based techniqueLischinski et al. [2006] • Worked efficiently for semi-automatic identification

  15. Segmentation Phase Stroke-base technique - Video

  16. Outline • Objectives • Previous Work • Aging Editing Process • Segmentation Phase • Elimination Phase • Reproduction Phase • Results and Limitations • Conclusion 17

  17. Elimination Phase The algorithm • Constrained texture synthesis • Match the non-aged neighbourhood • Search using ANN library Arya et al. [1998] best match copy the pixel color … new best match Elimination image Source image

  18. Elimination Phase Hole-filling • The boundary pixels • Non-aged pixels in their neighbourhood • Must be filled first • The aged region is filled iteratively

  19. Outline • Objectives • Previous Work • Aging Editing Process • Segmentation Phase • Elimination Phase • Reproduction Phase • Results and Limitations • Conclusion 20

  20. Reproduction Phase The new term • Extension of the elimination algorithm • Consider the aged / non-aged context

  21. Reproduction Phase Aging effects transfer and combination • Does not synthesize the entire image • Only specified regions • Iterative construction from multiple source images

  22. Outline • Objectives • Previous Work • Aging Editing Process • Segmentation Phase • Elimination Phase • Reproduction Phase • Results and Limitations • Conclusion

  23. Results Source image Source aging mask Elimination image Reproduction image Target aging mask

  24. Results Source image Elimination image Reproduction image

  25. Results Source image Elimination image Reproduction image

  26. Results Source image Aging masks Reproduction image More results in the paper and the video…

  27. Results Efficiency 2 minutes every iteration 3 seconds every iteration 25 seconds - once 2.5 minutes - once • User interaction is minimal • Interactive computation time • Efficient for redesign iterations Obtained on a PC with 3.2 GHz CPU and 3GB of RAM

  28. Limitations • Apply only on surfaces • No fractures or deformations • Camera-based texture acquisition • Specular lighting • Surface distortion • Current implementation • Interactive on textures up to 512 x 512

  29. Outline • Objectives • Previous Work • Aging Editing Process • Segmentation Phase • Elimination Phase • Reproduction Phase • Results and Limitations • Conclusion

  30. Conclusion • A framework • To edit aging effects on textures • To reduce the amount of work needed during the redesign iterations • Benefits • Appropriate for artists • adequate control and interactivity • no complex parameters • Works well for several types of aging effects

  31. Conclusion Future work • Synthesize the target aging mask • For numerous regions • Ex: scratches • Handle layers in effects combination • Multiple effects over the same regions • Ex: dirt on top of rust • Faster synthesis • To handle higher resolution textures

  32. Questions ? • We would like to thank : And all our reviewers…

  33. Previous Work Texture synthesis • Texture synthesis [Efros 1999; Hertzmann 2001; Kwatra 2003; Lefebvre 2006; Liang 2001; etc.] • Synthesis based on neighbourhood matching • Our system • Extends from these algorithms • Specializes for the aging context

  34. Previous Work Texture synthesis • Image analogies, Hertzmaan et al. [2001] • The output image is completely synthesized • Our approach uses a similar algorithm that synthesize only regions of the output • Our approach should be considered as an extension

  35. Elimination Phase The replacement pixel • The replacement pixel is : • Selected from the non-aged pixels of the source image • One of the best neighbourhood matches • The system seeks a replacement pixel that minimizes the following L2 norm :

  36. Elimination Phase Interactivity • An exhaustive search would require processing time far from interactive • Thus, an approximation of the best match is found with the ANN library (Arya et al. [1998]) • Approximate nearest neighbour searching algorithm based on a kd-tree structure • Our feature vector is composed of the RGB components of the non-aged pixels around the pixel to replace

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