Patternshop: Editing Point Patterns by Image Manipulation -- Supplemental Webpage

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Xingchang Huang, Max-Planck-Institut für Informatik, Germany

Tobias Ritschel, University College London, United Kingdom

Hans-Peter Seidel, Max-Planck-Institut für Informatik, Germany

Pooran Memari, CNRS, LIX, École Polytechnique, IP Paris, INRIA, France

Gurprit Singh, Max-Planck-Institut für Informatik, Germany

We provide additional results on different applications. Please click on the links to see the results.

• Application: point pattern design

• Application: image stippling reconstruction and editing (face)

• Application: image stippling reconstruction and editing (other categories)

• Application: face stippling reconstruction and editing (input from other methods)

• Application: face stippling reconstruction (identity edit)

• Application: point pattern expansion

• Failure cases

Summary:

• We use the network trained on faces to reconstruct density and correlation map, where the input comes from existing stippling methods including Zhou et al. [2012] and Salaun et al. [2022].

• We can either produce a similar point pattern to the input, or edit the point patterns to have spatially-varying correlations, compared with the original input stippling.

We show editing details at each row. The PDF link of the pointsets are provided on top of the pointset images. Please zoom-in or zoom-out the webpage accordingly.

1: sharpen the density map and edit the hair and the background correlation

Input points (synthesized by Zhou et al. [2012]) [PDF]

Network output

Editing

Our edit-aware synthesis [PDF]

Network output (L)

Network output (AB)

Editing (L)

Editing (AB)

2: edit the background correlation

Input points (synthesized by Zhou et al. [2012]) [PDF]

Network output

Editing

Our edit-aware synthesis [PDF]

Network output (L)

Network output (AB)

Editing (L)

Editing (AB)

3: change background with gradient correlation

Input points (synthesized by Salaun et al. [2022]) [PDF]

Network output

Editing

Our edit-aware synthesis [PDF]

Network output (L)

Network output (AB)

Editing (L)

Editing (AB)

4: edit correlation on hair-strand, hand and cloth

Input points (synthesized by Salaun et al. [2022]) [PDF]

Network output

Editing

Our edit-aware synthesis [PDF]

Network output (L)

Network output (AB)

Editing (L)

Editing (AB)

5: edit hair correlation

Input points (synthesized by Salaun et al. [2022]) [PDF]

Network output

Editing

Our edit-aware synthesis [PDF]

Network output (L)

Network output (AB)

Editing (L)

Editing (AB)