设计领域新风潮:Low Poly 风格

背景材料

设计行业,流行趋势瞬息万变。继拟物化、扁平化、长阴影之后, Low Poly(['pɒlɪ],“低面设计”,也叫“多边形设计”)又火速掀起了最新设计风潮。

在设计领域,多边形的复杂程度确定了最终效果的细腻程度,为了让设计产品的外观看起来更加圆润、细腻、自然,通常需要柔化多边形边缘、面与面之间的过渡,并增加多边形数量。 Low Poly 则大反其道,用较少的多边形抽象打造棱角分明、简约的晶格化艺术风格,广受欢迎,一个例子便是 QQ 2015 的动态 Low Poly 登录界面背景。

QQ 2015 登录界面

QQ 2015 登录界面

传统设计领域,Low Poly 图片由设计师利用 PhotoShop、Adobe Illustrator、Cinema4D 等专业软件手工制作,不仅费时费力,而且效率低下,不能实现在线处理,应用面窄。

Phil Klay - Breno Bitencourt 为 Esquire 杂志制作的 lowpoly 风格插画

Phil Klay - Breno Bitencourt 为 Esquire 杂志制作的 lowpoly 风格插画

从理论上,Low Poly 其实是一种多边形三角分割方法(polygon triangulation)。

三角分割方法的通常方法是德劳内三角剖分(Delaunay Triangulation)。在数学和计算几何领域, 平面上的点集 \(P\) 的 Delaunay 三角化是一种三角剖分 \(DT(P)\),使得在 P 中没有点严格处于 \(DT(P)\) 中任意一个三角形外接圆的内部。Delaunay 三角化最大化了此三角剖分中三角形的最小角,换句话,此算法尽量避免出现“极瘦”的三角形。此算法命名来源于 Boris Delaunay,以纪念他自 1934 年在此领域的工作。在二维图中,判断点 \(D\) 是否落在点 \(A\)\(B\)\(C\) 的包围盒内的方法是判断一个判定矩阵的秩的正负号,即: \[\begin{vmatrix} A_x & A_y & A_x^2 + A_y^2 & 1\\ B_x & B_y & B_x^2 + B_y^2 & 1\\ C_x & C_y & C_x^2 + C_y^2 & 1\\ D_x & D_y & D_x^2 + D_y^2 & 1 \end{vmatrix} = \begin{vmatrix} A_x - D_x & A_y - D_y & (A_x^2 - D_x^2) + (A_y^2 - D_y^2) \\ B_x - D_x & B_y - D_y & (B_x^2 - D_x^2) + (B_y^2 - D_y^2) \\ C_x - D_x & C_y - D_y & (C_x^2 - D_x^2) + (C_y^2 - D_y^2) \end{vmatrix} > 0\] 如果 \(A\)\(B\)\(C\) 在空间上是逆时针顺序,只有点 \(D\) 落于三点包围盒内,判定矩阵的秩才为正。

Low Poly 与 Delaunay Triangulation 的不同在于,它不要求避免极瘦三角形的出现(也不是说不强调),而是更多强调轮廓的表达。

Delaunay Trianguation 不能有效把握整体轮廓

Delaunay Trianguation 不能有效把握整体轮廓

Low Poly 则可以有效表达出对象的骨架

Low Poly 则可以有效表达出对象的“骨架”

从视觉上,Low Poly 的处理“有粗有精”、层次分明,极具美感,而 Delaunay Triangulation 则“浑然一体”,没有侧重,或许在地形保持上更胜一筹,但用于设计领域则美感不足。

算法分析

参考: Artistic Low Poly rendering for images (Meng Gai, Guoping Wang) - Springer

策略

  1. The artists usually use relative regular triangles rather than extremely degenerated ones.
  2. The arrangement of the mesh vertices implies the structure of the objects.
  3. Non-uniform subdivision. The background is often abstracted into larger triangles than the front salient object.
  4. Color disturbance. The artists choose various colors in the flat area to make the image stereoscopic.

效果

  1. automatic (可以手动修改,但自动的效果已经很好)
  2. By constraining the edge features and the technique of color picking, our result image has a clear visual effect without zigzag artifacts.
  3. We propose a feature flow field to guide the iteration of the Voronoi diagram. The arrangement of vertices after optimization well reflects the structure of the object.
  4. A non-uniform sampling strategy based on salient region detection to make the front object and the background have different refinement density.
  5. Our method is very fast. It only costs several seconds even on images of million pixels.

相关工作

  1. NPR (Non-photorealistic rendering), but not low-poly rendering
  2. not directly. but relative
    1. Image compression
    2. Image vectorization
    3. Image tessellation
      • Some Voronoi-based methods also give results like the Low Poly style, but they have subtle difference. Besides using polygons as primary elements rather than triangles, the polygons in the Voronoi tessellation do not adapt to the shapes and details as well as the Low Poly style, so that the objects in their result images are relatively more confusing.
      • do not use PSNR value to evaluate the result.

算法流程

  1. Constrained edge feature
    1. Edge Ddrawing method + Canny edge detector → produce high-quality edge segments (clean, well-localized and one-pixel wide)
    2. a polygon approximation algorithm (classical Douglas-Peucker algorithm) → simplify the edges and leave the key points only
    3. If an edge is longer than a minimum length, we slice it into two segments from its midpoint. We choose the minimum length same as the sampling interval \(L_i\):

    \(L_i = \eta (L_w+L_h)\)

    where

    \(L_w\): image width,

    \(L_h\) image height
    \(\eta\): controls the sampling density, 0.2 in our case
    (a) Original image, (b) the edge feature, (c) the simplified polygons and the constrained points

    (a) Original image, (b) the edge feature, (c) the simplified polygons and the constrained points

  2. Sampling based on saliency

    use different sample densities between the saliency region and the background:

    \[ \begin{cases} N_s &=& \lambda (N - N_c ) \\ N_b &=& (1 - \lambda )(N - N_c ) \end{cases}\]

    where

    \(N\): total number of sampling, \(N = \lfloor{\frac{L_w}{L_i}}\rfloor \times \lfloor\frac{L_h}{L_i}\rfloor\)

    \(N_c\): constrained point numbers in the previous step
    \(\lambda\): we choose 0.7 as an empirical value

    bs-a bs-b

    bs-c bs-d

    1. Input image.
    2. Saliency map.
    3. Result with \(\lambda\) = 0.2.
    4. Result with \(\lambda\) = 0.8.
  3. Feature flow field

    1. Vertices placement of the mesh follow the local shape structure of the object → a feature flow field to guide the optimization of the vertices positions.
    2. steps

      • compute a distance map \(\operatorname{D}(x)\)
      The distance map and the feature flow map. (a) Input image, (b) the distance map, (c) the feature flow map

      The distance map and the feature flow map. (a) Input image, (b) the distance map, (c) the feature flow map

      • use the Jump Flooding method to solve this distance transform problem *. &neq; image-stippling problem, for density. instead CVT (centroidal Voronoi tessellation)

      \[F(x) = \begin{cases} \frac{255}{m} \operatorname{D}(x) \operatorname{mod}(m), \text{if} \frac{\operatorname{D}(x)}{m} \operatorname{mod}(2) = 0 \\ \frac{255}{m}(1 - \operatorname{D}(x) \operatorname{mod}(m)), \text{else} \end{cases}\]

      where
      \(m\): controls the width of the lanes interval in the feature flow map. \(m = \frac{L_i}{2}\) in our experiments
  4. Vertex optimization

    1. CVT approximates a Poisson-disk point distribution that the seeds can cover the space fairly

    2. The centroid c of a Voronoi cell is computed as follows:

      \[c = \frac{\sum_i w_i x_i}{\sum_i w_i}\]

      where
      \(x_i\): denotes the pixels in the cell
      \(w_i\) the associated weight
  5. Constrained triangulation
  6. Color post-processing

效果展示

案例分析

算法实现的 Low Poly 的案例也有不少,我选择了张雯莉的 Polyvia,因为她的代码开源在 GitHub,而且有很好的说明和 Demo。其 Low Poly 处理的主要流程是:

Polyvia 算法处理流程

Polyvia 算法处理流程

也就是 1) 边缘检测 \(\to\) 2) 从边缘中选择点 \(\to\) 3) 利用点生成 Low Poly 多边形,最后,还要对 4)每个三角形着色。在边缘检测上,张的程序使用 WebGL 着色器加速运算,效率很高。

其中 2) 的选点策略对最终效果影响较大。另一篇论文: Artistic Low Poly rendering for images (Meng Gai, Guoping Wang) - Springer 则用边缘提取产生约束边进行带约束的 Delaunay 三角化,用 Voronoi 迭代优化顶点位置,最终效果就好一些。可以看到,对同一幅图片, TRIGRAFF1 的 Delaunay 程度不够,张的 Delaunay 程度过了,而 Gai 的效果则很完美。这都和 Vertex 点的选择有关。

几种效果对比

几种效果对比

张是还利用 Three.js 做了一个在线的视频 Low Poly 处理程序,效果也不错,地址:http://zhangwenli.com/Polyvia/video.html


refs and see also

  1. 如何简单地把一张相片处理成低多边形 Low Poly 3D 风格效果? - 知乎
  2. 设计新趋势!用 PS 和 AI 打造 Low Poly 低多边形肖像
  3. “低面建模”设计美学-20140726早读课 | 互联网早读课
  4. 如何使用 JavaScript 生成 lowpoly 风格图像? - 知乎
  5. Dribbble - Phil Klay by Breno Bitencourt

  1. Art camera TRIGRAFF:在 App Store 上的内容


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