Hallucinating Point Cloud into 3D Sculptural Object

Chun-Liang Li? , Eunsu Kang? , Songwei Ge? , Lingyao Zhang, Austin Dill,Manzil Zaheer, Barnabas Poczos
Carnegie Mellon University{chunlial, eunsuk, songweig, lingyaoz, abdill, manzil, bapoczos}@andrew.cmu.edu?Equal Contribution

1Introduction

Many artworks generated with the assistance of machine learning algorithms have appeared in thelast three years. Aesthetically impressive images have resulted that belong to the traditional art
area of painting and have received global recognition. The generated art images from Elgammal
et al. [7] were favored by human viewers over the images of paintings at the prestigious Miami ArtBasel. CloudPainter, which was the top prize winner of the International Robot Art Competition in2018, proved that a machine can achieve the aesthetic level of professional painters [3]. Recentlya painting that was generated by GANs [8] appeared at the art auction by Christies [2]. At thesame time, there has been very little exploration in the area of 3D objects, which traditionally would
belong to the area of Sculpture. The creative generation of 3D Object research by Lehman et al.
successfully generated “evolved” forms, however, the final form was not far from the original form
and it remained in the range of mimicking the original [12]. Another relevant work, Neural 3D Mesh
Renderer [11] focuses on adjusting the rendered mesh based on DeepDream [1] textures.
Our team of artists and machine learning researchers designed a creative algorithm that can generateauthentic sculptural artworks. These artworks do not mimic any given forms and cannot be easily
categorized into the dataset categories. Our approach extends DeepDream [1] from images to 3D
point clouds. The proposed algorithm, Amalgamated DeepDream (ADD), leverages the propertiesof point clouds to create objects with better quality than the naive extension. ADD presents promisefor the creativity of machines, the kind of creativity that pushes artists to explore novel methods ormaterials and to create new genres instead of creating variations of existing forms or styles within
one genre. For example, from Realism to Abstract Expressionism, or to Minimalism. Lastly, wepresent the sculptures that are 3D printed based on the point clouds created by ADD.

2Learning to Generate Creative Point Clouds

Using deep learning to generate new objects has been studied in different data types, such asmusic [17], images [8], 3D voxels [18] and point clouds [4, 13]. However, these models arelearned to generate examples from the “same” distribution of the given training data, insteadof learning to generate “creative” objects [7]. One alternative is to decode the combinationof latent codes of two objects in an autoencoder. Empiricalevidence shows that decoding mixed codes usually produces
semantically meaningful objects with features from the corresponding objects. This approach has also been applied to image
creation [6]. One could adopt encoding-decoding algorithms(a) Sampling(b) Interpolationfor point clouds [20, 9, 13] to the same idea. The sampling andinterpolation results based on Li et al. [13] are shown in Fig- Figure 1: Results of Li et al. [13].
ure 1.
DeepDream for Point Clouds In addition to simple generative models and interpolations, DeepDream leverages trained deep neural networks by enhancing patterns to create psychedelic and surreal images. Given a trained neural network fθ and an input image x, DeepDream aims to modify
32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada.

x to maximize (amplify) fθ (x; a), where a is an activation function of fθ . Algorithmically, we
iteratively modify x via a gradient update
xt = xt−1 + γ∇x fθ (x; a).
(1)
One special case of DeepDream is when we use a classification network as fθ
and we replace a with the outputs of the final layer corresponding to certainlabels. This is related to adversarial attack [16] and unrestricted adversarial
examples [5, 14]. However, directly extending DeepDream to point clouds withneural networks for sets [15, 21] results in undesirable point clouds with sparseareas as shown in Figure 2.
2.1

Figure 2: Naive
DeepDream.

Amalgamated DeepDream (ADD)

To avoid sparsity in the generated point cloud, one compromise is to run DeepDream with fewer iterations or a smaller learning rate, but it results in limited difference from the input point cloud. Another approach is conducting post-processing to add points to sparse areas. However, without priorknowledge of the geometry of the objects, making smooth post-processed results is non-trivial [10].
As opposed to images, mixing two point clouds
Algorithm 1: Amalgamated DeepDream (ADD)
is a surprisingly simple task, which can be doneby taking the union of two point clouds because input : trained fθ and input x, where x0 = xof their permutation invariance property. We for t = 1 . . . T dox̂ = xt−1 + γ∇x fθ (xt−1 ; a)propose a novel DeepDream variant for point
xt = x̂ ∪ x
clouds with a set-union operation in the creation
process to address the drawbacks of the naive end
extension. To be specific, in each iteration after running the gradient update (1), we amalgamate the transformed point clouds with the input
point clouds1 . We call the proposed algorithm Amalgamated DeepDream (ADD) as shown in Algorithm 1. A running example of ADD targeting the transformation of bottle into cone in ModelNet40 [19], which is the same as Figure 2, is shown in Figure 3. In each iteration, ADD enjoys theadvantage of deforming objects based on gradient updates with respect to a trained neural network
as DeepDream without creating obviously sparse areas. In the meantime, it can better preserve thefeatures of input point clouds with the amalgamation operation when we create new features basedon DeepDream updates. More created point clouds are shown in Figure 4.

(a) Input(b) Iter. 5 (c) Iter. 10Figure 3: Transforming a bottle Figure 4: ADD with single ob- Figure 5: ADD with dual andinto a cone via ADD.
ject input.
triple object input.
In addition to using union in the transformation process in every iteration, we can push this idea further by using amalgamated point clouds as input instead of a single object. The results of ADD withthe union of two or three objects are shown in Figure 5. Compared with Figure 4, ADD with multiple inputs results in objects with more geometric details benefited by a more versatile input space.
From point clouds to realizable 3D sculptures Our final goal is to create 3D sculptures in the real world. We
use standard software MeshLab and Meshmixer to reconstruct a mesh from point clouds created by ADD. We thenuse Lulzbot TAZ 3D printer with dual-extruder, transparent PLA, and dissolvable supporting material to create thesculpture of the reconstructed mesh. The printed sculptures of the point cloud in Figure 5 (RHS) are shown in Figure 6: Created Sculpture from ADD.
Figure 6.
1

We down-sample the point clouds periodically to avoid exploded number of points.

2

References[1] URL https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html.
[2] URL https://www.christies.com/features/A-collaboration-between-two-artists-one-human-one-a-machine-9332-1.aspx.
[3] URL https://robotart.org/2018-winners/.
[4] Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. Learning representations and generative models for 3d point clouds. arXiv preprint arXiv:1707.02392, 2017.
[5] T. B. Brown, N. Carlini, C. Zhang, C. Olsson, P. Christiano, and I. Goodfellow. Unrestricted
adversarial examples. arXiv preprint arXiv:1809.08352, 2018.
[6] Shan Carter and Michael Nielsen. Using artificial intelligence to augment human intelligence.
Distill, 2017.
[7] Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone. Can: Creativeadversarial networks, generating” art” by learning about styles and deviating from style norms.
arXiv preprint arXiv:1706.07068, 2017.
[8] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil
Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014.
[9] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry.
Atlasnet: A papier-mache approach to learning 3d surface generation. arXiv preprint
arXiv:1802.05384, 2018.
[10] Hugues Hoppe, Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle. Surface
reconstruction from unorganized points. 1992.
[11] Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada. Neural 3d mesh renderer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3907–3916,
2018.
[12] Joel Lehman, Sebastian Risi, and Jeff Clune. Creative generation of 3d objects with deeplearning and innovation engines. In Proceedings of the 7th International Conference on Computational Creativity, 2016.
[13] Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabas Poczos, and Ruslan Salakhutdinov.
Point cloud gan. arXiv preprint arXiv:1810.05795, 2018.
[14] Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, and Alec Jacobson. Adversarial geometry and lighting using a differentiable renderer. arXiv preprint
arXiv:1808.02651, 2018.
[15] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on pointsets for 3d classification and segmentation. CVPR, 2017.
[16] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian
Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint
arXiv:1312.6199, 2013.
[17] Aäron Van Den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, AlexGraves, Nal Kalchbrenner, Andrew W Senior, and Koray Kavukcuoglu. Wavenet: A generativemodel for raw audio. In SSW, page 125, 2016.
[18] Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. Learning aprobabilistic latent space of object shapes via 3d generative-adversarial modeling. In NIPS,2016.
[19] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, andJianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In CVPR, 2015.
3

[20] Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian. Foldingnet: Point cloud auto-encoder
via deep grid deformation. In CVPR, volume 3, 2018.
[21] Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola. Deep sets. In NIPS, 2017.

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