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Processor: 2x Intel Xeon Gold 6230 (20 Cores, 2.10 GHz)

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Memory: 768 GB

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Researchers and engineers at universities, start-ups, Fortune 500s, public agencies, and national labs use Lambda to power their artificial intelligence workloads.

Trusted by thousands of customers worldwide

Researchers and engineers at universities, start-ups, Fortune 500s, public agencies, and national labs use Lambda to power their artificial intelligence workloads.   

Deep Learning Workstation with 4 GPUs

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RenderNet: A Deep Conv. Network for Differentiable Rendering from 3D Shapes

Traditional computer graphics rendering pipelines are designed for procedurally generating 2D images from 3D shapes with high performance. 

Computer Science > Computer Vision and Pattern Recognition

HoloGAN: Unsupervised learning of 3D representations from natural images

Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang

(Submitted on 2 Apr 2019 (v1), last revised 1 Oct 2019 (this version, v2))

 

We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images. Most generative models rely on 2D kernels to generate images and make few assumptions about the 3D world. These models therefore tend to create blurry images or artefacts in tasks that require a strong 3D understanding, such as novel-view synthesis. HoloGAN instead learns a 3D representation of the world, and to render this representation in a realistic manner. Unlike other GANs, HoloGAN provides explicit control over the pose of generated objects through rigid-body transformations of the learnt 3D features. Our experiments show that using explicit 3D features enables HoloGAN to disentangle 3D pose and identity, which is further decomposed into shape and appearance, while still being able to generate images with similar or higher visual quality than other generative models. HoloGAN can be trained end-to-end from unlabelled 2D images only. Particularly, we do not require pose labels, 3D shapes, or multiple views of the same objects. This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.

Comments:	International Conference on Computer Vision ICCV 2019. For project page, see this https URL
Subjects:	Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:1904.01326 [cs.CV]
	(or arXiv:1904.01326v2 [cs.CV] for this version)

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Submission history

From: Thu Nguyen-Phuoc [view email][v1] Tue, 2 Apr 2019 10:36:01 UTC (4,497 KB)
[v2] Tue, 1 Oct 2019 10:41:28 UTC (3,832 KB)

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