- Co-founder, World Labs12
Justin Johnson is one of the founders of World Labs, the spatial intelligence company started with Fei-Fei Li, Ben Mildenhall and Christoph Lassner, and Johnson's GitHub profile lists World Labs as Johnson's organization. Johnson is first author of papers on perceptual losses for real-time style transfer, the CLEVR visual reasoning dataset and DenseCap dense captioning, and a co-author of Mesh R-CNN and PyTorch3D.1234567
At a glance
- Current role
- Co-founder
- Company
- World Labs
- Sector
- Company
- Work listed
- 3
- Sources cited
- 8
- Profile reviewed
Career
World Labs lists Johnson among its founders, together with Fei-Fei Li, Ben Mildenhall and Christoph Lassner, describing the founding team as working across machine learning, generative AI, computer vision and graphics. Johnson's GitHub profile names World Labs as Johnson's organization.12
Research
Johnson's early papers, written with Fei-Fei Li and others, include DenseCap (2015) on dense image captioning with Andrej Karpathy, the perceptual-losses paper (2016) on real-time style transfer and super-resolution, and the CLEVR dataset (2016) for diagnosing visual reasoning. Johnson's GitHub account hosts the reference code for the perceptual-losses paper in the fast-neural-style repository, alongside the densecap repository.53482
Later work moved toward 3D understanding: Johnson co-authored Mesh R-CNN (2019) with Georgia Gkioxari and Jitendra Malik, which predicts 3D meshes from images, and the 2020 paper describing the PyTorch3D library.67
Work and contributions
Perceptual Losses for Real-Time Style Transfer and Super-Resolution Published research
First author, with Alexandre Alahi and Fei-Fei Li, of the 2016 paper that trains feed-forward networks for image transformation tasks such as style transfer and super-resolution using perceptual loss functions.38
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning Open data
First author of the 2016 paper introducing CLEVR, a diagnostic dataset for testing compositional language and elementary visual reasoning.4
Accelerating 3D Deep Learning with PyTorch3D Open-source code
Co-author of the 2020 paper describing PyTorch3D, a library for 3D deep learning.7
Timeline
- Posted DenseCap, on fully convolutional localization networks for dense captioning, as first author.5
- Posted the perceptual-losses paper on real-time style transfer and super-resolution as first author.3
- Posted the CLEVR visual reasoning dataset paper as first author.4
- Co-authored Mesh R-CNN with Georgia Gkioxari and Jitendra Malik.6
- Co-authored the paper describing the PyTorch3D library for 3D deep learning.7
Where they work
- World Labs 4 people in the wiki
Related people
- Ben MildenhallAlso at World Labs
- Christoph LassnerAlso at World Labs
- Fei-Fei LiAlso at World Labs
- Luke Melas-KyriaziComputer vision · Image, video, and 3D generation
- Miika AittalaImage, video, and 3D generation · Computer vision
- Patrick EsserImage, video, and 3D generation · Computer vision
- Angjoo KanazawaComputer vision · Image, video, and 3D generation
- Phillip IsolaComputer vision · Image, video, and 3D generation
- Ron MokadyImage, video, and 3D generation · Computer vision
- David LuebkeImage, video, and 3D generation · Computer vision
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References
8 sources · profile reviewed Oct 22, 2026 · data updated Oct 22, 2026
- About | World Labs
- jcjohnson (Justin Johnson) on GitHub
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution (arXiv 1603.08155)
- CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning (arXiv 1612.06890)
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning (arXiv 1511.07571)
- Mesh R-CNN (arXiv 1906.02739)
- Accelerating 3D Deep Learning with PyTorch3D (arXiv 2007.08501)
- jcjohnson/fast-neural-style README (code for the perceptual-losses paper)