Justin Johnson @jcjohnss
Assistant Professor @UMich CSE; Previously Research Scientist @MetaAI; CS PhD @Stanford. Deep Learning + Computer Vision. Joined January 2014-
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We'll present Text2Room as an oral tomorrow at #ICCV2023 - talk at 9am in "Paris Sud" - poster 10:30am-12:30pm in "room Nord" Come say hi and have a chat if you're interested in our work! :)
We'll present Text2Room as an oral tomorrow at #ICCV2023 - talk at 9am in "Paris Sud" - poster 10:30am-12:30pm in "room Nord" Come say hi and have a chat if you're interested in our work! :)
How could we generate large-scale 3d scenes with text inputs? Our #ICCV2023 oral paper "Text2room" shows we can directly generate meshes of 3D scenes by combining pretrained 2D diffusion model and depth estimators. Check out our paper on tomorrow morning's oral&poster session!
We are releasing code (training + inference) and models for our ICML 2023 paper that uses hyperbolic geometry to improve CLIP-style image/text feature learning
We are releasing code (training + inference) and models for our ICML 2023 paper that uses hyperbolic geometry to improve CLIP-style image/text feature learning
(1/4) Excited to share our #ICCV2023 paper Text2Room! We generate scene-scale textured 3D meshes from a given text prompt leveraging 2D text-to-image models such as StableDiffusion. Project: lukashoel.github.io/text-to-room/ Code: github.com/lukasHoel/text… Video: youtu.be/fjRnFL91EZc
This week at #ICML I will present “Hyperbolic Image-Text Representations” — we bring hyperbolic geometry to CLIP and learn interpretable models that also match/outperform CLIP on standard vision tasks. 📜 arxiv.org/abs/2304.09172 📅 Jul 27, 10:30. Say hi! With @metaai @michigan_AI
Our new paper led by @_nileshk shows how to generate animations of people interacting with objects by guiding a diffusion model with a learned object interaction field. Diffusion models can be used to generate all kinds of data, not just images and videos!
Our new paper led by @_nileshk shows how to generate animations of people interacting with objects by guiding a diffusion model with a learned object interaction field. Diffusion models can be used to generate all kinds of data, not just images and videos! https://t.co/6TRKS8G8Kw
It was amazing presenting Omni3D: A Large Benchmark and Model for Monocular 3D Detection in the Wild @CVPR . Project+Code: garrickbrazil.com/omni3d/ Work with amazing Garrick @jstraub6 @nikhilaravi @jcjohnss @georgiagkioxari A long 🧵 1/n
CVPR ATTENDEES, REAL AND VIRTUAL: Voting for all motions has now begun, and voting closes in just under 25 hours! I will be enthusiastically voting "yes" on all motions, including Motion 3 to repeal the social media ban. Here's how to vote:
⛳️ Come chat with me, @georgiagkioxari @jcjohnss, Garrick, Abhinav & Julian about Omni3D at @CVPR today! 📌 Poster #76 ⏲️ Wednesday 4:30-6:30pm #CVPR2023
⛳️ Come chat with me, @georgiagkioxari @jcjohnss, Garrick, Abhinav & Julian about Omni3D at @CVPR today! 📌 Poster #76 ⏲️ Wednesday 4:30-6:30pm #CVPR2023
JPEG decoding converts DCT coefficients into RGB patches; a ViT converts RGB patches back into vectors. Feeding DCT coefficients directly to a ViT can give significant speedups, and satisfyingly lets the model learn from a more "raw" form of the input data.
JPEG decoding converts DCT coefficients into RGB patches; a ViT converts RGB patches back into vectors. Feeding DCT coefficients directly to a ViT can give significant speedups, and satisfyingly lets the model learn from a more "raw" form of the input data.
Our @CVPR paper introduces HexPlane: a simple, efficient representation for dynamic 3D scenes that enables us to train dynamic NeRFs more than 100x faster than prior work
Our @CVPR paper introduces HexPlane: a simple, efficient representation for dynamic 3D scenes that enables us to train dynamic NeRFs more than 100x faster than prior work
Predicting 3D scene structure from a single image is hard. Much prior work essentially distills from classical multiview reconstruction methods, treating their outputs as ground-truth. In this @CVPR paper we instead train from raw (posed) RGB-D data
Predicting 3D scene structure from a single image is hard. Much prior work essentially distills from classical multiview reconstruction methods, treating their outputs as ground-truth. In this @CVPR paper we instead train from raw (posed) RGB-D data
Check out our new work on 3D object auto-captioning with BLIP2, CLIP, and GPT4. Through human evaluations, we found our framework, Cap3D, is better, cheaper, and faster than crowdsourced annotation. Access descriptive captions, point clouds, and shap-E latent codes for…
Check out our new work on 3D object auto-captioning with BLIP2, CLIP, and GPT4. Through human evaluations, we found our framework, Cap3D, is better, cheaper, and faster than crowdsourced annotation. Access descriptive captions, point clouds, and shap-E latent codes for…
Large (text, image) datasets led to breakthroughs in text-to-image synthesis. 3D is the next frontier, but large (text, 3D) data doesn't exist. We show that BLIP-2 + GPT-4 can caption meshes with quality comparable to people, and release 660k captions for Objaverse meshes.
Large (text, image) datasets led to breakthroughs in text-to-image synthesis. 3D is the next frontier, but large (text, 3D) data doesn't exist. We show that BLIP-2 + GPT-4 can caption meshes with quality comparable to people, and release 660k captions for Objaverse meshes.
I am 💯 with the original goal behind the ban, but here's our reasoning for the repeal: Unfortunately the current effect is that well-known authors receive promotion anyway, while smaller labs receive no promotion at all and cannot even speak up when they have a similar work.
I am 💯 with the original goal behind the ban, but here's our reasoning for the repeal: Unfortunately the current effect is that well-known authors receive promotion anyway, while smaller labs receive no promotion at all and cannot even speak up when they have a similar work. https://t.co/KLY4ytLC3y
The CVPR/ICCV social media ban was well-intentioned but ineffective. Well-known authors still have their unpublished work promoted by influencers, but authors can't discuss their own work for months. This is absurd. We hope to change this. Please vote if you are attending CVPR!
The CVPR/ICCV social media ban was well-intentioned but ineffective. Well-known authors still have their unpublished work promoted by influencers, but authors can't discuss their own work for months. This is absurd. We hope to change this. Please vote if you are attending CVPR!
Check out Neural Shape Compiler: a unified framework facilitating Text<->PointCloud<->Program transformations for 3D shapes. Extendable for more modalities and conversions. Joint work with @honglaklee @jcjohnss. Project:tiangeluo.github.io/projectpages/s… Code & Pre-trained…
Hyperbolic Image-Text Representations abs: arxiv.org/abs/2304.09172
Cool demo combining Segment Anything + MCC for single-image 3D reconstruction! What an exciting time ... combining specialized models can open up new capabilities with minimal effort
Cool demo combining Segment Anything + MCC for single-image 3D reconstruction! What an exciting time ... combining specialized models can open up new capabilities with minimal effort
AK @_akhaliq
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172 Followers 367 Following Software Developer | In love with React.js | Typescript, Node.js, GraphQL, React Native, Databases, Golang, Python | current @iitdelhi ML/AI/structural engg
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PhysDreamer Physics-Based Interaction with 3D Objects via Video Generation Realistic object interactions are crucial for creating immersive virtual experiences, yet synthesizing realistic 3D object dynamics in response to novel interactions remains a significant
This AI Research Introduces PERF: The Panoramic NeRF Transforming Single Images into Explorable 3D Scenes Quick read: marktechpost.com/2023/11/08/thi… Paper: arxiv.org/abs/2310.16831… Github: github.com/perf-project/P… Project: perf-project.github.io #ArtificialInteligence #DataScience…
Core pytorch NNs (forward and backward) written in clean, readable Triton (from @bobmcdear) github.com/BobMcDear/atto…
This incredibly well-written paper illustrates how deep domain thinking can lead to simplicity and why representation still matters with today’s AI. Also, this is how one should approach writing surveys. Not “Foo did X and Bar did Y. End of story.” arxiv.org/abs/2404.11735
RefFusion from NVIDIA uses reference images to effectively inpaint, insert objects, and outpaint with Gaussian Splatting. 🔗radiancefields.com/reffusion-inpa…
Say hello to our new web-based AI World builder! 🗺️🤖🪄 Turning an idea into a controllable 3D world requires enormous effort. Cube now allows creators to generate 3D controllable videos (from images + text) for rapid ideation/UGC. Access (Maker/Pro): 3d.csm.ai/register
What do you see in these images? These are called hybrid images, originally proposed by Aude Oliva et al. They change appearance depending on size or viewing distance, and are just one kind of perceptual illusion that our method, Factorized Diffusion, can make.
New #NVIDIA paper: Real-time text-to-3D generation #ICCV2023 3D generation from text requires expensive per-prompt optimization. We train 1 model on many prompts for real-time generalization to unseen prompts, interpolations and more! ATT3D details: research.nvidia.com/labs/toronto-a…
Our computer vision textbook is released! Foundations of Computer Vision with Antonio Torralba and Bill Freeman mitpress.mit.edu/9780262048972/… It’s been in the works for >10 years. Covers everything from linear filters and camera optics to diffusion models and radiance fields. 1/4
We've raised a $1.25B infrastructure fund! We love all infra, compute, network, storage, databases, data science, gen AI, dev tools ... from silicon to UIs. Infra is the true root of value in tech. And we're deepening our commitment to it. a16z.com/new-funds-new-…
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling arxiv.org/abs/2404.09227
Meta presents Taming Latent Diffusion Model for Neural Radiance Field Inpainting Yields state-of-the-art NeRF inpainting results on various real-world scenes proj: hubert0527.github.io/MALD-NeRF/ abs: arxiv.org/abs/2404.09995
Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video proj: video2game.github.io abs: arxiv.org/abs/2404.09833
📢📢📢 3D Gaussian Splatting brought you real-time rendering, but at slightly lower PSNR compared to mipNeRF360... 𝐚𝐬 𝐨𝐟 𝐭𝐨𝐝𝐚𝐲, 𝐭𝐡𝐚𝐭 𝐢𝐬 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐭𝐫𝐮𝐞. Introducing "3D Gaussian Splatting as Markov Chain Monte Carlo" arxiv.org/abs/2404.09591
Google announces Probing the 3D Awareness of Visual Foundation Models Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their training task, their
This is an interesting, timely, and important paper. The takeaway is that "recent self-supervised models such as DINOv2 learn representations that encode depth and surface normals, with StableDiffusion being a close second". This contrasts with vision-language models like CLIP,…
Google announces Probing the 3D Awareness of Visual Foundation Models Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their training task, their
What I've learned about Apple's mysterious fisheye projection format, the format they use for their impressive Immersive videos on the Vision Pro: blog.mikeswanson.com/post/747761863…
Check out RealmDreamer (realmdreamer.github.io)--our new 3D scene generation method! No multiview data required :) One of my favorites is this: "Fantasy lighthouse in the Arctic, surrounded by a world of ice and snow, shining with a mystical light under the aurora borealis."
RealmDreamer Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion We introduce RealmDreamer, a technique for generation of general forward-facing 3D scenes from text descriptions. Our technique optimizes a 3D Gaussian Splatting representation to match
Google presents Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention 1B model that was fine-tuned on up to 5K sequence length passkey instances solves the 1M length problem arxiv.org/abs/2404.07143
Meta announces 2nd-gen inference chip MTIAv2. * 708TF/s Int8 / 353TF/s BF16 * 256MB SRAM, 128GB memory * 90W TDP. 24 chips per node, 3 nodes per rack. * standard PyTorch stack (Dynamo, Inductor, Triton) for flexibility Fabbed on TSMC's 5nm process, its fully programmable via the…
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