We compiled a new results video for our #CVPR2022 paper 'De-rendering 3D Objects in the Wild'. Have a look (and swing by our poster on June 24th)! 😁

ArXiv: arxiv.org/abs/2201.02279
Project page: robots.ox.ac.uk/~vgg/research/…
GitHub: github.com/Brummi/derende…

CVPR2022のBest Paper Honorable Mentionのこれ面白かった。CVPRなのに音源分離？と思ったら、マジで画像（動画）から音源分離してた。
marksheinin.com/vibration

Happy to finally be able to share our #CVPR2022 paper, InstructPix2Pix!

We taught a diffusion model how to follow image editing instructions — just say how you want to edit an image, and it’ll do it!

(w/ Tim Brooks & Alyosha Efros)
More on Tim’s site:
timothybrooks.com/instruct-pix2p…
🧵

It still feels like a dream.
Yes, I am working on #Vtuber tech at Google AI!
💪😎💪

Our new BlazePose GHUM Holistic model predicts full body and hand joint rotations for 3D avatars. Available soon at mediapipe.dev. 🐎💃🎵

Paper and live demo at #CVPR2024 for #CVPR2022 .

Interested in Vision Language Pre-training (VLP) but do not know where to start? Hard to track the rapid progress in VLP? Come and join us at our CVPR2022 VLP tutorial on 19th Jun (9am-5pm CDT) in person in New Orleans or virtually. vlp-tutorial.github.io #CVPR2022

(1/N) 🎷🎷🎷 Check out our work on Neural Kernel Fields (NKF) at #cvpr2022 at Halls B2-C! We present a new type of #neuralfields based on learned kernels. NKF pushes the state of the art in shape reconstruction from points and shows unprecedented generalization ability:

Vision-Language Pre-training:
Basics, Recent Advances, and Future Trends
arxiv.org/abs/2210.09263
microsoftから、CVPR2022のチュートリアルを基に書かれたvision&languageの事前学習に関する大規模なsurvey資料
最高では

📢📢 Looking forward to present our #CVPR2022 paper Panoptic Neural Fields at poster stand 218a this morning!

🌐: abhijitkundu.info/projects/pnf/
📜: arxiv.org/abs/2205.04334
🎥: youtu.be/3aXHxuQ-xBM

With Kyle, Xiaoqi Alireza Fathi Caroline Leonidas Guibas Andrea Tagliasacchi 🇨🇦🏔️ Frank Dellaert Tom.

(1/7) Our #CVPR2022 paper “How Much More Data Do I Need? Estimating Requirements For Downstream Tasks” explores data collection practices!

w/ James Lucas David Acuna Daiqing Li Jonah Philion Jose M. Alvarez Zhiding Yu Sanja Fidler Marc T. Law

Webpage: nv-tlabs.github.io/estimatingrequ…

Excited to present our work on head avatars at #CVPR2022 . Stop by at our poster 258 on Friday morning 10:00 - 12:30 and let's have a chat!

Malte Prinzler Titus Leistner Carsten Rother Matthias Niessner Justus Thies

CVPR2022のdiffusion tutorialに載ってた図、良い
arxiv.org/abs/2112.07804

Happy to share our 'Temporal Alignment Networks for Long-term Video' in CVPR2022 (oral). Our model learns to align text and visual in untrimmed long videos without human annotation. We can get an auto-aligned web-scale video dataset for video representation learning! #CVPR2022

SotA V&L models often target specific modalities or tasks. With our fully open source FLAVA model, we aim to move beyond that. FLAVA works on a wide range of 35 tasks like image classification, NLU, retrieval and visual reasoning. Presenting it tomorrow at #CVPR2022 . A 🧵 (1/N)

Here's our latest - scaling vision transformers to gigapixel images via hierarchical self-supervised training accepted at #CVPR2022 (oral) from our superstar grad student Richard J. Chen
Paper: arxiv.org/pdf/2206.02647…
Code: github.com/mahmoodlab/HIPT
Oral: youtube.com/watch?v=cABkB1…

Yufeng (Yufeng Zheng) is about to present our paper on learning implicit morphable head avatars from videos at #CVPR2022 (oral: Image & Video Synthesis Session, 1:30pm). Sadly I can't be there in person but don't fret, Yufeng can still make my Avatar say cheeky things 😆

Interesting recent information about wheat (ስንዴ) growing locations around the globe during winter. Source: NASA Harvest (on CVPR2022) #Ethiopia is seen clearly on the map.-

Mentioning some people here 😁 Solomon Kassa Solomon Assefa Conflict Zone Abiy Ahmed Ali 🇪🇹

Glad to see Edgar Sucar, Andrew Davison 'Implicit Mapping and Positioning in Real-time' highlighted in the Neural Fields in Robotics tutorial at #cvpr2022

Take a look at the project here edgarsucar.github.io/iMAP/

At #CVPR2022 last week, we presented:
Comparing Correspondences: Video Prediction with Correspondence-wise Losses
w/ Daniel Geng, Max Hamilton

A simple change that makes off-the-shelf image similarity metrics more robust to small positional errors.

-> dangeng.github.io/CorrWiseLosses

Before I forget, I'd like to summarize some interesting papers that I found at #CVPR2022 .

Dual-key multimodal backdoors for visual question answering
arxiv.org/abs/2112.07668

1. This paper proposes an interesting Trojan attack method.

To start, what exactly is a Trojan attack?