Ayush Tewari (@_atewari) 's Twitter Profile
Ayush Tewari

@_atewari

Assistant Professor @Cambridge_Eng

ID: 1077992450432499712

linkhttps://ayushtewari.com calendar_today26-12-2018 18:20:03

102 Tweet

1,1K Followers

527 Following

mansin (@mankaran32) 's Twitter Profile Photo

My second hand redmi note 9 pro running flowpilot is driving my alto k10 šŸ˜‚Can it get more desi than this ? #flowpilot #openpilot #ai #robotics #autonomous #cars #Android

VCAI - MPI for Informatics (@vcaimpi) 's Twitter Profile Photo

Excited to present some experiments we did with our partners from Volucap - Volumetric Studio on using neural rendering for creating a bullet time effect on a scene from #Matrix4šŸŽ„šŸŒ NR-Nerf project page: vcai.mpi-inf.mpg.de/projects/nonri… Volucap project page: volucap.com/portfolio-item…

Vincent Sitzmann (@vincesitzmann) 's Twitter Profile Photo

Me and some members of my research group (Ayush Tewari, George Cazenavette, Cameron Smith) will be at NeurIPS - talk to us about our work on training 3D diffusion models only from images (…ffusion-with-forward-models.github.io) and pixelNeRF without camera poses (scenerepresentations.org/publications/f…)! #NeurIPS2023

Shunsuke Saito (@psyth91) 's Twitter Profile Photo

2. Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering (vcai.mpi-inf.mpg.de/projects/2023-…) 12/14/2023 (Thu), 11:10pm-11:25pm Diffusion-prior meets inverse rendering for high-resolution illumination recovery!!

2. Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering (vcai.mpi-inf.mpg.de/projects/2023-…) 
12/14/2023 (Thu), 11:10pm-11:25pm
Diffusion-prior meets inverse rendering for high-resolution illumination recovery!!
Vincent Sitzmann (@vincesitzmann) 's Twitter Profile Photo

How can we learn to generate 3D scenes directly with diffusion models if we only have images, no ground-truth 3d scenes? Ayush, Tianwei and George will tell you at our poster ā€œdiffusion with Forward Modelsā€, #202!

How can we learn to generate 3D scenes directly with diffusion models if we only have images, no ground-truth 3d scenes? Ayush, Tianwei and George will tell you at our poster ā€œdiffusion with Forward Modelsā€, #202!
Andrej Karpathy (@karpathy) 's Twitter Profile Photo

🧠: ā€œLet’s but this (text)book! Nice and now… instead of reading it… let’s buy another one!ā€ šŸ’” All of the dopamine is generated only at the point of resolving to read something. After that there is no juice left šŸ˜…

Vincent Sitzmann (@vincesitzmann) 's Twitter Profile Photo

Introducing ā€œFlowMapā€, the first self-supervised, differentiable structure-from-motion method that is competitive with conventional SfM like Colmap! cameronosmith.github.io/flowmap/ IMO this solves a major missing piece for internet-scale training of 3D Deep Learning methods. 1/n

Ayush Tewari (@_atewari) 's Twitter Profile Photo

Excited to announce that I will be joining the University of Cambridge Engineering Dept as an assistant professor in spring 2025! I will be looking for students for the next year. Check out Elliott / Shangzhe Wu's thread for details on how to apply, and get in touch!

Excited to announce that I will be joining the University of Cambridge <a href="/Cambridge_Eng/">Engineering Dept</a> as an assistant professor in spring 2025! 

I will be looking for students for the next year. Check out <a href="/elliottszwu/">Elliott / Shangzhe Wu</a>'s thread for details on how to apply, and get in touch!
Cambridge MLG (@cambridgemlg) 's Twitter Profile Photo

✨Applications are now open for PhDs at the Cambridge Machine Learning Group!✨ We're looking for outstanding candidates interested in fundamental ML research and applications to scientific domains! More info: mlg.eng.cam.ac.uk/phd_programme_… 🧵Find more about PIs & focus areas below!

Haydn Belfield (@haydnbelfield) 's Twitter Profile Photo

Consider applying! Supervisors are Jose Miguel Hernandez-Lobato, Carl Rasmussen, Richard E. Turner, Adrian Weller, Hong Ge and Ayush Tewari.

Vincent Sitzmann (@vincesitzmann) 's Twitter Profile Photo

If you are looking to do a PhD on inverse graphics, 3D computer vision, differentiable rendering, etc, please apply to Ayush's lab at the University of Cambridge! He is brilliant, very patient, and a kind human :)

Tangible (@tangiblerobots) 's Twitter Profile Photo

Robots can do flips and play chess, but they still can’t grab a snack or clean your table. Teleoperation is the key to unlocking real dexterity. It’s not a compromise—it’s a proven, powerful approach that combines human intuition with robotic precision. We’re not just using

Andrea Tagliasacchi šŸ‡ØšŸ‡¦ (@taiyasaki) 's Twitter Profile Photo

šŸ“¢šŸ“¢šŸ“¢ "š‘šššš¢ššš§š­ š…šØššš¦: Real-Time Differentiable Ray Tracing", a mesh-based 3D represention. radfoam.github.io arxiv.org/abs/2502.01157 Co-lead by my PhD students Shrisudhan Govindarajan and Daniel Rebain, and w/ Kwang Moo Yi

Rika Antonova (@contactrika) 's Twitter Profile Photo

Join our team at Cambridge! We have fully funded PhD positions in robot learning, novel robot hardware design, and reinforcement learning. Looking for applicants with a strong background in dexterous manipulation & hardware prototyping. Interested? Please send me a message/email.

Join our team at Cambridge! We have fully funded PhD positions in robot learning, novel robot hardware design, and reinforcement learning. Looking for applicants with a strong background in dexterous manipulation &amp; hardware prototyping.
Interested? Please send me a message/email.
Shivam Duggal (@shivamduggal4) 's Twitter Profile Photo

Compression is the heart of intelligence From Occam to Kolmogorov—shorter programs=smarter representations Meet KARL: Kolmogorov-Approximating Representation Learning. Given an image, token budget T & target quality šœ– —KARL finds the smallest t≤T to reconstruct it within šœ–šŸ§µ

Compression is the heart of intelligence
From Occam to Kolmogorov—shorter programs=smarter representations

Meet KARL: Kolmogorov-Approximating Representation Learning.

Given an image, token budget T &amp; target quality šœ– —KARL finds the smallest t≤T to reconstruct it within šœ–šŸ§µ