Exciting new work from @sihyun_yu and our team at Google Deep Mind! Memory-Augmented Latent Transformers (MALT) Diffusion, a new diffusion model specialized for long video generation! https://t.co/gcDZr5mVbf

Check out our tech report on proactive T2I agents that ask clarification questions to reduce uncertainty! With this agent, we obtain 2 times higher VQAScore in just 5 turns!🤯 We've open-sourced our agent code powered by #Gemini @GoogleDeepMind! 🚀 Code: https://t.co/67dTGDQpZI

legit treflip by Veo 2 with just one error (wheel inversion in the middle). legs move realistically for a treflip 🤯. skateboarding videos are notoriously hard to generate

Many thanks to @MeeraHahn , Wenjun Zeng, Nithish Kannen, Rich Galt, Kartikeya Badola, @_beenkim for making this happen! Stay tuned for code release!

Tired of endless prompt tweaking? We've released a tech report on proactive text-to-image agents powered by #Gemini @GoogleDeepMind! Our agents ask clarifying questions and use belief graphs to understand what you really want. https://t.co/jRwjxtqALx https://t.co/XFVvva7r96

6/ Finally, our model can be used to generate videos with consistent 3D camera motion.

2/ website: https://t.co/atH5wzRudu Our approach has two key design decisions. First, we use a causal encoder to compress images and videos in a shared latent space.

We introduce W.A.L.T, a diffusion model for photorealistic video generation. Our model is a transformer trained on image and video generation in a shared latent space. 🧵👇

If you’re having trouble keeping up with Video AI😅, there have been 5 state-of-the-art generative video models released *in last 7 days*: 🤯😎🧵

Have you ever wondered about emergent intelligence in robotic agents? This work shows interesting emergent intelligence and behaviors in blind navigation agents! Blind agents learn maps as they navigate. This allows them to navigate as successfully as an agent with vision

How can we fill in missing pulsative sensor data? Prior state-of-the-art fails in our novel setting, despite its well-defined temporal structure. Checkout our #NeurIPS2022 paper, PulseImpute, @ 4 pm CST! arxiv: https://t.co/Hbv2x7ZvkP github: https://t.co/bTManuLyEH

i trained an ai chatbot on my childhood journal entries - so that i could engage in real-time dialogue with my "inner child" some reflections below:

Dense self-supervised learning from multiple 3D viewpoints → dense feature representations that generalize both to novel object instances and to novel categories of instances. Checkout our #NeurIPS2022 paper! arxiv: https://t.co/rxzdrScII1 github: https://t.co/5n98W7Wykt

We model indoor environments using FPV panoramic navigation graphs and introduce a visiolinguistic transformer model, LED-Bert, which scores the alignment between navigation graph nodes and dialogs and achieves SOTA performance on the LED task!

✨Transformer-based Localization from Embodied Dialog with Large-scale Pre-training✨ has been accepted as an oral at @aaclmeeting! https://t.co/L7SSGfqk0w w/ @RehgJim

Today, along with my collaborators at @GoogleAI, we announce DreamBooth! It allows a user to generate a subject of choice (pet, object, etc.) in myriad contexts and with text-guided semantic variations! The options are endless. (Thread 👇) webpage: https://t.co/EDpIyalqiK 1/N

A new year, a new shameless twitter plug: Check out our Toys4K 3D object dataset 4K instances, 105 categories, 15+ instances per category https://t.co/K85J007iru

No RL, No Simulation: Learning to Navigate without Navigating abs: https://t.co/xWFQwed1ES

Check out our video on NRNS here:

Excited to present our #neurips work NRNS!