Check out our latest work in video generation! 🚀
We introduce a simple human motion reward model to post-train video diffusion models for realistic human motion with correct dynamics and anatomy. 🧠🎥
Check out the project page for more videos!
Webpage: https://t.co/o8cgGnTrbO
Excited to share our latest work on video generation!!
Video models, even state-of-the-art ones like Sora-2, struggle to generate complex human motion. Here’s a video of me doing a backflip 😂 (fwiw, my real-life backflips aren’t any better 😅)
Here's how to fix it 👇
1/5
Ashutosh @chargedneutron_ is presenting ExpertAF during this poster session! We are at poster #280. Come by to chat about it!
Project page: https://t.co/nz82YDcoPk
Super excited to share some recent work that shows that pure, text-only LLMs, can see and hear without any training! Our approach, called "MILS", uses LLMs with off-the-shelf multimodal models, to caption images/videos/audio, improve image generation, style transfer, and more!
🌟Introducing our #CVPR2024 work on video object state changes
We present a novel open-world problem formulation, a new large-scale dataset, and a learning framework for temporally localizing different stages of an object state change
Explore more: https://t.co/Z63OdsDhna
Introducing our #CVPR2024 (Highlight ✨) paper, "Detours for Navigating Instructional Videos."
Imagine watching an instructional video and having questions like, "Can I add carrots here?" Our novel VidDetours enable video detours that use user queries and prior viewing context.
We have created a weakly-supervised dataset and a manually annotated testing dataset for this new task.
Paper: https://t.co/oqD5No5in9
Project page: https://t.co/42AjSfi8j4
Code:
https://t.co/OT133ktDGe
We evaluate our method on several video datasets on step forecasting, step recognition and task recognition and see consistent gains over prior work.
Project page: https://t.co/fFzlpd7pc6
Please come by the poster session today (12/12 morning session) at #129.
Introducing our #NeurIPS23 paper on using video-mined task graphs for keystep recognition in instructional videos. 🎉
We first use videos on the internet to learn a task graph and then use the learned graph to regularize keystep recognition in instructional videos.
Exciting collaboration between 14 research institutions resulting in a first-of-its kind dataset pushing the frontiers in skill understanding. Covers cooking, sports, dance and health - thus enabling research in multiple domains and tasks. Glad to be a part of this effort!🎉
1️⃣ Ego-Exo4D
A new foundational dataset + benchmark suite to support research on video learning & multimodal perception, co-developed with 14 university partners.
Details ➡️ https://t.co/DmQXYvmFIN
Core to the work is videos of skilled human activities, simultaneously capturing both first-person "egocentric" + multiple “exocentric” views.
IOC Session approves @LA28’s proposal for 5⃣ additional sports:
⚾Baseball/🥎softball, 🏏cricket, 🏈flag football, 🥍lacrosse and ⚫squash have been officially included as additional sports on the programme for the Olympic Games Los Angeles 2028. #LA28
Here's my conversation with Mark Zuckerberg, his 3rd time on the podcast, but this time we talked in the Metaverse as photorealistic avatars. This was one of the most incredible experiences of my life. It really felt like we were talking in-person, but we were miles apart 🤯 It's hard to put into words how awesome this was for someone like me who values the intimacy of in-person conversation. It gave me a glimpse of an exciting future with many new possibilities and fascinating questions about the nature of reality and human connection ❤
Timestamps:
0:00 - Introduction
0:52 - Metaverse
15:27 - Quest 3
30:16 - Nature of reality
34:54 - AI in the Metaverse
51:51 - Large language models
57:49 - Future of humanity
@MengTo Really liked your TL;DR! I also lived briefly in Japan and that spoiled me. Did you not consider moving to Japan? If no, what were the reasons?