If you are @cvprconference.bsky.social come find me at our poster: 🗓️ Sat, June 6 2026, 11:45 AM - 1:45 PM CDT 📍 ExHall F, Poster #222

Humans can watch tasks like cooking or assembly and reason about what happened, when, and between which parts. Can LVLMs do the same? We built Flat-Pack Bench to test this – and found there is still a long way to go. Accepted at #CVPR2026! 🎥🪑🧩(1/n)

G3T Up! Gravity Aligned Coordinate Frames Simplify Pointmap Processing Bharath Raj Nagoor Kani, @snavely.bsky.social tl;dr: fine-tune VGGT on custom gravity-aligned 3D data->gravity-aligned pointmaps arxiv.org/abs/2605.27372

It’s grad school application season, and I wanted to give some public advice. Caveats: -*-*-*-* 
> These are my opinions, based on my experiences, they are not secret tricks or guarantees 
> They are general guidelines, not meant to cover a host of idiosyncrasies and special cases

We are trying to create a list of in-copyright novels that contain maps. If you know of some, drop them in the thread below! 🧵👇

For those at CVPR, @justachetan.bsky.social will be presenting this poster tomorrow at 10:30 (Exhibit hall D, Poster #34). Come hear about why neural field derivatives are noisy, and how we resurrect image processing ideas for neural fields!

Thrilled to attend my first-ever #CVPR2025! 🎉 Please reach out if you would like to chat about neural fields, dynamic scenes, video understanding, or just generally about gaming, musicals, or ☕️ I will also be presenting our poster ⬇️ (Come visit!)

Check out our poster at #CVPR2025 on accurate differential operators for hybrid neural fields (like Instant NGP)! 🗓️ Fri, June 13, 10:30 AM–12:30 PM 📍 ExHall D, Poster #34 🔗 justachetan.github.io/hnf-derivati... 👉 cvpr.thecvf.com/virtual/2025... Details ⬇️ (1/n)

Reasoning about the "why" behind user behavior can improve LLM personas! ✨🧠📈 📝Excited to share our new work: Improving LLM Personas via Rationalization with Psychological Scaffolds 🔗 arxiv.org/abs/2504.17993 🧵 (1/n)

[1/10] Is scene understanding solved? Models today can label pixels and detect objects with high accuracy. But does that mean they truly understand scenes? Super excited to share our new paper and a new task in computer vision: Visual Jenga! 📄 arxiv.org/abs/2503.21770 🔗 visualjenga.github.io

Introducing MegaSaM! Accurate, fast, & robust structure + camera estimation from casual monocular videos of dynamic scenes! MegaSaM outputs camera parameters and consistent video depth, scaling to long videos with unconstrained camera paths and complex scene dynamics!