Our interpretability team recently released research that traced the thoughts of a large language model.
Now we’re open-sourcing the method. Researchers can generate “attribution graphs” like those in our study, and explore them interactively.
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!
Excited to announce 🚀gsplat v1.0🚀: a ⏩efficient⏩ CUDA backend for 3D Gaussian Splatting!
https://t.co/2nREWuEYwX
A drop-in replacement of the official impl. with:
- Up to 2x faster training;
- Up to 4x less GPU memory;
- Render millions of GSs in real-time;
- And more;
What SLAHMR does is super impressive! It robustly tracks the motion of people filmed with a moving camera. It’s a great showcase of how to build robust CV systems by combining several single purpose models. I used @rerundotio to dig into the implementation and it’s pretty cool.
What SLAHMR does is super impressive! It robustly tracks the motion of people filmed with a moving camera. It’s a great showcase of how to build robust CV systems by combining several single purpose models. I used @rerundotio to dig into the implementation and it’s pretty cool.