We very recently were looking at RealmDreamer, which generates scenes from prompts. Just over a month later, CAT3D, short for "Create Anything in 3D," has emerged and takes things up a notch or two.

Tri-MipRF was one of the more underrated NeRF papers to be released last year. Now we're seeing a progression of the work Tri-Mip created with Rip-NeRF.

SMERF, or Streamable Memory Efficient Radiance Fields was originally announced late last year from Google Research, to great excitement.

As SIGGRAPH 2024 approaches, some of the early papers have started to surface.

If you think you've seen a large-scale NeRF before, have you seen a 25km capture, spread across 260,000 images?

NVIDIA's recently announced RefFusion, however, takes a different approach by employing Gaussian Splatting.

Gaussian Splatting has quickly become one of the most exciting research topics in Radiance Fields, thanks to its fast training, real time rendering rates, and easy to create pipeline. The one critique that emerged was the resulting file size from captures, often venturing into the high hundreds of megabytes and up.

Since the unveiling of the Sora's large-scale generative Radiance Fields, the tech world has been buzzing with anticipation about the future of 3D scene generation. There hasn't been much public work since then showcasing what could be coming, but today we're looking at RealmDreamer, which creates scene level generations based on original text prompts.

Playing as yourself in a video game has always seemed like a fun idea. Now, we're one step closer to making that a reality with PhysAvatar.