p.s. This was lobbed onto the frontpage by the second-chance pool (https://news.ycombinator.com/item?id=26998308) and I need to make sure we don't end up with duplicate threads that way.
Released last week. Looks like all the weights are now out and published. Don’t sleep on the SAM 3D series — it’s seriously impressive. They have a human pose model which actually rigs and keeps multiple humans in a scene with objects, all from one 2D photo (!), and their straight object 3D model is by far the best I’ve played with - it got a really very good lamp with translucency and woven gems in usable shape in under 15 seconds.
Are those the actual wireframes they're showing in the demos on that page? As in, do the produced models have "normal" topology? Or are they still just kinda blobby with a ton of polygons
I haven’t tried it myself, but if you’re asking specifically about the human models, the article says they’re not generating raw meshes from scratch. They extract the skeleton, shape, and pose from the input and feed that into their HMR system [0], which is a parametric human model with clean topology.
So the human results should have a clean mesh. But that’s separate from whatever pipeline they use for non-human objects.
I’ve only used the playground. But I think they are actual meshes - they don’t have any of the weird splat noise at the edge of the objects, and they do not seem to show similar lighting artifacts to a typical splat rendering.
For the objects I believe they're displaying Gaussian splats in the demo, but the model itself can also produce a proper mesh. The human poses are meshes (it's posing and adjusting a pre-defined parametric model).
Looking forward to your progress! Just checked the paper and it says the underlying backbone is still DETR. My guess would be that SAM3 uses more video frames during the training process and caused the dilution of sparse engineering-paper-like data.
Side question: what are the current top goto open models for image captioning and building image embeddings dbs, with somewhat reasonable hardware requirements?
For pure image embedding, I find DINOv3 to be quite good. For multimodal embedding, maybe RzenEmbed. For captioning I would use a regular multimodal LLM, Qwen 3 or Gemma 3 or something, if your compute budget allows.
I would suggest YOLO. Depending on your domain, you might also finetune these models. Its relativly easy as they are not big LLMs but either image classification or bounding boxes.
Alternative downloads exist. You can find torrents, and match checksums against the HF downloads, but there are also mirrors and clones right there in HF which you can download without even having to log in.
Readit News
https://news.ycombinator.com/item?id=45982073
Meta Segment Anything Model 3 - https://news.ycombinator.com/item?id=45982073 - Nov 2025 (133 comments)
p.s. This was lobbed onto the frontpage by the second-chance pool (https://news.ycombinator.com/item?id=26998308) and I need to make sure we don't end up with duplicate threads that way.
So the human results should have a clean mesh. But that’s separate from whatever pipeline they use for non-human objects.
[0]: https://github.com/facebookresearch/MHR
I would recommend bounding boxes.
Checkout https://github.com/MiscellaneousStuff/meta-sam-demo
It's a rip of the previous sam playground. I use it for a bunch of things.
Sam 3 is incredible. I'm surprised it's not getting more attention.
Remember, it's not the idea, it's the marketing!