Most research time isn’t spent thinking of ideas.
It’s spent checking prior work, testing hypotheses, debugging silent bugs, organizing results, making slides.
The parts where weeks of work silently disappear.
I tried encoding some of that experience as “Agent Skills”.
me: "can you use whatever resources you like, and python, to generate a short 'youtube poop' video and render it using ffmpeg ? can you put more of a personal spin on it? it should express what it's like to be a LLM"
claude opus 4.6:
To conclude:
👉#SDFit exploits an explicit, morphable 3D shape prior (mSDF) to generate 3D hypotheses, and a feedback loop to fit the mSDF hypothesis to an img
👉#SDFit is a good step toward robust 3D perception, and provides insights toward methods that better 'understand' rather than 'hallucinate'
(5/6🧵)
Intelligent systems need to understand #3D Objects from single images. However, recent SotA methods work well for unoccluded views & common poses, and prioritize shape over pose. We tackle this by developing #SDFit#ICCV2025 a novel Render-n-Compare framework for 3D objects that:
👉 Is robust to occlusion & uncommon poses
👉 Treats both Shape & Pose as 1st class citizens
(1/6🧵)
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