"Robot, make me a chair" Computer-aided design (CAD) systems are tried-and-true tools used to design many of the physical objects we use each day. But CAD software requires extensive expertise to master, and many tools incorporate such a high level of detail they don't lend themselves to brainstorming or rapid prototyping. In an effort to make design faster and more accessible for non-experts, researchers from MIT and elsewhere developed an AI-driven robotic assembly system that allows people to build physical objects by simply describing them in words. Their system uses a generative AI model to build a 3D representation of an object's geometry based on the user's prompt. Then, a second generative AI model reasons about the desired object and figures out where different components should go, according to the object's function and geometry.

Neural Radiance Field (NeRF) is an implicit 3D reconstruction method that has shown immense potential and has gained significant attention for its ability to reconstruct 3D scenes solely from a set of photographs.

We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark.

In this work, we propose a novel 3D Transformer framework called LART for 3D motion transfer.

Unfortunately PBS is often computationally expensive and rarely differentiable, which limits the scope of downstream applications.