Instruct 3D-to-3D

Instruct 3D-to-3D is a pioneering framework designed for the precise manipulation of 3D environments through natural language instructions. Unlike traditional 3D generative models that create assets from scratch, this system specializes in the '3D-to-3D' task: taking an existing 3D scene—typically represented as a Neural Radiance Field (NeRF) or 3D Gaussian Splat (3DGS)—and modifying it according to text prompts (e.g., 'Make it look like a snowy winter day'). Technically, the architecture utilizes a pre-trained image-to-image diffusion model (like InstructPix2Pix) and applies it to multiple views of the 3D scene. It employs an Iterative Dataset Update (IDU) method, where the underlying 3D representation is optimized against the progressively edited 2D views, ensuring global 3D consistency and high-frequency detail preservation. By 2026, this technology has become a cornerstone in digital twin management and virtual production, allowing architects and game designers to rapidly prototype stylistic variations of real-world scans without manual re-modeling. Its ability to maintain structural integrity while altering semantic properties makes it superior to simple texture swapping techniques.