Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera poses.

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

We present a method for text-driven perpetual view generation - synthesizing long-term videos of various scenes solely from an input text prompt describing the scene and camera poses.

This consistency ensures perceptual realism and immersion, enriching the overall user experience.

Reconstructing 3D shapes from single-view images has been a long-standing research problem.