The author presents ViewDiff, a method that leverages pretrained text-to-image models to generate high-quality, multi-view consistent images of real-world 3D objects. By integrating novel layers into the U-Net architecture, the approach produces diverse and realistic renderings.
Pretrained text-to-image models can be leveraged to generate high-quality, multi-view consistent images of real-world 3D objects.