Introduction: (The full text is 1000 words, and it takes about 3 minutes to read.) This article will touch on some computer-related terminology (not too much), and by the end, you will have a general understanding of the development patterns of AI-based 3D generation models.

• Let’s start with the conclusion: Recently, several 3D generation models have coincidentally adopted the approach of simplifying 3D model problems into 2D model problems. Historically, successful simplification of a problem often indicates that the problem is (almost) solved.

• Instant3D: Uses a fine-tuned stable diffusion model to generate feature-continuous four views based on input text/images, and then reconstructs the 3D model using NeRF from these four views.

• One-2-3-45++: Similar to the previous model, it uses a fine-tuned SD model to input a monocular image to obtain feature-continuous multi-view images, then reconstructs the 3D model using a 3D convolutional neural network and SDF.

• Wonder3D: Based on the stable diffusion model, it fine-tunes a model capable of outputting feature-continuous multi-view RGB images and normal maps simultaneously, and finally reconstructs the 3D model using a normal fusion model.

• My answer has two parts: The first reason is easy to think of: displaying 3D models is relatively complex and not as easy to store and display as images. The second reason is deeper: what we actually need is not 2D or 3D itself, but the narrative and emotional value brought to us by each modality.