This market is inspired by a market originally by 

Will a non-crappy video equivalent of dall-e be published before June 2023?

So first off, buyer beware as this market, much like the inspiration market, may be up to interpretation and does not abide by best practices of it being something mapable by a third-party, quantitative metric with a threshold, but rather by the market maker's interpretation of evidence provided in the comments.

Recently, a paper came out from OpenAI researches called 

, which is a 3D object generator. From the paper, Shap-E is an improvement over a previous model called 

. Whereas Point-E modeled point clouds, Shap-E uses something called Neural Radiance Fields (NeRF) which represents a scene as an implicit function. 

An implicit function is a mathematical function that involves variables implicitly, as opposed to explicitly having a relationship,  as is the case in an explicit function. Whereas in this function:

 is explicitly a function of x. Whereas in the function:

Point-E used an explicit generative model over point clouds to generate 3D assets, unlike Shap-E which directly generates the parameters of implicit functions. So in rendering a 3D object, Point-E uses a Gaussian Diffusion process was used to mimic how a light source illuminates a scene.

−1​ at the previous time step. The state 

−1​, where the noise is Gaussian with mean sqrt(1−

​ determines the amount of noise added at each time step.

 is a Gaussian random variable which explicitly defines xt, and 

So what they did is reversed the above Noising Process, 

​), setting the goal of learning the paremeters 

 of the neural network so that it can accurately reverse the noising process. So as a result, Point-E was able to generate images which are very detailed like the following:

Contrast this to Shap-E, which implicitly maps an input pair with : 

The given equation, Neural Radiance Field (NeRF), defines a function 

Θ​ takes a spatial coordinate and a viewing direction as input and computes a color and a density as output. The exact way in which it computes the color and density would be determined by the parameters Θ. So with that mathematical background understood, Shap-E was trained in two stages:

Coherency was evidently given priority over diversity of samples.

Shap-E was able to generate images which were, "pleasing" and did not skip out on parts of the model as opposed to Point-E, like the following:

I was able to render an image of a dog with a HuggingFace demo:

and I was able to successfully convert this into a GLTF file: