The possibility and the mechanics of altering Shape-E, Dreamfields, and Dreamfusion input datasets

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Hello OpenAI Community,

I am currently exploring the application of different datasets within generative AI models, more specifically models like Shape-E, Dreamfields, and Dreamfusion. My primary question revolves around the possibility and the mechanics of altering these models’ input datasets.

  1. Have there been any successful attempts or documented instances where an alternative dataset was used for training the Shape-E model, replacing the original dataset? If so, could you please provide some details or direct me to relevant resources?

  2. Furthermore, I’m interested in generating very specific 3D models - in this case, models of plants with non-typical shapes. As an example, if the input request is “plant in the shape of a cube,” the model should ideally generate a plant conforming to a cubic structure, rather than generating a simple cube model. Is it possible to modify the original dataset in these generative models to create such specific outputs? Would the training process be different or pose specific challenges?

Any insights or guidance regarding the aforementioned queries would be greatly appreciated.

Thank you in advance for your time and assistance.