MB Lab can look okay, but the figures are also often exaggerated and some of the presets are quite ugly and unrealistic, while many features simply don't work or break in use. There are custom morphs you can add on to make it semi-usable. MakeHuman gives rather ugly results (particularly the eyes and weirdly shaped female chest) and lacks definition in a lot of areas despite it's relatively high poly count. Daz, Fuse, and Character creator will all give you better looking, higher quality results, and HumanGen looks MUCH higher quality as well.
MH has generous rights CC0 as well, MBLab not so much. They're okay if you can't model humans, but want art with human characters and don't want to pay money. You will not get results like Human Generator with MH or MBLab no matter how hard you try. Our approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors.I don't want this to sound like an ad, but they're very much an issue of "you get what you pay for," unfortunately. The resulting 3D model of the given text can be viewed from any angle, relit by arbitrary illumination, or composited into any 3D environment. Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss.
We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D assets and efficient architectures for denoising 3D data, neither of which currently exist. Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs.
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