'We can democratize 3D synthesis and open up everyone’s creativity in 3D content creation.'
, which uses a text-to-image model to create a 2D image that is then optimized into volumetric NeRF data, Magic3D uses a two-stage process that takes a crude model made in low resolution and optimizes it to a higher resolution. According to the paper's authors, the Magic3D method can create 3D objects twice as quickly as DreamFusion.that is both quick and high-quality. Before making a coarse model, they use a low-resolution diffusion in the first step.
They then accelerate using a hash grid and sparse acceleration structure. In the second stage, they employ a textured mesh model that is initialized from the coarse neural representation to enable optimization using a high-resolution latent diffusion model in conjunction with an effective differentiable renderer.DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields , achieving remarkable text-to-3D synthesis results.
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