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We present Boosting3D, a multi-stage single image-to-3D generation methodthat can robustly generate reasonable 3D objects in different data domains. Thepoint of this work is to solve the view consistency problem in singleimage-guided 3D generation by modeling a reasonable geometric structure. Forthis purpose, we propose to utilize better 3D prior to training the NeRF. Morespecifically, we train an object-level LoRA for the target object usingoriginal image and the rendering output of NeRF. And then we train the LoRA andNeRF using a progressive training strategy. The LoRA and NeRF will boost eachother while training. After the progressive training, the LoRA learns the 3Dinformation of the generated object and eventually turns to an object-level 3Dprior. In the final stage, we extract the mesh from the trained NeRF and usethe trained LoRA to optimize the structure and appearance of the mesh. Theexperiments demonstrate the effectiveness of the proposed method. Boosting3Dlearns object-specific 3D prior which is beyond the ability of pre-traineddiffusion priors and achieves state-of-the-art performance in the singleimage-to-3d generation task.

Boosting3D: High-Fidelity Image-to-3D by Boosting 2D Diffusion Prior to 3D Prior with Progressive Learning