GECO: Fast Generative Image-to-3D within one SECOnd.

Abstract

Recent advancements in single-image 3D generation have produced two main categories of methods: reconstruction-based and generative methods. Reconstruction-based methods are efficient but lack uncertainty handling, leading to blurry artifacts in unseen regions. Generative approaches that based on score distillation [47], [71] are slow due to scene-specific optimization. Other methods, like InstantMesh [76], use a two-stage process - generating multi-view images with a diffusion model and then reconstructing 3D - which is inefficient due to multiple denoising steps of the diffusion model. To overcome these limitations, we introduce GECO, a feed-forward method for fast and high-quality single-image-to-3D generation within one second on a single GPU. Our approach resolves uncertainty and inefficiency issues through a two-stage distillation process. In the first stage, we distill a multi-step diffusion model [56] into a one-step model using score distillation for single-image-to-multi-view synthesis. To mitigate the synthesis quality degradation caused by the one-step model, we introduce a second distillation stage to learn to predict high-quality 3D from imperfect multi-view generated images by performing distillation directly on 3D representations. Experiments demonstrate that GECO offers significant speed improvements and comparable reconstruction quality compared to prior two-stage methods. Code: https://cwchenwang.github.io/geco.