GECO: Generative Image-to-3D within a SECOnd
Authors: name: Chen Wang, name: Jiatao Gu, name: Xiaoxiao Long, name: Yuan Liu, name: Lingjie Liu

3D generation has seen remarkable progress in recent years. Existing
techniques, such as score distillation methods, produce notable results but
require extensive per-scene optimization, impacting time efficiency.
Alternatively, reconstruction-based approaches prioritize efficiency but
compromise quality due to their limited handling of uncertainty. We introduce
GECO, a novel method for high-quality 3D generative modeling that operates
within a second. Our approach addresses the prevalent issues of uncertainty and
inefficiency in current methods through a two-stage approach. In the initial
stage, we train a single-step multi-view generative model with score
distillation. Then, a second-stage distillation is applied to address the
challenge of view inconsistency from the multi-view prediction. This two-stage
process ensures a balanced approach to 3D generation, optimizing both quality
and efficiency. Our comprehensive experiments demonstrate that GECO achieves
high-quality image-to-3D generation with an unprecedented level of efficiency.