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.

翻译：近年来，三维生成领域取得了显著进展。现有技术（如分数蒸馏方法）虽能产生显著成果，但需要进行大量的逐场景优化，影响了时间效率。另一方面，基于重建的方法虽优先考虑效率，但由于其对不确定性的处理能力有限，往往牺牲了生成质量。我们提出了GECO，一种能在秒级时间内完成高质量三维生成建模的新方法。我们的方法通过两阶段策略解决了当前方法中普遍存在的不确定性和低效性问题。在第一阶段，我们利用分数蒸馏训练一个单步多视图生成模型。随后，应用第二阶段蒸馏以解决多视图预测中视角不一致的挑战。这种两阶段流程确保了三维生成在质量与效率上的平衡优化。我们的综合实验表明，GECO以前所未有的效率水平实现了高质量的图像到三维生成。