GECO: Generative Image-to-3D within a SECOnd
<|reference_start|>GECO: Generative Image-to-3D within a SECOnd: Recent years have seen significant advancements in 3D generation. While methods like score distillation achieve impressive results, they often require extensive per-scene optimization, which limits their time efficiency. On the other hand, reconstruction-based approaches are more efficient but tend to compromise quality due to their limited ability to handle 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 existing methods through a two-stage approach. In the first 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 in the multi-view generation. 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 mesh generation with an unprecedented level of efficiency. We will make the code and model publicly available.<|reference_end|>
@article{wang2024geco:,
    title={GECO: Generative Image-to-3D within a SECOnd},
    author={Chen Wang, Jiatao Gu, Xiaoxiao Long, Yuan Liu, Lingjie Liu},
    journal={arXiv preprint arXiv:2405.20327},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20327},
    primaryClass={cs.CV}
}