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REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment

2024-05-28 18:45:10
Haonan Han, Rui Yang, Huan Liao, Jiankai Xing, Zunnan Xu, Xiaoming Yu, Junwei Zha, Xiu Li, Wanhua Li

Abstract

Traditional image-to-3D models often struggle with scenes containing multiple objects due to biases and occlusion complexities. To address this challenge, we present REPARO, a novel approach for compositional 3D asset generation from single images. REPARO employs a two-step process: first, it extracts individual objects from the scene and reconstructs their 3D meshes using off-the-shelf image-to-3D models; then, it optimizes the layout of these meshes through differentiable rendering techniques, ensuring coherent scene composition. By integrating optimal transport-based long-range appearance loss term and high-level semantic loss term in the differentiable rendering, REPARO can effectively recover the layout of 3D assets. The proposed method can significantly enhance object independence, detail accuracy, and overall scene coherence. Extensive evaluation of multi-object scenes demonstrates that our REPARO offers a comprehensive approach to address the complexities of multi-object 3D scene generation from single images.

Abstract (translated)

传统图像到3D模型在处理包含多个对象的场景时常常受到偏见和遮挡复杂性的影响。为解决这个问题,我们提出了REPARO,一种从单张图像中生成合成3D资产的新方法。REPARO采用两个步骤:首先,它从场景中提取单个对象,然后使用标准的图像到3D模型重构它们的3D网格;然后,它通过不同的渲染技术优化这些网格的布局,确保连贯的场景构图。通过在不同的渲染中集成最优的传输基于长距离外观损失项和高层次语义损失项,REPARO可以有效地恢复3D资产的布局。对多对象场景的广泛评估表明,我们的REPARO提供了解决单张图像中多对象3D场景生成的复杂性的全面方法。

URL

https://arxiv.org/abs/2405.18525

PDF

https://arxiv.org/pdf/2405.18525.pdf


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