Atlas3D：物理约束的自支撑文本到3D转换，用于模拟和制造。

Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication

May 28, 2024

作者: Yunuo Chen, Tianyi Xie, Zeshun Zong, Xuan Li, Feng Gao, Yin Yang, Ying Nian Wu, Chenfanfu Jiang

cs.AI

摘要

现有基于扩散的文本到3D生成方法主要关注产生视觉逼真的形状和外观，通常忽略了下游任务所需的物理约束。生成的模型在放置在基于物理的模拟或3D打印中时经常无法保持平衡。这种平衡对于满足用户设计意图在互动游戏、具身人工智能和机器人技术中的重要性不言而喻，稳定的模型对于可靠的交互至关重要。此外，稳定的模型确保3D打印的物体，如家居装饰用的小雕像，可以独立站立而无需额外支撑。为填补这一空白，我们引入Atlas3D，这是一种自动且易于实施的方法，可增强现有基于得分蒸馏采样（SDS）的文本到3D工具。Atlas3D确保生成符合重力、接触和摩擦物理稳定性定律的自支撑3D模型。我们的方法结合了一种新颖的可微分基于模拟的损失函数和受物理启发的正则化，可作为现有框架的细化或后处理模块。我们通过大量生成任务验证了Atlas3D的有效性，并在模拟和真实环境中验证了生成的3D模型。

English

Existing diffusion-based text-to-3D generation methods primarily focus on producing visually realistic shapes and appearances, often neglecting the physical constraints necessary for downstream tasks. Generated models frequently fail to maintain balance when placed in physics-based simulations or 3D printed. This balance is crucial for satisfying user design intentions in interactive gaming, embodied AI, and robotics, where stable models are needed for reliable interaction. Additionally, stable models ensure that 3D-printed objects, such as figurines for home decoration, can stand on their own without requiring additional supports. To fill this gap, we introduce Atlas3D, an automatic and easy-to-implement method that enhances existing Score Distillation Sampling (SDS)-based text-to-3D tools. Atlas3D ensures the generation of self-supporting 3D models that adhere to physical laws of stability under gravity, contact, and friction. Our approach combines a novel differentiable simulation-based loss function with physically inspired regularization, serving as either a refinement or a post-processing module for existing frameworks. We verify Atlas3D's efficacy through extensive generation tasks and validate the resulting 3D models in both simulated and real-world environments.