The demand for efficient 3D model generation techniques has grown exponentially, as manual creation of 3D models is time-consuming and requires specialized expertise. While generative models have shown potential in creating 3D textured shapes from 2D images, their applicability in 3D industries is limited due to the lack of a well-defined camera distribution in real-world scenarios, resulting in low-quality shapes. To overcome this limitation, we propose GET3D--, the first method that directly generates textured 3D shapes from 2D images with unknown pose and scale. GET3D-- comprises a 3D shape generator and a learnable camera sampler that captures the 6D external changes on the camera. In addition, We propose a novel training schedule to stably optimize both the shape generator and camera sampler in a unified framework. By controlling external variations using the learnable camera sampler, our method can generate aligned shapes with clear textures. Extensive experiments demonstrate the efficacy of GET3D--, which precisely fits the 6D camera pose distribution and generates high-quality shapes on both synthetic and realistic unconstrained datasets.

翻译：随着手动创建3D模型耗时且需专业技能的现状，高效3D模型生成技术的需求呈指数级增长。尽管生成模型已展现出从2D图像创建带纹理3D形状的潜力，但由于现实场景中缺乏明确定义的相机分布导致生成形状质量低下，其在3D行业的应用仍受限。为突破这一局限，我们提出GET3D--——首个能从姿态与尺度未知的2D图像直接生成带纹理3D形状的方法。GET3D--包含3D形状生成器与可学习相机采样器，可捕获相机的6D外部变化。此外，我们提出新型训练策略，在统一框架中稳定优化形状生成器与相机采样器。通过使用可学习相机采样器控制外部变量，该方法能生成对齐且纹理清晰的形状。大量实验证明，GET3D--能精确拟合6D相机姿态分布，在合成与真实无约束数据集上均生成高质量形状，验证了其有效性。