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The demand for efficient 3D model generation techniques has grownexponentially, as manual creation of 3D models is time-consuming and requiresspecialized expertise. While generative models have shown potential in creating3D textured shapes from 2D images, their applicability in 3D industries islimited due to the lack of a well-defined camera distribution in real-worldscenarios, resulting in low-quality shapes. To overcome this limitation, wepropose GET3D--, the first method that directly generates textured 3D shapesfrom 2D images with unknown pose and scale. GET3D-- comprises a 3D shapegenerator and a learnable camera sampler that captures the 6D external changeson the camera. In addition, We propose a novel training schedule to stablyoptimize both the shape generator and camera sampler in a unified framework. Bycontrolling external variations using the learnable camera sampler, our methodcan generate aligned shapes with clear textures. Extensive experimentsdemonstrate the efficacy of GET3D--, which precisely fits the 6D camera posedistribution and generates high-quality shapes on both synthetic and realisticunconstrained datasets.

GET3D--: Learning GET3D from Unconstrained Image Collections