Orientation Matters: Making 3D Generative Models Orientation-Aligned
This addresses a usability issue for downstream tasks in 3D vision and graphics, but it is incremental as it builds on existing generative models with a new dataset and fine-tuning approach.
The paper tackles the problem of inconsistent orientation in 3D generative models by introducing orientation-aligned 3D object generation from single images, resulting in improved alignment and generalization across categories, with experiments showing superiority over post-hoc methods.
Humans intuitively perceive object shape and orientation from a single image, guided by strong priors about canonical poses. However, existing 3D generative models often produce misaligned results due to inconsistent training data, limiting their usability in downstream tasks. To address this gap, we introduce the task of orientation-aligned 3D object generation: producing 3D objects from single images with consistent orientations across categories. To facilitate this, we construct Objaverse-OA, a dataset of 14,832 orientation-aligned 3D models spanning 1,008 categories. Leveraging Objaverse-OA, we fine-tune two representative 3D generative models based on multi-view diffusion and 3D variational autoencoder frameworks to produce aligned objects that generalize well to unseen objects across various categories. Experimental results demonstrate the superiority of our method over post-hoc alignment approaches. Furthermore, we showcase downstream applications enabled by our aligned object generation, including zero-shot object orientation estimation via analysis-by-synthesis and efficient arrow-based object rotation manipulation.