Geometry-Preserving Image Generation for 6D Object Pose Estimation

We propose an image generation pipeline that preserves geometric consistency for 6D object pose estimation. Most pose estimation methods rely on training with large-scale annotated real datasets, but collecting such data is often difficult and expensive in practice. To address this limitation, we introduce GenerationPose. First, we create a largescale synthetic dataset by rendering objects from multiple viewpoints using their 3D meshes. We then convert these synthetic images into pseudoreal images using a geometry-conditioned appearance generation model trained on large-scale synthetic–real image pairs. Our experiments show that the model generalizes well to unseen objects and can generate realistic images from synthetic views only. Furthermore, we show that pose estimation models trained on the generated data achieve significantly better performance compared to baseline methods. The code is available at https://github.com/JiafengZhang-1117/GenerationPose.