# One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation

arXiv: https://arxiv.org/abs/2509.07978 · HTML: https://mlsanity.com/paper/2509.07978/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2025.

## Built on by (most cited first, 14 of 14)

- [SAM 3D: 3Dfy Anything in Images](https://mlsanity.com/paper/2511.16624/) (2025)
- [CUPID: Generative 3D Reconstruction via Joint Object and Pose Modeling](https://mlsanity.com/paper/2510.20776/) (2025)
- [ByteLoom: Weaving Geometry-Consistent Human-Object Interactions through Progressive Curriculum Learning](https://mlsanity.com/paper/2512.22854/) (2025)
- [Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations](https://mlsanity.com/paper/2604.27106/) (2026)
- [One Video, One World: Turning Monocular Video into Physical 4D Scenes](https://mlsanity.com/paper/2606.31388/) (2026)
- [Fast-SAM3D: 3Dfy Anything in Images but Faster](https://mlsanity.com/paper/2602.05293/) (2026)
- [PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning](https://mlsanity.com/paper/2512.10840/) (2025)
- [UniPR: Unified Object-level Real-to-Sim Perception and Reconstruction from a Single Stereo Pair](https://mlsanity.com/paper/2603.19616/) (2026)
- [Object Pose Transformer: Unifying Unseen Object Pose Estimation](https://mlsanity.com/paper/2603.23370/) (2026)
- [Semantic Prior Guided One-View 6D Pose Estimation for Novel Objects](https://mlsanity.com/paper/2605.07023/) (2026)
- [Pose Anything Anywhere:Model-free Object Poses from Arbitrary References](https://mlsanity.com/paper/2606.23634/) (2026)
- [Emergence of a Shared Canonical Object Frame from In-the-Wild Videos](https://mlsanity.com/paper/2606.30058/) (2026)
- [LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation](https://mlsanity.com/paper/2609.35046/) (2026)
- [SkillWeaver: Agentic Exploration over Neural Interaction Skills for Scalable Robot Data Generation](https://mlsanity.com/paper/2609.36171/) (2026)

## Abstract (arXiv)

Estimating the 6D pose of arbitrary unseen objects from a single reference image is critical for robotics operating in the long-tail of real-world instances. However, this setting is notoriously challenging: 3D models are rarely available, single-view reconstructions lack metric scale, and domain gaps between generated models and real-world images undermine robustness. We propose OnePoseViaGen, a pipeline that tackles these challenges through two key components. First, a coarse-to-fine alignment module jointly refines scale and pose by combining multi-view feature matching with render-and-compare refinement. Second, a text-guided generative domain randomization strategy diversifies textures, enabling effective fine-tuning of pose estimators with synthetic data. Together, these steps allow high-fidelity single-view 3D generation to support reliable one-shot 6D pose estimation. On challenging benchmarks (YCBInEOAT, Toyota-Light, LM-O), OnePoseViaGen achieves state-of-the-art performance far surpassing prior approaches. We further demonstrate robust dexterous grasping with a real robot hand, validating the practicality of our method in real-world manipulation. Project page: https://gzwsama.github.io/OnePoseviaGen.github.io/