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Orient Anything V2: Unifying Orientation and Rotation Understanding
Zehan Wang, Ziang Zhang, Jiayang Xu, Jialei Wang, Tianyu Pang, Chao Du, HengShuang Zhao, Zhou Zhao
TL;DR
Orient Anything V2 addresses the limits of single-front-face orientation models, especially for rotationally symmetric objects and relative rotation. It combines scalable annotated 3D data, symmetry-aware distribution learning, and multi-frame prediction, achieving strong zero-shot performance across orientation, rotation, and symmetry tasks. The framework broadens orientation understanding to diverse objects while remaining limited by low-information or occluded views and a maximum of two input frames.
Problem
Orient Anything V1 models a unique front face, limiting its handling of rotational symmetries and relative rotations between views.
Method
Orient Anything V2 combines a scalable data engine, symmetry-aware distribution fitting, and multi-frame architecture for direct relative rotation prediction.
Results
The model demonstrates strong zero-shot generalization across absolute orientation estimation, relative rotation estimation, and object symmetry recognition, with superior orientation and record-setting rotation performance.
Takeaways & Limitations
Orient Anything V2 simultaneously predicts multiple valid front faces and supports orientation, relative pose rotation, and horizontal rotational symmetry recognition for diverse objects.
Takeaways & Limitations
Monocular views with very low information or severe occlusion reduce accuracy, and the current framework supports at most two input frames.
Abstract
from arXiv · showhide
This work presents Orient Anything V2, an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Building upon Orient Anything V1, which defines orientation via a single unique front face, V2 extends this capability to handle objects with diverse rotational symmetries and directly estimate relative rotations. These improvements are enabled by four key innovations: 1) Scalable 3D assets synthesized by generative models, ensuring broad category coverage and balanced data distribution; 2) An efficient, model-in-the-loop annotation system that robustly identifies 0 to N valid front faces for each object; 3) A symmetry-aware, periodic distribution fitting objective that captures all plausible front-facing orientations, effectively modeling object rotational symmetry; 4) A multi-frame architecture that directly predicts relative object rotations. Extensive experiments show that Orient Anything V2 achieves state-of-the-art zero-shot performance on orientation estimation, 6DoF pose estimation, and object symmetry recognition across 11 widely used benchmarks. The model demonstrates strong generalization, significantly broadening the applicability of orientation estimation in diverse downstream tasks.
1 Introduction
Orient Anything V2 extends orientation understanding beyond unique front faces to rotational symmetry and relative rotation. It combines scalable data, symmetry-aware learning, and multi-frame prediction, achieving strong zero-shot results across orientation, rotation, and symmetry tasks.
- Object orientation supports robot manipulation, autonomous driving, AR/VR, and spatial-aware image understanding and generation.
- Orient Anything V1 struggles with rotationally symmetric objects and relative rotation because it models only a unique front face.
- Orient Anything V2 introduces a scalable data engine and model framework to improve generalization and jointly understand object orientation and rotation.
- The data engine uses generative 3D models, calibrated multi-view pseudo-labels, and 600K assets with robust annotations for 0 to N valid front faces.The resulting dataset is described as 12× larger than the existing orientation dataset.
- The model shows strong zero-shot generalization across absolute orientation, relative rotation, and object symmetry recognition, including superior orientation and record-setting rotation results.
- Symmetry-aware distribution fitting predicts all plausible object orientations, while multi-frame input directly estimates relative rotations between views.
2 Related Work
Prior work addresses rotational symmetry, relative rotation, and single-view orientation, but remains limited by training coverage, pixel matching, or narrow domains. Orient Anything V2 responds with implicit multi-frame learning and upgrades both data and model design.
- Rotational symmetry preserves an object’s shape after certain rotations and matters for recognition, generation, pose estimation, and robotic manipulation.Existing single-view symmetry methods are constrained by limited training data and lack zero-shot open-world generalization.
- Objects may have multiple valid front faces determined by vertical-axis symmetry, including two faces under 180-degree symmetry and no meaningful direction under continuous symmetry.
- Relative rotation estimation is important for 6DoF pose estimation and robotics, while correspondence-based methods can fail under large viewpoint changes.
- Orient Anything V2 uses a purely implicit multi-frame approach to predict zero-shot relative rotations between arbitrary views.
- The work upgrades Orient Anything through a scalable data engine and direct symmetry and rotation prediction.
3 Revisiting Orient Anything V1
Orient Anything V1 provides robust single-image orientation estimation but is limited by its training data and its treatment of rotational symmetry and relative rotation. These limitations motivate the need for a broader data source and improved rotation understanding.
- V1 capabilities: Orient Anything V1 estimates object orientation from single images using a VLM-annotated canonical front view and distribution fitting.It also provides a confidence score for whether an object has a unique front face.
- Data limitations: Real 3D asset datasets suffer from imbalanced category distributions, inconsistent quality, incomplete geometry, and fixed poses.These issues include severe underrepresentation of uncommon animals and a substantial domain gap from real-world object variations.
- Rotation limitations: V1 overlooks multiple valid front faces caused by rotational symmetry, limiting orientation understanding for symmetric objects.Its single-front-face definition cannot effectively distinguish or identify potential orientations for many real-world objects.
- Rotation limitations: V1 often fails at relative rotation estimation because independently predicted absolute orientations accumulate significant error.The paper describes relative rotation and front-facing orientation as inherently coupled.
- Motivation: These limitations motivate upgrading Orient Anything through improved training data and direct modeling of symmetry and rotation.The proposed direction is to address both data-driven and model-driven shortcomings.
4 Scalable Data Engine
The scalable data engine synthesizes broad, balanced 3D assets and combines model-generated multi-view labels with consistency calibration. This pipeline produces more reliable orientation and symmetry annotations while supporting the V2 framework.
- 3D asset synthesis: The synthesis pipeline converts class tags into captions, images, and 3D meshes using generative models.Captions describe object attributes and poses, generated images promote explicit 3D structure, and image-to-3D models produce meshes.
- 3D asset synthesis: 600k synthetic 3D assets provide approximately 30 items per ImageNet-21K class tag, with complete geometry, detailed textures, and balanced coverage.The dataset is 12× larger than the filtered real dataset used in Orient Anything V1.
- Robust annotation: The annotation system uses an improved V1-based model to generate multi-view pseudo-labels, projects them into canonical 3D coordinates, and fits their azimuth distribution.This ensemble process captures object orientations and rotational symmetries from multiple renderings.
- Robust annotation: Periodic fitting represents a main azimuth direction, rotational periodicity, and the corresponding number of valid front faces, while zero periodicity indicates no dominant orientation.The fitted periodicity α represents 360/α-degree symmetry and α valid front faces.
- Calibration: Ensembling pseudo-labels suppresses single-view outliers, while cross-asset consistency calibration checks symmetry agreement within categories.Only around 15% of 21k source category tags show minor inconsistencies involving a small number of assets.
- V2 framework: The Orient Anything V2 framework uses one or two input frames that are tokenized, jointly encoded, and decoded into orientation or rotation distributions.The figure describes DINOv2 tokenization, transformer encoding, and MLP prediction heads.
5 Framework
Orient Anything V2 replaces V1’s unimodal orientation targets with symmetry-aware periodic distributions and extends the architecture to multi-frame relative rotation prediction. The framework shares information between absolute orientation and relative rotation.
- Symmetry-aware distribution: V1 models azimuth, polar, and in-plane rotation with circular Gaussian distributions centered on one unique front-facing orientation.Symmetric or front-less objects receive low orientation confidence in V1.
- Symmetry-aware distribution: V2 models 0 to N valid front faces directly in a symmetry-aware periodic azimuth distribution.This design replaces V1’s extra confidence mechanism for handling multiple or absent front faces.
- Symmetry-aware distribution: The periodic target uses fitted phase and periodicity for azimuth, while polar and in-plane rotation distributions are constructed similarly without periodicity.The azimuth target contains 360 angle bins, and σ is the variance hyper-parameter.
- Inference: At inference, least-squares fitting of predicted distributions yields azimuth periodicity, azimuth, polar, and rotation parameters that indicate valid front faces and directions.The number of valid front faces is represented by the fitted azimuth periodicity.
- Multi-frame rotation: The multi-frame architecture jointly encodes one or more images and predicts absolute orientation from the first frame and relative rotations from subsequent frames.The first frame uses the symmetry-aware distribution, while later frames use a related fitting task without symmetry periodicity.
- Training: The model is initialized from VGGT and trained to fit target orientation or rotation distributions with Binary Cross-Entropy loss.VGGT contributes a 1.2-billion-parameter transformer pretrained on 3D geometry tasks.
6 Experiment
Experiments evaluate Orient Anything V2 on absolute orientation, relative rotation, and rotational symmetry, alongside ablations of synthetic data, scaling, and initialization. The model improves single-view orientation, performs strongly on two-view rotation, and recognizes horizontal symmetry.
- 6.1 Zero-shot Orientation Estimation: 86.4% accuracy on Ori_COCO demonstrates improved zero-shot single-view orientation estimation over Orient Anything V1.The method also performs well on difficult categories such as bicycles and achieves state-of-the-art results on multiple real-world image datasets.
- 6.3 Zero-shot Symmetry Recognition: 65% accuracy in distinguishing object horizontal rotational symmetry shows that symmetry recognition remains challenging for general VLMs.Combined with absolute orientation estimation, the model can infer multiple potential orientations from a single image.
- 6.4 Ablation Study: Synthetic assets provide comparable absolute-orientation results to annotated real assets but a significant advantage for rotation estimation.The authors associate this advantage with richer, more realistic textures in synthetic assets.
- 6.4 Ablation Study: More diverse training data improves overall performance, with rotation estimation more sensitive to data scale than orientation estimation.The ablations use the same training step while varying the amount and diversity of data and 3D assets.
- 6.4 Ablation Study: DINOv2 initialization improves semantic and object-structure information, while VGGT further improves rotation estimation through 3D geometric pre-training.Training without pre-trained initialization produces the worst results.
7 Conclusion
Orient Anything V2 unifies object orientation and rotation understanding through scalable data, symmetry-aware learning, and multi-frame prediction. It supports absolute orientation, relative pose rotation, and horizontal symmetry recognition, while remaining less accurate under low-information or severely occluded views and limited to two input frames.
- Conclusion: The model combines a scalable data engine, symmetry-aware distribution learning target, and multi-frame framework for unified orientation and rotation understanding.These components support single-view orientation, two-frame relative pose rotation, and horizontal rotational symmetry recognition.
- Conclusion: Orient Anything V2 can predict multiple valid front faces, making it suitable for diverse objects and real-world application scenarios.
- Limitations: Predictions are less accurate in views with very low information or severe occlusion, and the current framework supports at most two input frames.Extending the model to more frames is identified as important for video understanding applications.
A More Visualizations of Images in The Wild
Visualizations cover in-the-wild examples of absolute orientation, horizontal rotational symmetry, and two-frame relative pose rotation. They include objects with no, one, two, and four valid front directions.
- In-the-Wild Visualizations: The visualizations demonstrate single-view absolute orientation estimation, horizontal rotational symmetry recognition, and two-frame relative pose rotation across varied domains.
- Relative Rotation: The figures include separate examples of relative pose rotation estimation for images in the wild.
- Rotational Symmetry: Examples are organized by objects with no front direction and with one, two, or four front directions.