Research map / 3D vision / Human pose estimation
Orient Anything V2: Unifying Orientation and Rotation Understanding
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Orient Anything V2 scales orientation learning with 600K synthetic generated 3D assets, model-in-the-loop symmetry annotation, symmetry-aware periodic distribution fitting, and a multi-frame VGGT-initialized model that predicts absolute orientation and relative rotation.
Problem
Single-view object orientation estimation in V1 ignores rotational symmetry and cannot directly estimate relative rotation between views; real 3D asset datasets are imbalanced and low quality.
Approach
Generate 600K balanced 3D assets via Qwen/FLUX/Hunyuan3D pipeline, annotate 0 to N valid front faces by ensembling multi-view pseudo-labels and fitting periodic Gaussian distributions, train a VGGT-initialized multi-frame transformer whose camera-like tokens predict absolute orientation (symmetry-aware) and relative rotation.
Key results
- Ori_COCO orientation accuracy rises from 72.4 (V1) to 86.4 with V2; Objectron Med error drops from 30.67 to 22.62.
- On LINEMOD with POPE sampling, median rotation error drops to 7.82 vs 15.73 for POPE; with random pairs (78°), Acc30 on OnePose++ reaches 85.5.
- Symmetry recognition on Omni6DPose reaches 65.2% accuracy, above GPT-4o (62.5%) and other VLMs.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Upgrade Orient Anything V1 to V2 | Orient Anything · SUN-RGBD | median orientation error: 33.94 -> 26.00 | Helped |
| Upgrade Orient Anything V1 to V2 | Orient Anything · ARKitScenes | median orientation error: 77.58 -> 36.48 | Helped |
| Upgrade Orient Anything V1 to V2 | Orient Anything · Pascal3D+ | median orientation error: 22.90 -> 15.02 | Helped |
| Upgrade Orient Anything V1 to V2 | Orient Anything · Objectron | median orientation error: 30.67 -> 22.62 | Helped |
| Upgrade Orient Anything V1 to V2 | Orient Anything · ImageNet3D | median orientation error: 13.34 -> 15.26 | Hurt |
| Use Orient Anything V2 rather than POPE | zero-shot relative rotation estimation · LINEMOD, random sample | Acc30: 10.3 -> 51.6 | Helped |
| Use Orient Anything V2 rather than POPE | zero-shot relative rotation estimation · YCB-Video, random sample | Acc30: 40.9 -> 61.2 | Helped |
| Use Orient Anything V2 rather than POPE | zero-shot relative rotation estimation · OnePose++, random sample | Acc30: 25.6 -> 85.5 | Helped |
| Use Orient Anything V2 rather than POPE | zero-shot relative rotation estimation · OnePose, random sample | Acc30: 45.1 -> 86.7 | Helped |
| Replace 40K real assets with 40K synthetic assets (ablation) | Orient Anything V2 trained with VGGT initialization · Objectron | median orientation error: 25.05 -> 24.44 | Helped |
| Replace 40K real assets with 40K synthetic assets (ablation) | Orient Anything V2 trained with VGGT initialization · LINEMOD | median rotation error: 10.70 -> 10.16 | Helped |
| Scale synthetic training assets from 200K to 600K (ablation) | Orient Anything V2 with VGGT initialization · Ori_COCO | accuracy: 75.2 -> 86.4 | Helped |
Surprising findings
- Strong VLMs such as Gemini-2.5-pro and GPT-o3 perform near or below GPT-4o on horizontal symmetry recognition, indicating the task is hard even for frontier models.
- V2 is worse than V1 on ImageNet3D (Acc30 65.2 vs 71.3), though it is better on the other datasets.
Limitations
- Less accurate on views with very low information or severe occlusion due to monocular ambiguity.
- Supports at most two input frames, limiting video applications.
Experimental setup
Datasets: ImageNet3D ImageNet-21K Objaverse Omni6DPose SUN-RGBD ARKitScenes Pascal3D+ Objectron Ori_COCO LINEMOD YCB-Video OnePose
Benchmarks: ImageNet3D SUN-RGBD ARKitScenes Pascal3D+ Objectron Ori_COCO LINEMOD YCB-Video OnePose++ OnePose Omni6DPose
Metrics: Med angle error Acc30 Acc15 Acc (8-bin recognition)
Baselines: Orient Anything V1 Gen6D LoFTR POPE Qwen2.5VL-72B GPT-4o GPT-o3 Gemini-2.5-pro
Builds on: Orient Anything VGGT DINOv2 SynCLR
Closest papers by meaning
- Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models (ICML 2025 · similarity 0.93)
- Orientation Matters: Making 3D Generative Models Orientation-Aligned (NeurIPS 2025 · similarity 0.88)
- Symmetry-Robust 3D Orientation Estimation (ICML 2025 · similarity 0.88)
- Leveraging 3D Geometric Priors in 2D Rotation Symmetry Detection (CVPR 2025 · similarity 0.86)
- UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image (CVPR 2025 · similarity 0.85)
- 3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation (ICLR 2024 · similarity 0.85)
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