Orient Anything V2: Unifying Orientation and Rotation Understanding
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.
69646388138cc47cbd765291
Zehan Wang
69646388138cc47cbd765292
Ziang Zhang
claimed_verified
2026-01-12T10:33:53.563Z
65b36a383a41095a56d0736d
https://cdn-avatars.huggingface.co/v1/production/uploads/65b36a383a41095a56d0736d/GSCizt3FTG0v_oqwbFoXZ.png
ZiangZhang
Viglong
user
Viglong
69646388138cc47cbd765293
Jiayang Xu
69646388138cc47cbd765294
Jialei Wang
69646388138cc47cbd765295
Tianyu Pang
69646388138cc47cbd765296
Chao Du
69646388138cc47cbd765297
HengShuang Zhao
69646388138cc47cbd765298
Zhou Zhao
Orient Anything V2: Unifying Orientation and Rotation Understanding
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.
Orient Anything V2 enhances 3D orientation understanding through scalable 3D asset synthesis, symmetry-aware periodic distribution fitting, and multi-frame relative rotation prediction, achieving state-of-the-art performance across multiple benchmarks.
generative models 3D assets model-in-the-loop annotation symmetry-aware periodic distribution fitting multi-frame architecture relative rotation prediction zero-shot performance 6DoF pose estimation object symmetry recognition