Orient Anything V2: Unifying Orientation and Rotation Understanding

TL;DR

Orient Anything V2 is a comprehensive foundation model that unifies object orientation and rotation understanding, leveraging innovative data synthesis, symmetry-aware modeling, and multi-frame architecture to achieve state-of-the-art zero-shot performance.

Contribution

It introduces four key innovations including scalable synthetic data, a robust annotation system, symmetry-aware modeling, and multi-frame architecture for improved orientation understanding.

Findings

Achieves state-of-the-art zero-shot performance on orientation and pose estimation.

Demonstrates strong generalization across 11 benchmarks.

Effectively models object rotational symmetry.

Abstract

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.