DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics

TL;DR

DailyArt introduces a novel approach to infer articulated joints from a single static image by synthesizing an open state to reveal articulation cues, enabling joint estimation without multi-view data or explicit priors.

Contribution

It formulates joint estimation as a synthesis-mediated reasoning task, allowing simultaneous recovery of all joints from a single image without object-specific templates or multi-view inputs.

Findings

Achieves strong performance in articulated joint estimation.

Supports part-level novel state synthesis conditioned on joints.

Operates without explicit part annotations at test time.

Abstract

Articulated objects are essential for embodied AI and world models, yet inferring their kinematics from a single closed-state image remains challenging because crucial motion cues are often occluded. Existing methods either require multi-state observations or rely on explicit part priors, retrieval, or other auxiliary inputs that partially expose the structure to be inferred. In this work, we present DailyArt, which formulates articulated joint estimation from a single static image as a synthesis-mediated reasoning problem. Instead of directly regressing joints from a heavily occluded observation, DailyArt first synthesizes a maximally articulated opened state under the same camera view to expose articulation cues, and then estimates the full set of joint parameters from the discrepancy between the observed and synthesized states. Using a set-prediction formulation, DailyArt recovers all joints simultaneously without requiring object-specific templates, multi-view inputs, or explicit part annotations at test time. Taking estimated joints as conditions, the framework further supports part-level novel state synthesis as a downstream capability. Extensive experiments show that DailyArt achieves strong performance in articulated joint estimation and supports part-level novel state synthesis conditioned on joints. Project page is available at https://rangooo123.github.io/DaliyArt.github.io/.