Articulated 3D Object Simulation Sequences Across 17 Categories

24 sequences simulate articulated object motion for 17 categories from PartNet-Mobility, including Camera, Chair, and Refrigerator. The dataset supports research on reconstructing articulated digital twins from monocular video, as presented in the associated paper by author ShawnRicardo.

Use Cases

• Train models to reconstruct articulated 3D geometry and motion from monocular video using the 17 object categories.
• Benchmark kinematic estimation algorithms on sequences featuring complex kinematics from categories like Door, Oven, and Eyeglasses.
• Develop category-specific motion priors for articulated objects such as Chair, Table, and Storage Furniture.

Strengths

• Covers 17 distinct articulated object categories from the PartNet-Mobility benchmark.
• Contains 24 sequences designed to feature complex kinematic motion.
• Created for a published research paper on articulated 3D reconstruction.

Limitations

• Dataset size, row count, and specific file formats are not provided in the input.
• Limited to simulated sequences, which may not capture the full complexity of real-world video data.
• The 17 categories represent a specific subset of objects, potentially limiting generalizability.

Provenance

Source

Official repository for the paper 'Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints'.

Collection Method

Simulated sequences based on 17 object categories from PartNet-Mobility.

Time Range

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Freshness

Last updated March 2026.

Geography

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