Functionalization via Structure Completion and Motion Rectification

arXiv:2605.18010 · cs.CV, cs.GR · Submitted 2026-05-18 · Read on arXiv

cs.CV, cs.GR

Submitted: 2026-05-18

Updated: 2026-09-22

Project page: https://mingrui-zhao.github.io/Functionalization

Terminology

• Transformer — 16× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Data Augmentation — 6× in this paper · explained in 7 episodes
• Decode — 5× in this paper · explained in 2 episodes
• Generalizability — 4× in this paper · explained in 6 episodes
• ACM Transactions on Graphics — 3× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Anchor Loss — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• In Eurographics State-of-the-art Report — 1× in this paper
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image
• ShapeNet: An Information-Rich 3D Model Repository
• Kinematic Kitbashing
• S2O: Static to Openable Enhancement for Articulated 3D Objects
• Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation
• Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model
• Particulate: Feed-Forward 3D Object Articulation
• SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects
• Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling
• Articulate That Object Part (ATOP): 3D Part Articulation via Text and Motion Personalization

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