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About this paper
Jiayi Liu, Manolis Savva, Ali Mahdavi-Amiri
This State of The Art Report provides a comprehensive review of advancements in 3D modeling of human-made articulated objects, distinctly analyzing geometry and articulation modeling while outlining current challenges and future research directions for computer vision, graphics, and robotics.
Key insights
- The field has significantly transitioned towards data-driven learning-based approaches, increasingly leveraging self-supervised and generative models, often with transfer learning from foundation models.
- Implicit neural representations, such as Neural SDFs and NeRFs, are becoming central intermediate representations for articulated object creation due to their continuous and differentiable nature.
- While progress is evident, critical challenges persist in achieving generalization across diverse objects, ensuring robustness in real-world conditions, and incorporating physical plausibility into models.
Summary by alphaXiv.
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