Summary of Artformer: Controllable Generation Of Diverse 3d Articulated Objects, by Jiayi Su et al.
ArtFormer: Controllable Generation of Diverse 3D Articulated Objects
by Jiayi Su, Youhe Feng, Zheng Li, Jinhua Song, Yangfan He, Botao Ren, Botian Xu
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework for modeling and generating 3D articulated objects, addressing limitations in existing methods that struggle to balance flexibility and quality. The framework parameterizes the object as a tree of tokens and employs a transformer to generate both high-level geometry code and kinematic relations. This is followed by decoding each sub-part’s geometry using a signed-distance-function (SDF) shape prior, enabling the synthesis of high-quality 3D shapes with varying number of parts. The method demonstrates effectiveness in conditional generation from text descriptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to create realistic 3D objects with many moving parts. Right now, making these objects is tricky because you have to choose between having a lot of flexibility or having good quality. To solve this problem, the researchers created a special code that can generate both the overall shape of an object and how its different parts move. Then, they used another code to make each part’s shape more detailed and realistic. This new method can create many different objects with lots of moving parts and makes them look very good. |
Keywords
» Artificial intelligence » Transformer
