Efficient Autoregressive Shape Generation via Octree-Based Adaptive Tokenization Article Swipe
Kangle Deng
,
Hsueh‐Ti Derek Liu
,
Yong‐Guan Zhu
,
Xiaoxia Sun
,
Chong Shang
,
K. Sham Bhat
,
Deva Ramanan
,
Jun-Yan Zhu
,
Maneesh Agrawala
,
Tinghui Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2504.02817
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2504.02817
Many 3D generative models rely on variational autoencoders (VAEs) to learn compact shape representations. However, existing methods encode all shapes into a fixed-size token, disregarding the inherent variations in scale and complexity across 3D data. This leads to inefficient latent representations that can compromise downstream generation. We address this challenge by introducing Octree-based Adaptive Tokenization, a novel framework that adjusts the dimension of latent representations according to shape complexity. Our approach constructs an adaptive octree structure guided by a quadric-error-based subdivision criterion and allocates a shape latent vector to each octree cell using a query-based transformer. Building upon this tokenization, we develop an octree-based autoregressive generative model that effectively leverages these variable-sized representations in shape generation. Extensive experiments demonstrate that our approach reduces token counts by 50% compared to fixed-size methods while maintaining comparable visual quality. When using a similar token length, our method produces significantly higher-quality shapes. When incorporated with our downstream generative model, our method creates more detailed and diverse 3D content than existing approaches.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2504.02817
- https://arxiv.org/pdf/2504.02817
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410348934
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410348934Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2504.02817Digital Object Identifier
- Title
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Efficient Autoregressive Shape Generation via Octree-Based Adaptive TokenizationWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-03Full publication date if available
- Authors
-
Kangle Deng, Hsueh‐Ti Derek Liu, Yong‐Guan Zhu, Xiaoxia Sun, Chong Shang, K. Sham Bhat, Deva Ramanan, Jun-Yan Zhu, Maneesh Agrawala, Tinghui ZhouList of authors in order
- Landing page
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https://arxiv.org/abs/2504.02817Publisher landing page
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https://arxiv.org/pdf/2504.02817Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2504.02817Direct OA link when available
- Concepts
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Octree, Autoregressive model, Lexical analysis, Computer science, STAR model, Speech recognition, Artificial intelligence, Econometrics, Mathematics, Autoregressive integrated moving average, Time series, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.allocates | 83 |
| abstract_inverted_index.challenge | 49 |
| abstract_inverted_index.criterion | 81 |
| abstract_inverted_index.dimension | 61 |
| abstract_inverted_index.framework | 57 |
| abstract_inverted_index.leverages | 109 |
| abstract_inverted_index.structure | 75 |
| abstract_inverted_index.comparable | 133 |
| abstract_inverted_index.complexity | 31 |
| abstract_inverted_index.compromise | 43 |
| abstract_inverted_index.constructs | 71 |
| abstract_inverted_index.downstream | 44, 152 |
| abstract_inverted_index.fixed-size | 22, 129 |
| abstract_inverted_index.generative | 2, 105, 153 |
| abstract_inverted_index.variations | 27 |
| abstract_inverted_index.approaches. | 166 |
| abstract_inverted_index.complexity. | 68 |
| abstract_inverted_index.demonstrate | 118 |
| abstract_inverted_index.effectively | 108 |
| abstract_inverted_index.experiments | 117 |
| abstract_inverted_index.generation. | 45, 115 |
| abstract_inverted_index.inefficient | 38 |
| abstract_inverted_index.introducing | 51 |
| abstract_inverted_index.maintaining | 132 |
| abstract_inverted_index.query-based | 94 |
| abstract_inverted_index.subdivision | 80 |
| abstract_inverted_index.variational | 6 |
| abstract_inverted_index.Octree-based | 52 |
| abstract_inverted_index.autoencoders | 7 |
| abstract_inverted_index.disregarding | 24 |
| abstract_inverted_index.incorporated | 149 |
| abstract_inverted_index.octree-based | 103 |
| abstract_inverted_index.transformer. | 95 |
| abstract_inverted_index.Tokenization, | 54 |
| abstract_inverted_index.significantly | 145 |
| abstract_inverted_index.tokenization, | 99 |
| abstract_inverted_index.autoregressive | 104 |
| abstract_inverted_index.higher-quality | 146 |
| abstract_inverted_index.variable-sized | 111 |
| abstract_inverted_index.representations | 40, 64, 112 |
| abstract_inverted_index.representations. | 13 |
| abstract_inverted_index.quadric-error-based | 79 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |