ZipTok3D: High-Fidelity 3D Tokenization with Compact Token Prefixes
ZipTok3D is a 3D tokenizer designed for high-fidelity reconstruction from extremely short token sequences. It organizes object geometry into progressively informative global-token prefixes, using nested dropout to prioritize essential information in leading tokens, then iteratively decodes each prefix with a parameter-shared Transformer block—no separate generative sampling stage is needed. With the same token dimension, it matches the reconstruction quality of the 32-token COD-VAE baseline using just one token on ShapeNet and four on TRELLIS, achieving 32x and 8x shorter token sequences, respectively.
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[Submitted on 1 Sep 2026]
Title:ZipTok3D: High-Fidelity 3D Tokenization with Compact Token Prefixes
View a PDF of the paper titled ZipTok3D: High-Fidelity 3D Tokenization with Compact Token Prefixes, by Mingda Lin and 9 other authors
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Abstract:Compact token sequences are essential for efficient 3D generation. However, existing 3D tokenizers typically organize latent representations either over spatial regions or as fixed-size sets of global tokens, both suffering sharp reconstruction degradation when compressed to extremely low token budgets. In this paper, we present ZipTok3D, a 3D tokenizer designed for high-fidelity reconstruction from extremely short token sequences. Its key idea is to organize object geometry into progressively informative global-token prefixes and unfold these compact representations through iterative decoding. Specifically, nested dropout randomly truncates the latent sequence after encoding during training and requires each retained prefix to reconstruct the complete object, thereby prioritizing essential geometric information in the leading tokens. The decoder then repeatedly applies a parameter-shared Transformer block to recover fine-grained geometry from each prefix without a separate generative sampling stage. With the same token dimension, ZipTok3D achieves reconstruction quality comparable to the 32-token COD-VAE baseline using only one token on ShapeNet and four on TRELLIS, yielding $32\times$ and $8\times$ shorter token sequences, respectively.
Comments: 25 pages, 9 figures, 6 tables, including appendix
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.01740 [cs.CV]
(or arXiv:2609.01740v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.01740
arXiv-issued DOI via DataCite
Submission history
From: Mingda Lin [view email] [v1] Tue, 1 Sep 2026 18:07:49 UTC (10,425 KB)
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