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Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
AI for ScienceUnified Cross-Scale 3D ModelingUnified 3D Generation and Understanding
Abstract:

3D structure modeling is essential across scales, enabling applications from fluid simulation and 3D reconstruction to protein folding and molecular docking. Yet, despite shared 3D spatial patterns, current approaches remain fragmented, with models narrowly specialized for specific domains and unable to generalize across tasks or scales. We propose Uni-3DAR, a unified autoregressive framework for cross-scale 3D generation and understanding. At its core is a coarse-to-fine tokenizer based on octree data structures, which compresses diverse 3D structures into compact 1D token sequences. We further propose a two-level subtree compression strategy, which reduces the octree token sequence by up to 8x. To address the challenge of dynamically varying token positions introduced by compression, we introduce a masked next-token prediction strategy that ensures accurate positional modeling, significantly boosting model performance. Extensive experiments across multiple 3D generation and understanding tasks, including small molecules, proteins, polymers, crystals, and macroscopic 3D objects, validate its effectiveness and versatility. Notably, Uni-3DAR surpasses previous state-of-the-art diffusion models by a substantial margin, achieving up to 256% relative improvement while delivering inference speeds up to 21.8x faster.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

Uni-3DAR proposes a unified autoregressive framework for cross-scale 3D generation and understanding, employing an octree-based tokenizer with two-level subtree compression and masked next-token prediction. The paper resides in the 'Multi-Scale Tokenization and Autoregressive Modeling' leaf, which contains five papers total, including SAR3D, Xcube, and two others. This leaf represents a moderately active research direction within the broader autoregressive and hierarchical generation branch, focusing specifically on tokenization strategies and sequential prediction for 3D synthesis across varying resolutions.

The taxonomy reveals that Uni-3DAR's leaf sits within a larger autoregressive and hierarchical generation subtopic, which also includes neighboring leaves on hierarchical voxel/octree-based generation and hierarchical latent space methods. Adjacent branches address part-aware compositional generation, large-scale multi-modal synthesis, and reconstruction/understanding tasks. The scope note for this leaf emphasizes multi-scale vector quantization and hierarchical latent codes, while excluding diffusion-based or GAN-based methods without autoregressive components. Uni-3DAR's cross-domain ambitions (molecules to macroscopic objects) distinguish it from sibling papers that may target narrower application scopes or single-scale regimes.

Among thirty candidates examined, the contribution-level analysis found limited prior work overlap. The unified autoregressive framework (Contribution A) examined ten candidates with zero refutable matches, suggesting relative novelty in bridging multiple domains under one model. The octree tokenizer with two-level compression (Contribution B) examined ten candidates and identified one refutable match, indicating some precedent for hierarchical octree tokenization but potentially novel compression strategies. The masked next-token prediction for dynamic positions (Contribution C) also examined ten candidates with zero refutations, hinting at a less-explored technique within this limited search scope.

Based on the top-thirty semantic matches and citation expansion, Uni-3DAR appears to occupy a moderately novel position, particularly in its cross-domain unification and compression strategy. However, the analysis does not cover exhaustive literature beyond these candidates, and the single refutable match for the tokenizer suggests some overlap with existing octree-based methods. The framework's claimed versatility across molecular and macroscopic scales remains a distinguishing feature within the examined scope, though broader validation would require deeper exploration of domain-specific prior work.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: cross-scale 3D structure generation and understanding. This field addresses the challenge of representing and synthesizing three-dimensional data across multiple levels of detail, from fine geometric features to large-scale scene layouts. The taxonomy reveals a rich landscape organized around several complementary perspectives. Autoregressive and hierarchical generation methods leverage multi-scale tokenization and sequential modeling to build structures progressively, often using octree-based representations (e.g., Octree Transformer[43], OctGPT[47]) or hierarchical fusion strategies (HierOctFusion[6]). Part-aware and compositional approaches emphasize decomposing objects into meaningful components (PartCrafter[8], Omnipart[9]), while large-scale and multi-modal branches integrate diverse data sources—such as text, images, and sensor inputs—to generate expansive environments (DriveDreamer[5], CLAY[4]). Reconstruction and understanding methods focus on inferring 3D structure from observations, point cloud processing tackles efficient multi-scale feature learning, and mesh processing addresses denoising across resolutions. Application-specific branches span physical material systems, biological imaging, and domain-tailored workflows, reflecting the breadth of cross-scale challenges. Within this ecosystem, a particularly active line of work centers on autoregressive and hierarchical tokenization strategies that encode 3D data at multiple resolutions for efficient generation. Unified CrossScale 3D[0] sits squarely in this branch, emphasizing multi-scale tokenization and autoregressive modeling to handle varying levels of geometric detail. It shares conceptual ground with SAR3D[1], which also adopts autoregressive frameworks for 3D synthesis, and with Xcube[3], another recent effort exploring hierarchical representations. Compared to these neighbors, Unified CrossScale 3D[0] appears to pursue a more integrated treatment of scale transitions within a single generative pipeline, whereas works like 3D-WAG[18] and LION[19] may prioritize different trade-offs between expressiveness and computational efficiency. Across the field, open questions persist around balancing fine-grained fidelity with scalability, integrating part-level semantics into hierarchical models, and bridging the gap between purely geometric methods and multi-modal, application-driven systems.

Claimed Contributions

Unified autoregressive framework for cross-scale 3D generation and understanding

The authors introduce Uni-3DAR, a single autoregressive model that handles both 3D generation and understanding tasks across multiple scales, from microscopic structures like molecules and proteins to macroscopic 3D objects. This framework unifies previously fragmented domain-specific approaches into one architecture.

10 retrieved papers
Coarse-to-fine octree-based tokenizer with two-level subtree compression

The authors develop a hierarchical tokenization method using octree data structures to efficiently compress sparse 3D structures into 1D sequences. They introduce a two-level subtree compression that merges parent-child nodes into single tokens, achieving up to 8x reduction in sequence length while maintaining lossless representation.

10 retrieved papers
Can Refute
Masked next-token prediction strategy for dynamic token positions

The authors propose a novel training strategy that duplicates tokens with masked placeholders to handle the challenge of unpredictable token positions in sparse 3D structures. This method enables the model to predict token content while being conditioned on correct positional information, maintaining causal attention flow without complex sampling schemes.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unified autoregressive framework for cross-scale 3D generation and understanding

The authors introduce Uni-3DAR, a single autoregressive model that handles both 3D generation and understanding tasks across multiple scales, from microscopic structures like molecules and proteins to macroscopic 3D objects. This framework unifies previously fragmented domain-specific approaches into one architecture.

Contribution

Coarse-to-fine octree-based tokenizer with two-level subtree compression

The authors develop a hierarchical tokenization method using octree data structures to efficiently compress sparse 3D structures into 1D sequences. They introduce a two-level subtree compression that merges parent-child nodes into single tokens, achieving up to 8x reduction in sequence length while maintaining lossless representation.

Contribution

Masked next-token prediction strategy for dynamic token positions

The authors propose a novel training strategy that duplicates tokens with masked placeholders to handle the challenge of unpredictable token positions in sparse 3D structures. This method enables the model to predict token content while being conditioned on correct positional information, maintaining causal attention flow without complex sampling schemes.

Table of Contents