MegaParts：透過Token高效自回歸建模將部件感知的3D物體生成擴展至300個部件

摘要

部件感知的3D物體生成對於圖形學應用至關重要，例如可控建模、編輯與關節化，其中物體被表示為語義部件的連貫組合。然而，現有的部件感知生成方法在高度複雜的物體上難以有效擴展。隨著部件數量的增加，生成精細幾何形狀在token長度和記憶體方面的成本變得高得令人卻步。我們提出MegaParts，這是一個可擴展的自迴歸3D生成框架，透過結合結構化序列建模與token高效的向量量化形狀tokenizer來應對這項挑戰。我們的tokenizer透過在高保真重建的約束下最小化token使用量，學習部件級幾何形狀的離散潛在表徵，從而實現基於幾何複雜度的自適應長度token化。在此緊湊表徵的基礎上，我們訓練一個大型語言模型，在統一的結構化序列中生成物體邊界框、部件邊界框和部件形狀token。結合高效的長上下文訓練策略，我們的token高效方案可擴展到具有多達300個部件、序列長度高達256k個token的物體。這大幅擴展了部件感知3D生成的規模，同時保留組合結構並實現細粒度的部件級控制。我們的方法在網格品質上優於基線自迴歸模型與擴散模型，顯示壓縮的離散部件token不僅提升了可擴展性，也提升了生成幾何形狀可達到的保真度。這些結果表明，LLM原生的token高效自迴歸建模是擴散模型在大規模部件感知3D生成方面一個有吸引力的替代方案。

English

Part-aware 3D object generation is essential for graphics applications such as controllable modeling, editing, and articulation, where objects are represented as coherent assemblies of semantic parts. However, existing part-aware generation methods, do not scale well to highly complex objects. As the number of parts increases, generating detailed geometry becomes prohibitively expensive in token length and memory. We introduce MegaParts, a scalable autoregressive 3D generation framework to address this challenge by combining structured sequence modeling with a token-efficient vector-quantized shape tokenizer. Our tokenizer learns discrete latent representations for part-level geometry by minimizing token usage subject to high-fidelity reconstruction, enabling adaptive-length tokenization based on geometric complexity. On top of this compact representation, we train a large language model to generate object bounding boxes, part bounding boxes, and part shape tokens within a unified structured sequence. Combined with efficient long-context training strategy, our token-efficient formulation scales to objects with up to 300 parts and sequence lengths up to 256k tokens. This substantially extends the scale of part-aware 3D generation while preserving compositional structure and enabling fine-grained part-level control. Our method achieves higher mesh quality than baseline autoregressive and diffusion models, showing that compressed discrete part tokens improve not only scalability but also the achievable fidelity of generated geometry. These results suggest that LLM native token-efficient autoregressive modeling is a compelling alternative to diffusion for large-scale part-aware 3D generation. The project page is available at https://expmaster.github.io/megaparts_webpage.