Block3D：通过分块扩散实现的高效文本到3D生成

摘要

尽管文本到3D生成技术发展迅速，但在低推理成本下实现高几何保真度仍然具有挑战性。现有的文本到3D方法要么自回归地解码离散形状标记，要么利用扩散模型或流模型迭代细化全局3D表示。然而，自回归解码是顺序进行的，无法修正错误；而扩散模型和流匹配模型需反复处理完整表示，使得高质量生成的成本越来越高。本文提出Block3D，一种分块扩散框架，它将离散形状标记序列划分为连续块，自回归地生成这些块，并对当前块内的所有标记进行联合去噪。为缓解误差累积，我们引入置信度引导的块内修正，在每块定稿之前对低置信度标记进行修正。在TRELLIS-500K的留出集上，Block3D将平均端到端生成时间从25.71秒降至4.99秒，相比微调的自回归基线实现了5.15倍加速，且不牺牲几何保真度。

English

While text-to-3D generation has advanced rapidly, achieving high geometric fidelity at low inference cost remains challenging. Existing text-to-3D methods either decode discrete shape tokens autoregressively or iteratively refine global 3D representations with diffusion or flow models. However, autoregressive decoding is sequential and cannot revise errors, whereas diffusion and flow-matching models repeatedly process the full representation, making high-quality generation increasingly expensive. In this paper, we propose Block3D, a block-wise diffusion framework that partitions the discrete shape-token sequence into contiguous blocks, generates the blocks autoregressively, and jointly denoises all tokens within the current block. To alleviate error accumulation, we introduce confidence-guided intra-block correction, which revises low-confidence tokens before each block is finalized. On a held-out set from TRELLIS-500K, Block3D reduces mean end-to-end generation time from 25.71 seconds to 4.99 seconds, achieving a 5.15times speedup over the fine-tuned autoregressive baseline without sacrificing geometric fidelity.