# Block3D：通过分块扩散实现高效文本到3D生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-20 08:00
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmt8bo2db30obro731zn7jc5u
- 原文链接：https://arxiv.org/abs/2608.19567

## AI 摘要

Block3D提出一种分块扩散框架，将离散形状token序列划分为连续块，以自回归方式生成各块，并对当前块内所有token进行联合去噪。该方法引入置信度引导的块内修正机制，在每块完成前修正低置信度token，以缓解误差累积。在TRELLIS-500K留出集上，Block3D将端到端平均生成时间从25.71秒降至4.99秒，较微调自回归基线实现5.15倍加速，且不损失几何保真度。

## 正文

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.