Block3D: Efficient Text-to-3D Generation via Block-Wise Diffusion

2026-08-19

AI Summary

为解决高效生成高质量3D模型的问题，提出Block3D方法，通过分块扩散和置信度引导的校正机制，大幅降低生成时间而不牺牲几何保真度。

Abstract

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 $5.15\times$ speedup over the fine-tuned autoregressive baseline without sacrificing geometric fidelity.

Problem

• text-to-3D generation
• geometric fidelity
• inference cost
• autoregressive decoding
• diffusion models

Innovation

• block-wise diffusion
• confidence-guided intra-block correction
• autoregressive generation
• geometric fidelity