3D生成提速5倍，不再反复改全图
现在的AI生成3D模型，要么像写文章一样一个字一个字往外蹦(错了不能回头改)，要么像画油画一样反复涂改整张画布(慢)。这篇把两者拆开：把3D模型切成小块，每次只生成并修正当前这一块，生成完再拼起来。在TRELLIS-500K数据集上，从25.71秒降到4.99秒，快了5倍，质量没掉。它不是你明天就能用的工具，但告诉你一个方向：3D生成的速度瓶颈，可能不是算力不够，而是策略太笨。
📄 原文摘要(英文)
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.