Zhejiang University · Text-to-3D Generation

Block3D

Text-to-3D generation that partitions a discrete 1,024-token shape sequence into contiguous blocks, generates the blocks autoregressively, and denoises tokens within each block in parallel with confidence-guided intra-block correction. Reports a 5.15x end-to-end speedup over a fine-tuned autoregressive baseline — 4.99s against 25.71s — on a held-out split of TRELLIS-500K, at F-score@1% of 0.309 and normal consistency of 0.668. Code is on GitHub; the page states no parameter count, mesh resolution, VRAM requirement or licence.