Hugging Face Daily Papers · · 3 min read

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

Mirrored from Hugging Face Daily Papers for archival readability. Support the source by reading on the original site.

Block3D is an efficient text-to-3D generation framework that shifts the causal dependency of discrete shape tokens from individual tokens to contiguous blocks.</p>\n","updatedAt":"2026-08-25T06:08:39.523Z","author":{"_id":"66699aa8a33847217b5a49c7","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png","fullname":"Weijie Wang","name":"lhmd","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":12,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.815278172492981},"editors":["lhmd"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.19567","authors":[{"_id":"6a8b17d03d26296ea30917a5","name":"Bowen Cui","hidden":false},{"_id":"6a8b17d03d26296ea30917a6","user":{"_id":"66699aa8a33847217b5a49c7","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png","isPro":false,"fullname":"Weijie Wang","user":"lhmd","type":"user","name":"lhmd"},"name":"Weijie Wang","status":"claimed_verified","statusLastChangedAt":"2026-08-23T16:45:04.537Z","hidden":false},{"_id":"6a8b17d03d26296ea30917a7","name":"Zeyu Zhang","hidden":false},{"_id":"6a8b17d03d26296ea30917a8","name":"Yefei He","hidden":false},{"_id":"6a8b17d03d26296ea30917a9","name":"Mingda Lin","hidden":false},{"_id":"6a8b17d03d26296ea30917aa","name":"Haoyu Zhao","hidden":false},{"_id":"6a8b17d03d26296ea30917ab","name":"Yuanyu He","hidden":false},{"_id":"6a8b17d03d26296ea30917ac","user":{"_id":"653862bdbe39573b3b247b44","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/653862bdbe39573b3b247b44/oMqOC4USbQlPX5HFLDXt6.jpeg","isPro":false,"fullname":"Donny Chen","user":"donydchen","type":"user","name":"donydchen"},"name":"Donny Y. Chen","status":"claimed_verified","statusLastChangedAt":"2026-08-25T08:14:39.574Z","hidden":false},{"_id":"6a8b17d03d26296ea30917ad","name":"Feng Chen","hidden":false},{"_id":"6a8b17d03d26296ea30917ae","name":"Bohan Zhuang","hidden":false}],"publishedAt":"2026-08-20T00:00:00.000Z","submittedOnDailyAt":"2026-08-25T00:00:00.000Z","title":"Block3D: Efficient Text-to-3D Generation via Block-Wise Diffusion","submittedOnDailyBy":{"_id":"66699aa8a33847217b5a49c7","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png","isPro":false,"fullname":"Weijie Wang","user":"lhmd","type":"user","name":"lhmd"},"summary":"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.","upvotes":19,"discussionId":"6a8b17d03d26296ea30917af","projectPage":"https://alexandertsui.github.io/block3d/","githubRepo":"https://github.com/ziplab/Block3D","githubRepoAddedBy":"user","ai_summary":"Block3D accelerates text-to-3D generation by using block-wise diffusion with confidence-guided correction to reduce inference time while preserving geometric fidelity.","ai_keywords":["text-to-3D generation","discrete shape tokens","autoregressive decoding","diffusion models","flow-matching","block-wise diffusion","confidence-guided intra-block correction"],"ai_summary_model":"thinkingmachines/Inkling-Small","githubStars":3,"organization":{"_id":"61bac2af530e5c78d7b99667","name":"zju","fullname":"Zhejiang University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/5e1058e9fcf41d740b69966d/7G1xjlxwCdMEmKcxNR0n5.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"653862bdbe39573b3b247b44","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/653862bdbe39573b3b247b44/oMqOC4USbQlPX5HFLDXt6.jpeg","isPro":false,"fullname":"Donny Chen","user":"donydchen","type":"user"},{"_id":"66699aa8a33847217b5a49c7","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u8Z-6U8U7ARXOpdBDI7Qm.png","isPro":false,"fullname":"Weijie Wang","user":"lhmd","type":"user"},{"_id":"66974e7a57a5a55a1f4a0469","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/66974e7a57a5a55a1f4a0469/76WAtxoxJO5rjZWxEmF2t.jpeg","isPro":false,"fullname":"xxh","user":"shreddedpork","type":"user"},{"_id":"69a54b7ea022769cc83b28cc","avatarUrl":"/avatars/2889c2208ea70979d8942bc047332813.svg","isPro":false,"fullname":"Alexander Tsui","user":"Alexander1211","type":"user"},{"_id":"65df4f5af3efe60b06ea1409","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65df4f5af3efe60b06ea1409/u-9mnMU8bNXvyQ8z1CG5m.png","isPro":false,"fullname":"Xinye Li","user":"asdfo123","type":"user"},{"_id":"687f853bb39262ba84f3eeff","avatarUrl":"/avatars/cdfc44fde8237f08f10192553fe5a075.svg","isPro":false,"fullname":"Junhao Shen","user":"shenjunhao","type":"user"},{"_id":"65acf8d11f4b7b68fdecbbe6","avatarUrl":"/avatars/9e1261ce09b327cf79d44521666ec568.svg","isPro":false,"fullname":"WuYucheng","user":"Manolorsea","type":"user"},{"_id":"66decf61f9971122eec44dc8","avatarUrl":"/avatars/ffd1bf114f2fffc1f9a2ffe5964543f3.svg","isPro":false,"fullname":"Enjun Du","user":"EnjunDu","type":"user"},{"_id":"696db9060aefda1f4520816e","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/5WcvTplXGNUn7Y9vvC_Us.jpeg","isPro":false,"fullname":"Zhaoyang Liu","user":"AngelinaTheLime","type":"user"},{"_id":"65afbba21edab235a1323ad0","avatarUrl":"/avatars/bd047a559821e2bc802d66a073b994df.svg","isPro":true,"fullname":"3","user":"xuzishan","type":"user"},{"_id":"668fcd97495674543600db0b","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/JfBeJ6gSXe379G9ejjrLR.jpeg","isPro":false,"fullname":"Gao Mingqi","user":"EchoMinkki","type":"user"},{"_id":"68247dc1bbf412bd49e6002a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/68247dc1bbf412bd49e6002a/5fNm-XeX2PE_AJiitNCu2.jpeg","isPro":false,"fullname":"Georgia(ZZY)","user":"bajiang","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"61bac2af530e5c78d7b99667","name":"zju","fullname":"Zhejiang University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/5e1058e9fcf41d740b69966d/7G1xjlxwCdMEmKcxNR0n5.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2608/2608.19567.md","query":{}}">
Papers
arxiv:2608.19567

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

Published on Aug 20
· Submitted by
Weijie Wang
on Aug 25
Authors:
,

Abstract

Block3D accelerates text-to-3D generation by using block-wise diffusion with confidence-guided correction to reduce inference time while preserving geometric fidelity.

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.

Community

Paper author Paper submitter about 3 hours ago

Block3D is an efficient text-to-3D generation framework that shifts the causal dependency of discrete shape tokens from individual tokens to contiguous blocks.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.
Tap or paste here to upload images

· Sign up or log in to comment

Get this paper in your agent:

hf papers read 2608.19567
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper

No model linking this paper

Cite arxiv.org/abs/2608.19567 in a model README.md to link it from this page.

Datasets citing this paper

No dataset linking this paper

Cite arxiv.org/abs/2608.19567 in a dataset README.md to link it from this page.

Spaces citing this paper

No Space linking this paper

Cite arxiv.org/abs/2608.19567 in a Space README.md to link it from this page.

Collections including this paper

No Collection including this paper

Add this paper to a collection to link it from this page.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from Hugging Face Daily Papers