title: Unleashing Vecset Diffusion Model for Fast Shape Generation
summary: 3D shape generation has greatly flourished through the development of
  so-called "native" 3D diffusion, particularly through the Vecset Diffusion
  Model (VDM). While recent advancements have shown promising results in
  generating high-resolution 3D shapes, VDM still struggles with high-speed
  generation. Challenges exist because of difficulties not only in accelerating
  diffusion sampling but also VAE decoding in VDM, areas under-explored in
  previous works. To address these challenges, we present FlashVDM, a systematic
  framework for accelerating both VAE and DiT in VDM. For DiT, FlashVDM enables
  flexible diffusion sampling with as few as 5 inference steps and comparable
  quality, which is made possible by stabilizing consistency distillation with
  our newly introduced Progressive Flow Distillation. For VAE, we introduce a
  lightning vecset decoder equipped with Adaptive KV Selection, Hierarchical
  Volume Decoding, and Efficient Network Design. By exploiting the locality of
  the vecset and the sparsity of shape surface in the volume, our decoder
  drastically lowers FLOPs, minimizing the overall decoding overhead. We apply
  FlashVDM to Hunyuan3D-2 to obtain Hunyuan3D-2 Turbo. Through systematic
  evaluation, we show that our model significantly outperforms existing fast 3D
  generation methods, achieving comparable performance to the state-of-the-art
  while reducing inference time by over 45x for reconstruction and 32x for
  generation. Code and models are available at
  https://github.com/Tencent/FlashVDM.
paper_authors._id: 67dce2d2068292e7ef79b3dd
paper_authors.name: Zeqiang Lai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-21T11:40:33.586Z
paper_authors._id: 63044b89eedc089484c995ad
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/T4mOIQLaQdM5_oviaw_Cp.png
paper_authors.fullname: Zeqiang Lai
paper_authors.type: user
paper_authors.user: ZeqiangLai
paper_authors._id: 67dce2d2068292e7ef79b3de
paper_authors.name: Yunfei Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T12:00:37.346Z
paper_authors._id: 619b8af6cbedb87e1a92e692
paper_authors.avatarUrl: /avatars/1258fb2f2637adac8550100f3645651e.svg
paper_authors.fullname: YunFei Zhao
paper_authors.type: user
paper_authors.user: qikahh
paper_authors._id: 67dce2d2068292e7ef79b3df
paper_authors.name: Zibo Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T12:00:43.511Z
paper_authors._id: 62d8ce11c60d1450a1ed8795
paper_authors.avatarUrl: /avatars/26f1ca693ad7106be0f2f469070d8500.svg
paper_authors.fullname: zibo.zhao
paper_authors.type: user
paper_authors.user: cocacola
paper_authors._id: 67dce2d2068292e7ef79b3e0
paper_authors.name: Haolin Liu
paper_authors._id: 67dce2d2068292e7ef79b3e1
paper_authors.name: Fuyun Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T12:01:06.818Z
paper_authors._id: 63e9e92f20c109718713f5eb
paper_authors.avatarUrl: /avatars/9ff312e854d803e1a2e9e685a21d12f8.svg
paper_authors.fullname: Fu-Yun Wang
paper_authors.type: user
paper_authors.user: wangfuyun
paper_authors._id: 67dce2d2068292e7ef79b3e2
paper_authors.name: Huiwen Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T12:01:13.316Z
paper_authors._id: 67287a522ae45f363dd0ad43
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67287a522ae45f363dd0ad43/H6eyuxxSk6a84PzRoYcIU.png
paper_authors.fullname: huiwenshi
paper_authors.type: user
paper_authors.user: Huiwenshi
paper_authors._id: 67dce2d2068292e7ef79b3e3
paper_authors.name: Xianghui Yang
paper_authors._id: 67dce2d2068292e7ef79b3e4
paper_authors.name: Qinxiang Lin
paper_authors._id: 67dce2d2068292e7ef79b3e5
paper_authors.name: Jinwei Huang
paper_authors._id: 67dce2d2068292e7ef79b3e6
paper_authors.name: Yuhong Liu
paper_authors._id: 67dce2d2068292e7ef79b3e7
paper_authors.name: Jie Jiang
paper_authors._id: 67dce2d2068292e7ef79b3e8
paper_authors.name: Chunchao Guo
paper_authors._id: 67dce2d2068292e7ef79b3e9
paper_authors.name: Xiangyu Yue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T12:52:21.020Z
paper_authors._id: 666a8f24e2990b0cb16b7bf9
paper_authors.avatarUrl: /avatars/fcbaf8f1e3e53a2a4a819b7cb2c53aa4.svg
paper_authors.fullname: Xiangyu Yue
paper_authors.type: user
paper_authors.user: xyyue
paper_title: Unleashing Vecset Diffusion Model for Fast Shape Generation
paper_summary: 3D shape generation has greatly flourished through the development of
  so-called "native" 3D diffusion, particularly through the Vecset Diffusion
  Model (VDM). While recent advancements have shown promising results in
  generating high-resolution 3D shapes, VDM still struggles with high-speed
  generation. Challenges exist because of difficulties not only in accelerating
  diffusion sampling but also VAE decoding in VDM, areas under-explored in
  previous works. To address these challenges, we present FlashVDM, a systematic
  framework for accelerating both VAE and DiT in VDM. For DiT, FlashVDM enables
  flexible diffusion sampling with as few as 5 inference steps and comparable
  quality, which is made possible by stabilizing consistency distillation with
  our newly introduced Progressive Flow Distillation. For VAE, we introduce a
  lightning vecset decoder equipped with Adaptive KV Selection, Hierarchical
  Volume Decoding, and Efficient Network Design. By exploiting the locality of
  the vecset and the sparsity of shape surface in the volume, our decoder
  drastically lowers FLOPs, minimizing the overall decoding overhead. We apply
  FlashVDM to Hunyuan3D-2 to obtain Hunyuan3D-2 Turbo. Through systematic
  evaluation, we show that our model significantly outperforms existing fast 3D
  generation methods, achieving comparable performance to the state-of-the-art
  while reducing inference time by over 45x for reconstruction and 32x for
  generation. Code and models are available at
  https://github.com/Tencent/FlashVDM.
paper_ai_summary: FlashVDM accelerates both diffusion sampling and VAE decoding in 3D diffusion models, achieving high performance with reduced inference times.
paper_ai_keywords: 3D diffusion Vecset Diffusion Model VDM diffusion sampling VAE decoding FlashVDM DiT Progressive Flow Distillation lightning vecset decoder Adaptive KV Selection Hierarchical Volume Decoding Efficient Network Design FLOPs Hunyuan3D-2 Hunyuan3D-2 Turbo