Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D
  Assets
Recent advances in 3D-native generative models have accelerated asset
creation for games, film, and design. However, most methods still rely
primarily on image or text conditioning and lack fine-grained, cross-modal
controls, which limits controllability and practical adoption. To address this
gap, we present Hunyuan3D-Omni, a unified framework for fine-grained,
controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images,
Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose
priors as conditioning signals, enabling precise control over geometry,
topology, and pose. Instead of separate heads for each modality, our model
unifies all signals in a single cross-modal architecture. We train with a
progressive, difficulty-aware sampling strategy that selects one control
modality per example and biases sampling toward harder signals (e.g., skeletal
pose) while downweighting easier ones (e.g., point clouds), encouraging robust
multi-modal fusion and graceful handling of missing inputs. Experiments show
that these additional controls improve generation accuracy, enable
geometry-aware transformations, and increase robustness for production
workflows.
68d5f2138ccd91bdd39ffd3f
Team Hunyuan3D
68d5f2138ccd91bdd39ffd41
Bowen Zhang
68d5f2138ccd91bdd39ffd42
Chunchao Guo
68d5f2138ccd91bdd39ffd43
Haolin Liu
68d5f2138ccd91bdd39ffd44
Hongyu Yan
68d5f2138ccd91bdd39ffd45
Huiwen Shi
claimed_verified
2025-09-26T12:20:14.572Z
67287a522ae45f363dd0ad43
https://cdn-avatars.huggingface.co/v1/production/uploads/67287a522ae45f363dd0ad43/H6eyuxxSk6a84PzRoYcIU.png
huiwenshi
user
Huiwenshi
68d5f2138ccd91bdd39ffd46
Jingwei Huang
68d5f2138ccd91bdd39ffd47
Junlin Yu
68d5f2138ccd91bdd39ffd48
Kunhong Li
admin_assigned
2025-09-26T15:37:25.762Z
6565d276a69ed97d4d3b41fc
/avatars/66e517ef13579c71ceb2c1b643502f42.svg
Kunhong Li
user
KunB
68d5f2138ccd91bdd39ffd49
Linus
68d5f2138ccd91bdd39ffd4a
Penghao Wang
claimed_verified
2025-09-26T12:19:58.885Z
6462120032083c28b408e754
/avatars/f02c6d23f03420b716d995c92f8e2213.svg
Penghao Wang
user
AuWang
68d5f2138ccd91bdd39ffd4b
Qingxiang Lin
68d5f2138ccd91bdd39ffd4c
Sicong Liu
admin_assigned
2025-09-26T15:37:11.859Z
64ae973b6f2769d726dd6a47
/avatars/b5edcf73c361557700ecad90038e00bc.svg
sicong liu
user
sicongmf
68d5f2138ccd91bdd39ffd4d
Xianghui Yang
claimed_verified
2025-09-26T12:20:10.459Z
647d9e881a1fcad2fdbf4954
/avatars/92ee8727d5c9063d852d3537b7690843.svg
SeanYoung
user
SeanYoungxh
68d5f2138ccd91bdd39ffd4e
Yixuan Tang
68d5f2138ccd91bdd39ffd4f
Yunfei Zhao
68d5f2138ccd91bdd39ffd50
Zeqiang Lai
admin_assigned
2025-09-26T15:36:37.184Z
63044b89eedc089484c995ad
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/T4mOIQLaQdM5_oviaw_Cp.png
Zeqiang Lai
user
ZeqiangLai
68d5f2138ccd91bdd39ffd51
Zhihao Liang
68d5f2138ccd91bdd39ffd52
Zibo Zhao
admin_assigned
2025-09-26T15:36:25.208Z
62d8ce11c60d1450a1ed8795
/avatars/26f1ca693ad7106be0f2f469070d8500.svg
zibo.zhao
user
cocacola
Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D
  Assets
Recent advances in 3D-native generative models have accelerated asset
creation for games, film, and design. However, most methods still rely
primarily on image or text conditioning and lack fine-grained, cross-modal
controls, which limits controllability and practical adoption. To address this
gap, we present Hunyuan3D-Omni, a unified framework for fine-grained,
controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images,
Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose
priors as conditioning signals, enabling precise control over geometry,
topology, and pose. Instead of separate heads for each modality, our model
unifies all signals in a single cross-modal architecture. We train with a
progressive, difficulty-aware sampling strategy that selects one control
modality per example and biases sampling toward harder signals (e.g., skeletal
pose) while downweighting easier ones (e.g., point clouds), encouraging robust
multi-modal fusion and graceful handling of missing inputs. Experiments show
that these additional controls improve generation accuracy, enable
geometry-aware transformations, and increase robustness for production
workflows.
Hunyuan3D-Omni is a unified 3D asset generation framework that accepts multiple conditioning signals, improving controllability and robustness in production workflows.
3D-native generative models point clouds voxels bounding boxes skeletal pose priors cross-modal architecture progressive sampling strategy difficulty-aware sampling multi-modal fusion