Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D
  Assets
Authors: name: Team Hunyuan3D; name: Bowen Zhang; name: Chunchao Guo; name: Haolin Liu; name: Hongyu Yan; name: Huiwen Shi; name: Jingwei Huang; name: Junlin Yu; name: Kunhong Li; name: Linus; name: Penghao Wang; name: Qingxiang Lin; name: Sicong Liu; name: Xianghui Yang; name: Yixuan Tang; name: Yunfei Zhao; name: Zeqiang Lai; name: Zhihao Liang; name: Zibo Zhao
Published: 2025-09-25 14:39:17

Abstract: 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.