OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder
Existing diffusion-based 3D scene generation methods primarily operate in 2D image/video latent spaces, which makes maintaining cross-view appearance and geometric consistency inherently challenging. To bridge this gap, we present OneWorld, a framework that performs diffusion directly within a coherent 3D representation space. Central to our approach is the 3D Unified Representation Autoencoder (3D-URAE); it leverages pretrained 3D foundation models and augments their geometry-centric nature by injecting appearance and distilling semantics into a unified 3D latent space. Furthermore, we introduce token-level Cross-View-Correspondence (CVC) consistency loss to explicitly enforce structural alignment across views, and propose Manifold-Drift Forcing (MDF) to mitigate train-inference exposure bias and shape a robust 3D manifold by mixing drifted and original representations. Comprehensive experiments demonstrate that OneWorld generates high-quality 3D scenes with superior cross-view consistency compared to state-of-the-art 2D-based methods. Our code will be available at https://github.com/SensenGao/OneWorld.
69ba0efc5d2b9c28ea09f0ab
Sensen Gao
claimed_verified
2026-03-18T10:17:03.023Z
659765e22235d4056ba80c0a
https://cdn-avatars.huggingface.co/v1/production/uploads/659765e22235d4056ba80c0a/dATESmijLO3CpD1sCMezg.jpeg
Gao Sensen
Sensen02
user
Sensen02
69ba0efc5d2b9c28ea09f0ac
Zhaoqing Wang
69ba0efc5d2b9c28ea09f0ad
Qihang Cao
69ba0efc5d2b9c28ea09f0ae
Dongdong Yu
69ba0efc5d2b9c28ea09f0af
Changhu Wang
69ba0efc5d2b9c28ea09f0b0
Tongliang Liu
69ba0efc5d2b9c28ea09f0b1
Mingming Gong
69ba0efc5d2b9c28ea09f0b2
Jiawang Bian
OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder
Existing diffusion-based 3D scene generation methods primarily operate in 2D image/video latent spaces, which makes maintaining cross-view appearance and geometric consistency inherently challenging. To bridge this gap, we present OneWorld, a framework that performs diffusion directly within a coherent 3D representation space. Central to our approach is the 3D Unified Representation Autoencoder (3D-URAE); it leverages pretrained 3D foundation models and augments their geometry-centric nature by injecting appearance and distilling semantics into a unified 3D latent space. Furthermore, we introduce token-level Cross-View-Correspondence (CVC) consistency loss to explicitly enforce structural alignment across views, and propose Manifold-Drift Forcing (MDF) to mitigate train-inference exposure bias and shape a robust 3D manifold by mixing drifted and original representations. Comprehensive experiments demonstrate that OneWorld generates high-quality 3D scenes with superior cross-view consistency compared to state-of-the-art 2D-based methods. Our code will be available at https://github.com/SensenGao/OneWorld.
OneWorld enables 3D scene generation by performing diffusion in a unified 3D representation space using a 3D Unified Representation Autoencoder and specialized consistency losses.
diffusion-based 3D scene generation 3D Unified Representation Autoencoder 3D latent space Cross-View-Correspondence Manifold-Drift Forcing appearance semantics geometry-centric exposure bias 3D manifold