Extend3D: Town-Scale 3D Generation
In this paper, we propose Extend3D, a training-free pipeline for 3D scene generation from a single image, built upon an object-centric 3D generative model. To overcome the limitations of fixed-size latent spaces in object-centric models for representing wide scenes, we extend the latent space in the x and y directions. Then, by dividing the extended latent space into overlapping patches, we apply the object-centric 3D generative model to each patch and couple them at each time step. Since patch-wise 3D generation with image conditioning requires strict spatial alignment between image and latent patches, we initialize the scene using a point cloud prior from a monocular depth estimator and iteratively refine occluded regions through SDEdit. We discovered that treating the incompleteness of 3D structure as noise during 3D refinement enables 3D completion via a concept, which we term under-noising. Furthermore, to address the sub-optimality of object-centric models for sub-scene generation, we optimize the extended latent during denoising, ensuring that the denoising trajectories remain consistent with the sub-scene dynamics. To this end, we introduce 3D-aware optimization objectives for improved geometric structure and texture fidelity. We demonstrate that our method yields better results than prior methods, as evidenced by human preference and quantitative experiments.
69cc9d3a3f84a0971be66bd7
Seungwoo Yoon
claimed_verified
2026-04-01T08:34:26.484Z
66728afc4de1918e09192525
/avatars/9a2d444f0065cbdb5e40420e2a7cc64b.svg
Seungwoo Yoon
SW-Yoon
user
SW-Yoon
69cc9d3a3f84a0971be66bd8
Jinmo Kim
claimed_verified
2026-04-03T09:08:57.596Z
655b2c2edeee83130a571041
https://cdn-avatars.huggingface.co/v1/production/uploads/655b2c2edeee83130a571041/nOknykBsNLnKAW-VvogWL.jpeg
Jinmo Kim
AImond
user
AImond
69cc9d3a3f84a0971be66bd9
Jaesik Park
Extend3D: Town-Scale 3D Generation
In this paper, we propose Extend3D, a training-free pipeline for 3D scene generation from a single image, built upon an object-centric 3D generative model. To overcome the limitations of fixed-size latent spaces in object-centric models for representing wide scenes, we extend the latent space in the x and y directions. Then, by dividing the extended latent space into overlapping patches, we apply the object-centric 3D generative model to each patch and couple them at each time step. Since patch-wise 3D generation with image conditioning requires strict spatial alignment between image and latent patches, we initialize the scene using a point cloud prior from a monocular depth estimator and iteratively refine occluded regions through SDEdit. We discovered that treating the incompleteness of 3D structure as noise during 3D refinement enables 3D completion via a concept, which we term under-noising. Furthermore, to address the sub-optimality of object-centric models for sub-scene generation, we optimize the extended latent during denoising, ensuring that the denoising trajectories remain consistent with the sub-scene dynamics. To this end, we introduce 3D-aware optimization objectives for improved geometric structure and texture fidelity. We demonstrate that our method yields better results than prior methods, as evidenced by human preference and quantitative experiments.
An object-centric 3D generative model is extended with adaptive latent space and iterative refinement to generate complete 3D scenes from single images, incorporating noise-aware completion and 3D-aware optimization for improved fidelity.
object-centric 3D generative model latent space extension overlapping patches SDEdit under-noising 3D-aware optimization objectives monocular depth estimator denoising trajectories geometric structure texture fidelity