Scene-Conditional 3D Object Stylization and Composition
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908344466276352 |
|---|---|
| author | Zhou, Jinghao Jakab, Tomas Torr, Philip Rupprecht, Christian |
| author_facet | Zhou, Jinghao Jakab, Tomas Torr, Philip Rupprecht, Christian |
| contents | Recently, 3D generative models have made impressive progress, enabling the generation of almost arbitrary 3D assets from text or image inputs. However, these approaches generate objects in isolation without any consideration for the scene where they will eventually be placed. In this paper, we propose a framework that allows for the stylization of an existing 3D asset to fit into a given 2D scene, and additionally produce a photorealistic composition as if the asset was placed within the environment. This not only opens up a new level of control for object stylization, for example, the same assets can be stylized to reflect changes in the environment, such as summer to winter or fantasy versus futuristic settings-but also makes the object-scene composition more controllable. We achieve this by combining modeling and optimizing the object's texture and environmental lighting through differentiable ray tracing with image priors from pre-trained text-to-image diffusion models. We demonstrate that our method is applicable to a wide variety of indoor and outdoor scenes and arbitrary objects. Project page: https://jensenzhoujh.github.io/scene-cond-3d/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_12419 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Scene-Conditional 3D Object Stylization and Composition Zhou, Jinghao Jakab, Tomas Torr, Philip Rupprecht, Christian Computer Vision and Pattern Recognition Recently, 3D generative models have made impressive progress, enabling the generation of almost arbitrary 3D assets from text or image inputs. However, these approaches generate objects in isolation without any consideration for the scene where they will eventually be placed. In this paper, we propose a framework that allows for the stylization of an existing 3D asset to fit into a given 2D scene, and additionally produce a photorealistic composition as if the asset was placed within the environment. This not only opens up a new level of control for object stylization, for example, the same assets can be stylized to reflect changes in the environment, such as summer to winter or fantasy versus futuristic settings-but also makes the object-scene composition more controllable. We achieve this by combining modeling and optimizing the object's texture and environmental lighting through differentiable ray tracing with image priors from pre-trained text-to-image diffusion models. We demonstrate that our method is applicable to a wide variety of indoor and outdoor scenes and arbitrary objects. Project page: https://jensenzhoujh.github.io/scene-cond-3d/. |
| title | Scene-Conditional 3D Object Stylization and Composition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.12419 |