Articulate3D: Zero-Shot Text-Driven 3D Object Posing
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915464455651328 |
|---|---|
| author | Deb, Oishi Hu, Anjun Khakzar, Ashkan Torr, Philip Rupprecht, Christian |
| author_facet | Deb, Oishi Hu, Anjun Khakzar, Ashkan Torr, Philip Rupprecht, Christian |
| contents | We propose a training-free method, Articulate3D, to pose a 3D asset through language control. Despite advances in vision and language models, this task remains surprisingly challenging. To achieve this goal, we decompose the problem into two steps. We modify a powerful image-generator to create target images conditioned on the input image and a text instruction. We then align the mesh to the target images through a multi-view pose optimisation step. In detail, we introduce a self-attention rewiring mechanism (RSActrl) that decouples the source structure from pose within an image generative model, allowing it to maintain a consistent structure across varying poses. We observed that differentiable rendering is an unreliable signal for articulation optimisation; instead, we use keypoints to establish correspondences between input and target images. The effectiveness of Articulate3D is demonstrated across a diverse range of 3D objects and free-form text prompts, successfully manipulating poses while maintaining the original identity of the mesh. Quantitative evaluations and a comparative user study, in which our method was preferred over 85\% of the time, confirm its superiority over existing approaches. Project page:https://odeb1.github.io/articulate3d_page_deb/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19244 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Articulate3D: Zero-Shot Text-Driven 3D Object Posing Deb, Oishi Hu, Anjun Khakzar, Ashkan Torr, Philip Rupprecht, Christian Computer Vision and Pattern Recognition We propose a training-free method, Articulate3D, to pose a 3D asset through language control. Despite advances in vision and language models, this task remains surprisingly challenging. To achieve this goal, we decompose the problem into two steps. We modify a powerful image-generator to create target images conditioned on the input image and a text instruction. We then align the mesh to the target images through a multi-view pose optimisation step. In detail, we introduce a self-attention rewiring mechanism (RSActrl) that decouples the source structure from pose within an image generative model, allowing it to maintain a consistent structure across varying poses. We observed that differentiable rendering is an unreliable signal for articulation optimisation; instead, we use keypoints to establish correspondences between input and target images. The effectiveness of Articulate3D is demonstrated across a diverse range of 3D objects and free-form text prompts, successfully manipulating poses while maintaining the original identity of the mesh. Quantitative evaluations and a comparative user study, in which our method was preferred over 85\% of the time, confirm its superiority over existing approaches. Project page:https://odeb1.github.io/articulate3d_page_deb/ |
| title | Articulate3D: Zero-Shot Text-Driven 3D Object Posing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.19244 |