RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion
  Models
Text-to-texture generation has recently attracted increasing attention, but
existing methods often suffer from the problems of view inconsistencies,
apparent seams, and misalignment between textures and the underlying mesh. In
this paper, we propose a robust text-to-texture method for generating
consistent and seamless textures that are well aligned with the mesh. Our
method leverages state-of-the-art 2D diffusion models, including SDXL and
multiple ControlNets, to capture structural features and intricate details in
the generated textures. The method also employs a symmetrical view synthesis
strategy combined with regional prompts for enhancing view consistency.
Additionally, it introduces novel texture blending and soft-inpainting
techniques, which significantly reduce the seam regions. Extensive experiments
demonstrate that our method outperforms existing state-of-the-art methods.
67022b78ae1235c08ba4f564
Jangyeong Kim
claimed_verified
2024-10-07T07:26:52.030Z
634c1f9bb6628cbe2861dcc2
/avatars/dd48dff0b639123c605b5c0ee10577d7.svg
Jangyeong.Kim
user
longshiine
67022b78ae1235c08ba4f565
Donggoo Kang
admin_assigned
2024-10-07T09:16:52.614Z
62e772d0f97481d0a13f5766
/avatars/69415574d0cbf0887a151519ee579669.svg
Donggoo Kang
user
DK9
67022b78ae1235c08ba4f566
Junyoung Choi
67022b78ae1235c08ba4f567
Jeonga Wi
67022b78ae1235c08ba4f568
Junho Gwon
67022b78ae1235c08ba4f569
Jiun Bae
admin_assigned
2024-10-07T09:17:12.639Z
6304292d5c70c21d0eadf328
/avatars/a5e312922ba5026f3f223de7c65f0c3c.svg
Jiun Bae
user
Jiun
67022b78ae1235c08ba4f56a
Dumim Yoon
67022b78ae1235c08ba4f56b
Junghyun Han
RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion
  Models
Text-to-texture generation has recently attracted increasing attention, but
existing methods often suffer from the problems of view inconsistencies,
apparent seams, and misalignment between textures and the underlying mesh. In
this paper, we propose a robust text-to-texture method for generating
consistent and seamless textures that are well aligned with the mesh. Our
method leverages state-of-the-art 2D diffusion models, including SDXL and
multiple ControlNets, to capture structural features and intricate details in
the generated textures. The method also employs a symmetrical view synthesis
strategy combined with regional prompts for enhancing view consistency.
Additionally, it introduces novel texture blending and soft-inpainting
techniques, which significantly reduce the seam regions. Extensive experiments
demonstrate that our method outperforms existing state-of-the-art methods.
The proposed method uses state-of-the-art 2D diffusion models and novel techniques to generate consistent, seamless, and well-aligned textures for 3D meshes, improving upon existing methods.
2D diffusion models SDXL ControlNets symmetrical view synthesis regional prompts texture blending soft-inpainting