FlexPainter: Flexible and Multi-View Consistent Texture Generation
Texture map production is an important part of 3D modeling and determines the
rendering quality. Recently, diffusion-based methods have opened a new way for
texture generation. However, restricted control flexibility and limited prompt
modalities may prevent creators from producing desired results. Furthermore,
inconsistencies between generated multi-view images often lead to poor texture
generation quality. To address these issues, we introduce FlexPainter,
a novel texture generation pipeline that enables flexible multi-modal
conditional guidance and achieves highly consistent texture generation. A
shared conditional embedding space is constructed to perform flexible
aggregation between different input modalities. Utilizing such embedding space,
we present an image-based CFG method to decompose structural and style
information, achieving reference image-based stylization. Leveraging the 3D
knowledge within the image diffusion prior, we first generate multi-view images
simultaneously using a grid representation to enhance global understanding.
Meanwhile, we propose a view synchronization and adaptive weighting module
during diffusion sampling to further ensure local consistency. Finally, a
3D-aware texture completion model combined with a texture enhancement model is
used to generate seamless, high-resolution texture maps. Comprehensive
experiments demonstrate that our framework significantly outperforms
state-of-the-art methods in both flexibility and generation quality.
68425ef33b5bb39c456487e0
Dongyu Yan
claimed_verified
2025-06-06T07:40:56.223Z
64049ae20ab5e22719f35103
https://cdn-avatars.huggingface.co/v1/production/uploads/1678023295407-noauth.jpeg
Dongyu Yan
user
StarYDY
68425ef33b5bb39c456487e1
Leyi Wu
claimed_verified
2025-06-07T13:33:56.360Z
6622f3e1c80be2cc569fb5e1
/avatars/f4bacc6e090ec6e6d9f89b279783f1bd.svg
LeyiWu
user
YUEVII
68425ef33b5bb39c456487e2
Jiantao Lin
claimed_verified
2025-06-14T06:24:45.354Z
6332e2689bf698ce68a22e8c
/avatars/c1922acfda2e6d2fe7b03194a404eb10.svg
JIANTAO LIN
user
LTT
68425ef33b5bb39c456487e3
Luozhou Wang
68425ef33b5bb39c456487e4
Tianshuo Xu
68425ef33b5bb39c456487e5
Zhifei Chen
68425ef33b5bb39c456487e6
Zhen Yang
claimed_verified
2025-06-07T05:48:51.341Z
63f58403fcf95ecac2b33d78
/avatars/a77ea80784896502ae1cfa086a78ce66.svg
Zhen Yang
user
YZCS
68425ef33b5bb39c456487e7
Lie Xu
68425ef33b5bb39c456487e8
Shunsi Zhang
68425ef33b5bb39c456487e9
Yingcong Chen
extracted_pending
2025-06-06T03:22:29.750Z
655cba1d87b67834000590e8
/avatars/3bd43b7c9351f65b8f38f4c8237a0146.svg
Yingcong Chen
user
yingcongchen
FlexPainter: Flexible and Multi-View Consistent Texture Generation
Texture map production is an important part of 3D modeling and determines the
rendering quality. Recently, diffusion-based methods have opened a new way for
texture generation. However, restricted control flexibility and limited prompt
modalities may prevent creators from producing desired results. Furthermore,
inconsistencies between generated multi-view images often lead to poor texture
generation quality. To address these issues, we introduce FlexPainter,
a novel texture generation pipeline that enables flexible multi-modal
conditional guidance and achieves highly consistent texture generation. A
shared conditional embedding space is constructed to perform flexible
aggregation between different input modalities. Utilizing such embedding space,
we present an image-based CFG method to decompose structural and style
information, achieving reference image-based stylization. Leveraging the 3D
knowledge within the image diffusion prior, we first generate multi-view images
simultaneously using a grid representation to enhance global understanding.
Meanwhile, we propose a view synchronization and adaptive weighting module
during diffusion sampling to further ensure local consistency. Finally, a
3D-aware texture completion model combined with a texture enhancement model is
used to generate seamless, high-resolution texture maps. Comprehensive
experiments demonstrate that our framework significantly outperforms
state-of-the-art methods in both flexibility and generation quality.
FlexPainter, a novel texture generation pipeline, uses a shared conditional embedding space to enable flexible multi-modal guidance, ensuring high-quality and consistent texture map generation using image diffusion priors and a 3D-aware model.
diffusion-based methods texture generation flexible multi-modal conditional guidance conditional embedding space image-based CFG method structural information style information reference image-based stylization image diffusion prior grid representation view synchronization adaptive weighting module 3D-aware texture completion model texture enhancement model