SeqTex: Generate Mesh Textures in Video Sequence
Training native 3D texture generative models remains a fundamental yet
challenging problem, largely due to the limited availability of large-scale,
high-quality 3D texture datasets. This scarcity hinders generalization to
real-world scenarios. To address this, most existing methods finetune
foundation image generative models to exploit their learned visual priors.
However, these approaches typically generate only multi-view images and rely on
post-processing to produce UV texture maps -- an essential representation in
modern graphics pipelines. Such two-stage pipelines often suffer from error
accumulation and spatial inconsistencies across the 3D surface. In this paper,
we introduce SeqTex, a novel end-to-end framework that leverages the visual
knowledge encoded in pretrained video foundation models to directly generate
complete UV texture maps. Unlike previous methods that model the distribution
of UV textures in isolation, SeqTex reformulates the task as a sequence
generation problem, enabling the model to learn the joint distribution of
multi-view renderings and UV textures. This design effectively transfers the
consistent image-space priors from video foundation models into the UV domain.
To further enhance performance, we propose several architectural innovations: a
decoupled multi-view and UV branch design, geometry-informed attention to guide
cross-domain feature alignment, and adaptive token resolution to preserve fine
texture details while maintaining computational efficiency. Together, these
components allow SeqTex to fully utilize pretrained video priors and synthesize
high-fidelity UV texture maps without the need for post-processing. Extensive
experiments show that SeqTex achieves state-of-the-art performance on both
image-conditioned and text-conditioned 3D texture generation tasks, with
superior 3D consistency, texture-geometry alignment, and real-world
generalization.
686cef75cc230c60b4100b1d
Ze Yuan
claimed_verified
2025-07-08T12:40:21.376Z
64ad15a05d48838462e46687
/avatars/5f0d0233e99fdca3af8dca45341085be.svg
Ze Yuan
user
yuanze1024
686cef75cc230c60b4100b1e
Xin Yu
686cef75cc230c60b4100b1f
Yangtian Sun
686cef75cc230c60b4100b20
Yuan-Chen Guo
686cef75cc230c60b4100b21
Yan-Pei Cao
686cef75cc230c60b4100b22
Ding Liang
686cef75cc230c60b4100b23
Xiaojuan Qi
claimed_verified
2025-07-15T19:11:22.773Z
6875266f9cd3191dfddc7071
/avatars/64c581910833b111e9a7bae5b8740229.svg
xiaojuan qi
user
xjqi
SeqTex: Generate Mesh Textures in Video Sequence
Training native 3D texture generative models remains a fundamental yet
challenging problem, largely due to the limited availability of large-scale,
high-quality 3D texture datasets. This scarcity hinders generalization to
real-world scenarios. To address this, most existing methods finetune
foundation image generative models to exploit their learned visual priors.
However, these approaches typically generate only multi-view images and rely on
post-processing to produce UV texture maps -- an essential representation in
modern graphics pipelines. Such two-stage pipelines often suffer from error
accumulation and spatial inconsistencies across the 3D surface. In this paper,
we introduce SeqTex, a novel end-to-end framework that leverages the visual
knowledge encoded in pretrained video foundation models to directly generate
complete UV texture maps. Unlike previous methods that model the distribution
of UV textures in isolation, SeqTex reformulates the task as a sequence
generation problem, enabling the model to learn the joint distribution of
multi-view renderings and UV textures. This design effectively transfers the
consistent image-space priors from video foundation models into the UV domain.
To further enhance performance, we propose several architectural innovations: a
decoupled multi-view and UV branch design, geometry-informed attention to guide
cross-domain feature alignment, and adaptive token resolution to preserve fine
texture details while maintaining computational efficiency. Together, these
components allow SeqTex to fully utilize pretrained video priors and synthesize
high-fidelity UV texture maps without the need for post-processing. Extensive
experiments show that SeqTex achieves state-of-the-art performance on both
image-conditioned and text-conditioned 3D texture generation tasks, with
superior 3D consistency, texture-geometry alignment, and real-world
generalization.
SeqTex, an end-to-end framework, leverages pretrained video foundation models to generate high-fidelity UV texture maps directly, improving 3D consistency and generalization.
3D texture generative models UV texture maps pretrained video foundation models sequence generation decoupled multi-view and UV branch geometry-informed attention adaptive token resolution image-conditioned text-conditioned 3D consistency texture-geometry alignment