SeqTex: Generate Mesh Textures in Video Sequence
Authors: name: Ze Yuan, name: Xin Yu, name: Yangtian Sun, name: Yuan-Chen Guo, name: Yan-Pei Cao, name: Ding Liang, name: Xiaojuan Qi

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.