English

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training

Computer Vision and Pattern Recognition 2026-01-07 v1

Abstract

We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware optimization, manual assembly, or 2D image generation, often produce unrealistic or incoherent 3D assets due to the challenges of intricate part-level manipulation and limited out-of-domain generation. In contrast, Muses leverages the 3D skeleton, a fundamental representation of biological forms, to explicitly and rationally compose diverse elements. This skeletal foundation formalizes 3D content creation as a structure-aware pipeline of design, composition, and generation. Muses begins by constructing a creatively composed 3D skeleton with coherent layout and scale through graph-constrained reasoning. This skeleton then guides a voxel-based assembly process within a structured latent space, integrating regions from different objects. Finally, image-guided appearance modeling under skeletal conditions is applied to generate a style-consistent and harmonious texture for the assembled shape. Extensive experiments establish Muses' state-of-the-art performance in terms of visual fidelity and alignment with textual descriptions, and potential on flexible 3D object editing. Project page: https://luhexiao.github.io/Muses.github.io/.

Cite

@article{arxiv.2601.03256,
  title  = {Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training},
  author = {Hexiao Lu and Xiaokun Sun and Zeyu Cai and Hao Guo and Ying Tai and Jian Yang and Zhenyu Zhang},
  journal= {arXiv preprint arXiv:2601.03256},
  year   = {2026}
}

Comments

Project page: https://luhexiao.github.io/Muses.github.io/

R2 v1 2026-07-01T08:53:02.638Z