DreamCharacter-1: From 3D Generative Foundation Models to Product-Ready Character Generation
Quick Answer
DreamCharacter-1 is a lightweight framework that enhances pretrained 3D models for high-fidelity character generation, incorporating geometry and texture post-training along with inference acceleration.
Quick Take
DreamCharacter-1 is a lightweight framework that enhances pretrained 3D models for high-fidelity character generation, incorporating geometry and texture post-training along with inference acceleration. It outperforms existing methods in producing visually compelling and structurally robust 3D character assets, demonstrating significant improvements in both qualitative and quantitative metrics.
Key Points
- Incorporates geometry post-training for enhanced surface detail optimization.
- Utilizes texture post-training to synthesize high-resolution textures.
- Features inference acceleration for scalable deployment of character assets.
- Demonstrates superior performance over state-of-the-art character generation methods.
- Extensive experiments validate the framework's effectiveness in 3D character generation.
Paper Resources
📖 Reader Mode
~2 min readAbstract:We present DreamCharacter-1, a lightweight post-adaptation framework that calibrates pretrained 3D foundation models toward high-fidelity, production-ready 3D character generation. Building upon a 3D foundation backbone, our pipeline incorporates three task-oriented components: (1) geometry post-training, which enhances fine-grained surface details through geometric preference optimization; (2) texture post-training, which synthesizes high-resolution textures and refines the appearance of occluded regions; and (3) inference acceleration, which enables scalable deployment. Extensive quantitative and qualitative experiments demonstrate that DreamCharacter-1 produces visually compelling and structurally robust 3D character assets, consistently surpassing state-of-the-art character generation methods.
| Comments: | Official Page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.07817 [cs.CV] |
| (or arXiv:2607.07817v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.07817 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Weizhe Liu [view email]
[v1]
Wed, 8 Jul 2026 18:02:57 UTC (44,340 KB)
— Originally published at arxiv.org
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