DreamCharacter-1: From 3D Generative Foundation Models to Product-Ready Character Generation

2026-07-08

AI Summary

This work addresses the challenge of efficiently transforming pretrained 3D foundation models into high-fidelity, production-ready 3D character assets. To this end, we propose a lightweight post-adaptation framework that enhances surface details through geometry-aware preference optimization, improves visual quality via high-resolution texture synthesis coupled with appearance inpainting in occluded regions, and incorporates an inference acceleration mechanism to enable scalable deployment. Our approach achieves the first end-to-end optimization pipeline that bridges general-purpose 3D generative models and product-grade character assets, surpassing state-of-the-art methods in both visual expressiveness and structural robustness.

Abstract

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.

Problem

• 3D character generation
• foundation models
• high-fidelity
• production-ready
• geometric detail

Innovation

• 3D generative foundation models
• geometry post-training
• texture synthesis
• inference acceleration
• character generation