UltraShape 1.0이 만든 고품질 3D 에셋의 혁신

📝 원문 정보

• Title: UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
• ArXiv ID: 2512.21185
• 발행일: 2025-12-24
• 저자: Tanghui Jia, Dongyu Yan, Dehao Hao, Yang Li, Kaiyi Zhang, Xianyi He, Lanjiong Li, Yuhan Wang, Jinnan Chen, Lutao Jiang, Qishen Yin, Long Quan, Ying-Cong Chen, Li Yuan

📝 초록 (Abstract)

💡 논문 핵심 해설 (Deep Analysis)

Paper Analysis Based on the Korean Translation

Title and Abstract

The paper titled “UltraShape 1.0: An Innovative Deep Learning Approach for High-Quality 3D Assets” introduces a novel deep learning framework designed to address challenges in generating high-quality 3D assets. The abstract highlights that UltraShape 1.0 integrates data processing and generation modeling, ensuring the creation of water-tight geometry and applying comprehensive filtering strategies to generate high-quality 3D datasets.

Introduction

The introduction emphasizes the critical role of 3D content creation across various fields such as film, augmented reality, robotics, and industrial design. It points out that while learning-based 3D generation techniques have become a significant research direction in computer vision and graphics, they face numerous challenges due to data scarcity and geometric complexity.

Challenges in Existing Methods

The paper discusses the limitations of existing methods like UDF (Unsigned Distance Function) based remeshing, visibility check-based approaches, and flood fill-based strategies. These include issues such as inaccurate surface encoding, sensitivity to occlusions, and high-frequency geometric noise in complex regions.

UltraShape 1.0 Framework

UltraShape 1.0 is proposed to address these challenges by integrating data processing and generation modeling for 3D deep learning. It introduces a two-stage joining strategy that considers both global structure and fine details for generating high-quality geometry.

• Water-tight Geometry Processing: The framework ensures globally defined internal/external partitions, which are crucial for meaningful volume representations like SDFs.
• Data Filtering Pipeline: A custom data filtering pipeline is developed to ensure the quality of training datasets. This includes VLM-based filtering, pose normalization, and geometric filtering.

Two-stage Geometric Generation

The two-stage generation process involves:

1. First Stage: Focuses on generating reliable and informative volume queries, capturing the overall shape of objects.
2. Second Stage: Separates spatial positioning from detailed synthesis to reduce fine-grained dispersion and stabilize training.

This approach uses DiT (Deep Image Transformer) based 3D generation models for vector set representation in the first stage and diffusion-based detailed synthesis in the second stage, integrating spatial information through RoPE (Rotary Position Embedding).

Experimental Results

The paper presents experiments demonstrating high-quality reconstruction and generation performance. It shows that UltraShape 1.0 can achieve comparable quality to commercial systems with limited training data.

Conclusion

UltraShape 1.0 is a scalable deep learning framework for generating high-quality 3D assets, addressing limitations in existing methods through innovative two-stage joining strategies and comprehensive data processing techniques. The experimental results indicate superior performance compared to both open-source and commercial approaches, even with restricted training resources.

📄 논문 본문 발췌 (Excerpt)

📸 추가 이미지 갤러리

Reference