arXiv subjects

Yunshu Bai

Publications and source records attributed to Yunshu Bai.

3 recordsLinked to original sources

arXiv2026

SemMorph3D: Unsupervised Semantic-Aware 3D Morphing via Mesh-Guided Gaussians

We introduce METHODNAME, a novel framework for semantic-aware 3D shape and texture morphing directly from multi-view images. While 3D Gaussian Splatting (3DGS) enables photorealistic rendering, its unstructured nature often leads to catastrophic geometric fragmentation during morphing. Conversely, traditional mesh-based morphing enforces structural integrity but mandates pristine input topology and struggles with complex appearances. Our method resolves this dichotomy by employing a mesh-guided strategy where a coarse, extracted base mesh acts as a flexible geometric anchor. This anchor provides the necessary topological scaffolding to guide unstructured Gaussians, successfully compensating for mesh extraction artifacts and topological limitations. Furthermore, we propose a novel dual-domain optimization strategy that leverages this hybrid representation to establish unsupervised semantic correspondence, synergizing geodesic regularizations for shape preservation with texture-aware constraints for coherent color evolution. This integrated approach ensures stable, physically plausible transformations without requiring labeled data, specialized 3D assets, or category-specific templates. On the proposed TexMorph benchmark, METHODNAME substantially outperforms prior 2D and 3D methods, yielding fully textured, topologically robust 3D morphing while reducing color consistency error (Delta E) by 22.2% and EI by 26.2%. Project page: https://baiyunshu.github.io/GAUSSIANMORPHING.github.io/

cs.CV↗

arXiv2026

SPRITE: From Static Mockups to Engine-Ready Game UI

Game UI implementation requires translating stylized mockups into interactive engine entities. However, current "Screenshot-to-Code" tools often struggle with the irregular geometries and deep visual hierarchies typical of game interfaces. To bridge this gap, we introduce SPRITE, a pipeline that transforms static screenshots into editable engine assets. By integrating Vision-Language Models (VLMs) with a structured YAML intermediate representation, SPRITE explicitly captures complex container relationships and non-rectangular layouts. We evaluated SPRITE against a curated Game UI benchmark and conducted expert reviews with professional developers to assess reconstruction fidelity and prototyping efficiency. Our findings demonstrate that SPRITE streamlines development by automating tedious coding and resolving complex nesting. By facilitating rapid in-engine iteration, SPRITE effectively blurs the boundaries between artistic design and technical implementation in game development. Project page: https://baiyunshu.github.io/sprite.github.io/

cs.HC↗

arXiv2025

Zero-shot 3D Map Generation with LLM Agents: A Dual-Agent Architecture for Procedural Content Generation

Procedural Content Generation (PCG) offers scalable methods for algorithmically creating complex, customizable worlds. However, controlling these pipelines requires the precise configuration of opaque technical parameters. We propose a training-free architecture that utilizes LLM agents for zero-shot PCG parameter configuration. While Large Language Models (LLMs) promise a natural language interface for PCG tools, off-the-shelf models often fail to bridge the semantic gap between abstract user instructions and strict parameter specifications. Our system pairs an Actor agent with a Critic agent, enabling an iterative workflow where the system autonomously reasons over tool parameters and refines configurations to progressively align with human design preferences. We validate this approach on the generation of various 3D maps, establishing a new benchmark for instruction-following in PCG. Experiments demonstrate that our approach outperforms single-agent baselines, producing diverse and structurally valid environments from natural language descriptions. These results demonstrate that off-the-shelf LLMs can be effectively repurposed as generalized agents for arbitrary PCG tools. By shifting the burden from model training to architectural reasoning, our method offers a scalable framework for mastering complex software without task-specific fine-tuning.

cs.AI↗