ScalingGaussian: Enhancing 3D Content Creation with Generative Gaussian Splatting
The creation of high-quality 3D assets is critical in areas such as digital heritage, entertainment, and robotics, traditionally relying on professionals and specialized software. As demand for 3D resources in gaming and virtual reality (VR) increases, 3D image generation technologies are progressively reducing the dependence on specialized skills. However, current methods often struggle to achieve both fine texture detail and geometric consistency. To address this, we propose the ScalingGaussian framework, which combines 3D and 2D diffusion models, optimized through the introduction of Gaussian noise and Score Distillation Sampling (SDS) loss. This approach enhances the geometric stability and texture fidelity of 3D assets.
The Role of Deductive and Inductive Reasoning in Large Language Models
Large language models (LLMs) have made significant progress in AI, excelling in reasoning tasks. However, their reliance on static prompts limits their adaptability to complex, dynamic problems. To address this, we propose the Deductive and Inductive (DID) method, combining inductive reasoning for extracting general rules and deductive reasoning for applying them flexibly. Inspired by cognitive science, DID dynamically adjusts reasoning paths based on task context. Empirical tests on multiple datasets, including the challenging Holiday Puzzle, show DID significantly improves accuracy and reasoning quality without adding computational burden. This method offers a robust framework for advanced problem-solving in LLMs.