Title: DiffuPhyGS: Text-to-Video Generation with 3D Gaussians and Learnable Physical Properties via Diffusion Priors
Primary area: generative models
Keywords: Text-to-Video, Gaussian Splatting, Diffusion Model, Dynamic 3D Generation, LLM

Abstract: Generating realistic 3D object videos is crucial for virtual reality and digital content creation. However, existing methods often struggle to achieve high-quality appearance and physics-aware motion, relying on manual inputs and pre-existing models. To address these challenges, we propose DiffuPhyGS, a novel framework that generates high-quality 3D objects with realistic and learnable physical motion directly from text prompts. Our approach features an LLM-Chain-of-Thought-based Iterative Prompt Refinement (LLM-CoT-IPR) method, which obtains prompt-aligned 2D and multi-view 3D diffusion priors to guide Gaussian Splatting (GS) to generate 3D objects. We further enhance 3D generation quality with a Densification-by-Adaptive-Splitting (DAS) mechanism. Next, we employ a material property decoder that utilizes a Mixture-of-Experts Material Constitutive Models (MoEMCMs) to predict the mixed material properties of the 3D object. We then apply the Material Point Method (MPM) to deform 3D Gaussian kernels, ensuring physics-grounded motion guided by implicit and explicit physical priors from the video diffusion model and a velocity loss function. Extensive experiments show DiffuPhyGS outperforms other methods in generating realistic physics-grounded motion across diverse materials.