Text-to-3D Gaussian Splatting with Physics-Grounded Motion Generation

• URL: http://arxiv.org/abs/2412.05560v1
• Date: Sat, 07 Dec 2024 06:48:16 GMT
• Title: Text-to-3D Gaussian Splatting with Physics-Grounded Motion Generation
• Authors: Wenqing Wang, Yun Fu,
• Abstract summary: We present an innovative framework that generates 3D models with accurate appearances and geometric structures. By integrating text-to-3D generation with physics-grounded motion synthesis, our framework renders photo-realistic 3D objects.
• Score: 47.6666060652434
• License:
• Abstract: Text-to-3D generation is a valuable technology in virtual reality and digital content creation. While recent works have pushed the boundaries of text-to-3D generation, producing high-fidelity 3D objects with inefficient prompts and simulating their physics-grounded motion accurately still remain unsolved challenges. To address these challenges, we present an innovative framework that utilizes the Large Language Model (LLM)-refined prompts and diffusion priors-guided Gaussian Splatting (GS) for generating 3D models with accurate appearances and geometric structures. We also incorporate a continuum mechanics-based deformation map and color regularization to synthesize vivid physics-grounded motion for the generated 3D Gaussians, adhering to the conservation of mass and momentum. By integrating text-to-3D generation with physics-grounded motion synthesis, our framework renders photo-realistic 3D objects that exhibit physics-aware motion, accurately reflecting the behaviors of the objects under various forces and constraints across different materials. Extensive experiments demonstrate that our approach achieves high-quality 3D generations with realistic physics-grounded motion.

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