EG4D is a novel framework for generating 4D objects without score distillation. The method explicitly generates multi-view videos from a single input image to create high-quality and consistent 4D assets. It addresses challenges such as temporal inconsistency, inter-frame geometry and texture diversity, and semantic defects in video generation. The framework includes collaborative techniques like attention injection, a robust dynamic reconstruction method based on Gaussian Splatting, and a refinement stage with diffusion prior for semantic restoration. Qualitative results and user preference studies show that EG4D outperforms baselines in generation quality. The framework uses two video diffusion models: Stable Video Diffusion (SVD) and SV3D. SVD generates video frames, while SV3D generates multi-view images. The framework then uses 4D Gaussian Splatting for reconstruction and diffusion refinement for semantic restoration. The method achieves realistic 3D appearance, high image fidelity, and fine temporal consistency. The framework is evaluated on three cases and shows superior performance compared to existing methods. The framework is also tested on ablation studies, showing effective solutions to challenges in reconstructing 4D representation with synthesized videos. The framework is expected to have broader impact in generating dynamic 3D objects with high quality and consistency.EG4D is a novel framework for generating 4D objects without score distillation. The method explicitly generates multi-view videos from a single input image to create high-quality and consistent 4D assets. It addresses challenges such as temporal inconsistency, inter-frame geometry and texture diversity, and semantic defects in video generation. The framework includes collaborative techniques like attention injection, a robust dynamic reconstruction method based on Gaussian Splatting, and a refinement stage with diffusion prior for semantic restoration. Qualitative results and user preference studies show that EG4D outperforms baselines in generation quality. The framework uses two video diffusion models: Stable Video Diffusion (SVD) and SV3D. SVD generates video frames, while SV3D generates multi-view images. The framework then uses 4D Gaussian Splatting for reconstruction and diffusion refinement for semantic restoration. The method achieves realistic 3D appearance, high image fidelity, and fine temporal consistency. The framework is evaluated on three cases and shows superior performance compared to existing methods. The framework is also tested on ablation studies, showing effective solutions to challenges in reconstructing 4D representation with synthesized videos. The framework is expected to have broader impact in generating dynamic 3D objects with high quality and consistency.