Scene Splatter: Momentum 3D Scene Generation from Single Image with Video Diffusion Model
Shengjun Zhang, Jinzhao Li, Xin Fei, Hao Liu, Yueqi Duan·April 03, 2025
Summary
Scene Splatter innovates 3D scene generation from single images using video diffusion, overcoming limitations with enhanced detail and consistency. It iteratively recovers high-fidelity, artifact-free views without video length constraints, outperforming existing methods. The text also explores various research papers on 3D scene generation, reconstruction, and representation, covering topics like text-driven generation, denoising diffusion models, and image-to-3D conversion. Techniques include neural radiance fields, novel view synthesis, and deep feature effectiveness, aiming for high-fidelity and diverse outputs.
Introduction
Background
Overview of 3D scene generation challenges
Importance of high-fidelity and artifact-free views
Objective
Highlighting Scene Splatter's approach to overcoming limitations in 3D scene generation
Emphasizing the method's ability to generate high-fidelity, artifact-free views without video length constraints
Method
Video Diffusion for 3D Scene Recovery
Explanation of video diffusion technique
How it enables iterative recovery of high-fidelity views
Data-Driven Iterative Refinement
Description of the iterative process
How it ensures artifact-free and high-fidelity results
Constraint-Free Generation
Discussion on the method's ability to work without video length constraints
Comparison with existing methods
Research on 3D Scene Generation, Reconstruction, and Representation
Text-Driven Generation
Overview of text-driven approaches in 3D scene generation
Case studies and advancements in the field
Denoising Diffusion Models
Explanation of denoising diffusion in 3D scene reconstruction
Benefits and applications of these models
Image-to-3D Conversion Techniques
Overview of methods converting images to 3D scenes
Analysis of Scene Splatter's contribution to this area
Key Techniques and Innovations
Neural Radiance Fields
Description of neural radiance fields in 3D scene representation
Scene Splatter's application and improvements
Novel View Synthesis
Explanation of novel view synthesis in 3D scene generation
Scene Splatter's approach and outcomes
Deep Feature Effectiveness
Overview of deep learning in feature extraction for 3D scenes
Scene Splatter's use of deep features for high-fidelity outputs
High-Fidelity and Diverse Outputs
Discussion on the importance of high-fidelity and diversity in 3D scene generation
Scene Splatter's achievements in this area
Conclusion
Summary of Scene Splatter's Contributions
Future Directions and Potential Applications
Comparison with Other Methods
Impact on the Field of 3D Scene Generation
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
How does Scene Splatter utilize video diffusion to enhance 3D scene generation from single images?
What are the key implementation techniques used in Scene Splatter to achieve high-fidelity and artifact-free views?
What innovative approaches does Scene Splatter introduce in the context of text-driven generation and image-to-3D conversion?
How does Scene Splatter compare with existing methods in terms of handling video length constraints and output consistency?