dynamic scenes
Scenarios in which objects and environments change over time. In AI and computer vision, understanding dynamic scenes is crucial for applications like video analysis and object tracking.
- 4D3R: Motion-Aware Neural Reconstruction and Rendering of Dynamic Scenes from Monocular Videos
- BurstDeflicker: A Benchmark Dataset for Flicker Removal in Dynamic Scenes
- Dynamic Shadow Unveils Invisible Semantics for Video Outpainting
- Enhancing 3D Reconstruction for Dynamic Scenes
- Event-Driven Dynamic Scene Depth Completion
- HAIF-GS: Hierarchical and Induced Flow-Guided Gaussian Splatting for Dynamic Scene
- Learning Efficient Fuse-and-Refine for Feed-Forward 3D Gaussian Splatting
- Orientation-anchored Hyper-Gaussian for 4D Reconstruction from Casual Videos
- RGB-Only Supervised Camera Parameter Optimization in Dynamic Scenes
- Reconstruct, Inpaint, Test-Time Finetune: Dynamic Novel-view Synthesis from Monocular Videos
- SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing
- SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing
- Segment then Splat: Unified 3D Open-Vocabulary Segmentation via Gaussian Splatting
- Track3R: Joint Point Map and Trajectory Prior for Spatiotemporal 3D Understanding
- UltraLED: Learning to See Everything in Ultra-High Dynamic Range Scenes