arXiv:2308.12560
공식 코드 · Papers with Code
NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects
원문 초록
We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene. Compared to prior work, our framework significantly reduces blending artifacts when inserting multiple dynamic objects into a 3D scene at novel views and times; achieves comparable PSNR without the need for additional ground truth modalities like optical flow; and overall provides ease, flexibility, and scalability in neural composition. Our codebase is on GitHub.
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MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation
WAFT: Warping-Alone Field Transforms for Optical Flow
Multimodal Fusion SLAM with Fourier Attention
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised Training
MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution
Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing
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