Hugging Face Trending Papers
A Dual-Transformer for Multi-Camera View Recommendation
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arXiv Computer Vision
Aug 27
A Dual-Transformer for Multi-Camera View Recommendation
The paper introduces a Dual-Transformer architecture with Cross-Attention for multi-camera view recommendation, achieving a 56.60% Precision@0.5 on the TVMCE dataset, surpassing the previous best of 37.16%. The model separates temporal encoding of past frames from candidate view querying, and an ablation study shows the SwinV2 backbone yields 69.65% Precision@0.5. Fine‑tuning with as little as 20% of a target video improves precision, suggesting efficient personalization for specific editing styles.
arXiv Computer Vision
Oct 19
OmniXRef: A Unified Framework for Multimodal Multi-Reference Video Editing
arXiv:2610.22744v1 Announce Type: new Abstract: Creative intent in video editing can be expressed through text instructions (e.g., "remove the people"), image references (e.g., subject appearance), v...
arXiv Machine Learning
Aug 27
Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing
Memory-V2V is a memory‑augmented video‑to‑video diffusion framework designed to improve cross‑turn consistency in multi‑turn video editing. It stores previous outputs in an external memory, retrieves relevant edits, and incorporates them via relevance‑aware tokenization and adaptive compression, allowing scalable conditioning without linear computational growth. Experiments on iterative video novel view synthesis and text‑guided long video editing show that Memory‑V2V enhances consistency while preserving visual quality and outperforming strong baselines with modest overhead.
Hugging Face Trending Papers
Jul 22
Look Before You Edit: Attention-Guided Camera Placement and Multi-View Alignment for 3D Gaussian Splatting Editing
Text-driven 3D scene editing with 3D Gaussian Splatting (3DGS) typically applies a 2D diffusion editor to views rendered from fixed training cameras, limiting both the spatial coverage of edits and the user's freedom to target specific objects in complex scenes. We present LB-Edit, a framework that addresses two coupled problems: where to place editing cameras for localized edits, and how to make per-view edits agree with one another so that the 3D scene remains consistent after fine-tuning.
arXiv AI
Aug 6
ZoomV: Temporal Zoom-in for Efficient Long Video Understanding
arXiv:2504. 01407v3 Announce Type: replace-cross Abstract: Long video understanding poses a fundamental challenge for large video-language models (LVLMs) due to the overwhelming number of frames and the risk of losing essential context through naive downsampling.
arXiv Computer Vision
Sep 22
VRWKV-Editor: Reducing quadratic complexity in transformer-based video editing
arXiv:2509.25998v4 Announce Type: replace Abstract: In light of recent progress in video editing, deep learning models focusing on both spatial and temporal dependencies have emerged as the primary m...