arXiv AI By Yeobin Hong, Suhyeon Lee, Hyungjin Chung, Jong Chul Ye

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

Read the original on arXiv AI →

arXiv:2512. 05672v2 Announce Type: replace-cross Abstract: Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Aug 11

MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling

arXiv:2608. 08553v1 Announce Type: cross Abstract: Video super-resolution (VSR) aims to recover high-fidelity high-resolution videos from low-resolution inputs and is central to applications ranging from mobile capture to streaming and archival restoration.

By Rong Fu, Chunlei Meng, Yangchen Zeng, Xiaowen Ma, Yongtai Liu, Wangyu Wu, Shuo Yin, Zijian Zhang, Sicheng Li, Yingrui Ji, Chenhao Wang, Simon Fong
arXiv Computer Vision
Sep 21

4DGS-Fixer: Generative Sparse-View 4D Gaussian Splatting with Iterative Refinement Guided by Video Diffusion Priors

The paper introduces 4DGS-Fixer, an iterative refinement framework that uses a video diffusion model to enhance sparse-view 4D Gaussian Splatting for dynamic scene synthesis. It first fuses multi-view depth maps into dense point clouds for better geometric initialization, then applies a pretrained video restoration model to refine rendered sequences, providing pseudo-supervision for further refinement. Experiments on a benchmark dataset show the method outperforms existing baselines, achieving nearly a 2 dB PSNR improvement.

By Haitao Huang, Shenghao Zhao, Boyuan Tian, Shin-Fang Chng, Songlin Yang, Sheila Lim, Huangying Zhan, Yi Xu, Anyi Rao, Frank Guan
Hugging Face Trending Papers
Aug 13

V-RAE: Rethinking Video Latent Spaces for Generation

Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures have evolved substantially, their latent spaces are still optimized primarily for pixel-level reconstruction and provide limited high-level semantic organization.