arXiv AI By Youngyoon Choi, Kihyun Kim, Jeongwoo Shin, Joonseok Lee

Diverse Motion Customization via Control-based Dynamic Optimization

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arXiv AI
Oct 2

Video Generation Models: A Survey of Post-Training and Alignment

arXiv:2610.00812v1 Announce Type: cross Abstract: Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamic...

By Chaoyu Li, Xiaoyi Gu, Yogesh Kulkarni, Eun Woo Im, Mohammadmahdi Honarmand, Zeyu Wang, Juntong Song, Fei Du, Xilin Jiang, Kexin Zheng, Tianzhi Li, Fei Tao, Pooyan Fazli
arXiv Computer Vision
Sep 30

Reimagine Video Dynamics

arXiv:2609.36496v1 Announce Type: new Abstract: Most video editing methods focus on changing the appearance of the source video, while offering limited control over its dynamics. We introduce Reimagi...

By Yu Yuan, Yawen Lu, Guoxian Song, Kevin Duarte, Ratheesh Kalarot, Di Chang, Xijun Wang, Stanley H. Chan
arXiv Computer Vision
Oct 22

HorizonComposer: Spatiotemporally Consistent Driving Video Editing with Enriched Traffic Semantics

HorizonComposer is a new instruction‑guided video editing framework that preserves traffic semantics in driving videos. It uses a hybrid dataset of real and synthetic edits, applies advantage conditioning with dense pixel‑level rewards during training, and introduces thinking frames to reduce temporal shock. Experiments show state‑of‑the‑art spatiotemporal consistency, improved content preservation, and gains in downstream road line segmentation and multi‑object detection.

By Mauricio Soroco, Yuqiu Liu, Zaid Tasneem, Francesco Pittaluga, Abhishek Aich, Wuyang Chen, Manmohan Chandraker, Ziyu Jiang
arXiv Computer Vision
Sep 25

ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation

ViRDM is a new post‑training method for few‑step causal video generation that eliminates the need for a large teacher model and an online critic. By applying representation distribution matching (RDM) with a precomputed target distribution, a lightweight VAE decoder, and staged vector–Jacobian products, ViRDM overcomes memory, optimization, and temporal dynamics challenges. The approach reduces GPU memory usage and training time, achieving state‑of‑the‑art VBench performance with only 20 generator updates and 16 A100 GPU‑hours.

By Zichong Meng, Chongjian Ge, Chun-Hao P. Huang, Yang Zhou, Huaizu Jiang
arXiv AI
Jul 7

Motion Attribution for Video Generation

arXiv:2601. 08828v2 Announce Type: replace-cross Abstract: Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood.

By Xindi Wu, Despoina Paschalidou, Jun Gao, Antonio Torralba, Laura Leal-Taix\'e, Olga Russakovsky, Sanja Fidler, Jonathan Lorraine