arXiv:2605.27891v1 Announce Type: cross Abstract: The narrative quality of a video fundamentally determines its perceptual value. Although existing video generation methods can produce visually appealing content, they predominantly rely on sparse conditioning signals such as text…
SmartDirector enhances video generation by using multiple keyframes to improve narrative structure and temporal pacing through a two-stage process of low-resolution generation and high-resolution refinement.
MotiMotion introduces a reasoning-then-generation framework for motion-controlled video generation that improves plausibility through vision-language reasoning and confidence-aware control mechanisms.
arXiv cs.CV
TIER_1English(EN)·Yipeng Chen, Zhichao Ye, Zhenzhou Fang, Xinyu Chen, Xiaoyu Zhang, Jialing Liu, Nan Wang, Guofeng Zhang, Haomin Liu·
arXiv:2511.17185v2 Announce Type: replace Abstract: We propose PostCam, a streamlined framework for novel-view video generation that achieves superior detail preservation and precise camera trajectory editing in dynamic scenes. Current methods often struggle with a trade-off betw…
arXiv cs.CV
TIER_1English(EN)·Luis Denninger, Sina Mokhtarzadeh Azar, Juergen Gall·
arXiv:2605.22996v1 Announce Type: new Abstract: We present CoMoGen, a controllable video generation framework that generates realistic interactive dynamics from a single binary mask sequence conditioned on an input image. CoMoGen introduces a lightweight MaskAdapter that encodes …
arXiv cs.CV
TIER_1English(EN)·Jiarong Liang, Max Ku, Ka-Hei Hui, Ping Nie, Wenhu Chen·
arXiv:2602.13294v3 Announce Type: replace Abstract: Evaluating whether Multimodal Large Language Models (MLLMs) genuinely reason about physical dynamics remains challenging. Most existing benchmarks rely on recognition-style protocols such as Visual Question Answering (VQA) and V…
arXiv cs.CV
TIER_1English(EN)·Ziqi Huang, Ning Yu, Gordon Chen, Haonan Qiu, Paul Debevec, Ziwei Liu·
arXiv:2510.05094v2 Announce Type: replace Abstract: Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transiti…
arXiv:2605.22818v1 Announce Type: new Abstract: Current motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often yields unnatural or implausible outcomes, especially…
Current motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often yields unnatural or implausible outcomes, especially by missing secondary causal consequences. To ad…
Recent diffusion models achieve strong photorealism and fluency in video generation, yet remain fragile under abstract, sparse or complex conditions, leading to poor performance in professional production workflows such as storyboard sketches and clay render conditions. Existing …
