Dream4D is a new framework for generating spatiotemporally coherent 4D content. It uses a two‑stage pipeline: first, few‑shot learning predicts optimal camera trajectories from a single image; second, a pose‑conditioned diffusion process creates geometrically consistent multi‑view sequences that are converted into a persistent 4D representation. The method uniquely combines rich temporal priors from video diffusion models with geometric awareness from reconstruction models, achieving higher quality metrics such as mPSNR and mSSIM compared to existing approaches.
By Xiaoyan Liu, Kangrui Li, Jiaxin Liu, Yuehao Song, Yujie Xing
WorldForge V2 introduces a training‑free guidance framework that refines video diffusion outputs along a target camera path using warped references and optical‑flow‑identified latent channels for selective guidance. It employs a dual‑path strategy to correct artifacts, a v‑cache to reduce refinement cost, and Trajectory LoRA adapters trained in two stages to focus on trajectory‑responsive network blocks. The approach achieves state‑of‑the‑art generation quality and trajectory accuracy with minimal training data, enabling reliable camera control across unseen scenes.
By Chenxi Song, Yanming Yang, Tong Zhao, Ruibo Li, Joey Tianyi Zhou, Chi Zhang
arXiv:2510.24904v2 Announce Type: replace
Abstract: Although recent video generative models are getting more capable of following external camera controls, imposed by either text descriptions or came...
By Qiucheng Wu, Handong Zhao, Zhixin Shu, Jing Shi, Yang Zhang, Shiyu Chang
Stream4D is a training‑free, motion‑decoupled streaming 4D reconstruction framework that can be integrated into causal Transformer backbones such as StreamVGGT and Stream3R. It exploits the causal attention’s KV cache to aggregate temporal motion cues, fuses them with depth priors, and produces precise dynamic masks that separate static and dynamic regions. A soft key masking scheme then suppresses dynamic interference in attention, improving camera pose estimation and reducing ghosting in long sequences.
By Wei Zhang, Ying Feng, Mengtong Wu, Shiqiang Gong, Huaqi Zhang, Qi Zhang
arXiv:2609.39504v1 Announce Type: cross
Abstract: We present PartiCam, a training-free Particle filtering rooted method for improved Camera controlled video generation. Generating videos that follow...
By Amine Ouasfi, Runjia Li, Junlin Han, Eric Marchand, Philip H. S. Torr, Adnane Boukhayma
VideoTok4D introduces a 4D‑aware video tokenizer that transforms videos into compact tokens representing a dynamic 3D world. It employs spatiotemporal disentanglement to separate static and dynamic content, a track‑aware dynamic attention mechanism for motion consistency across views, and a diffusion prior (Co4DGen) for efficient 4D scene generation. Experiments show state‑of‑the‑art results with up to four orders of magnitude less storage than dense 4D representations, and shorter diffusion sequences for faster generation.
By Xinyi Chen, Hanxin Zhu, Xijun Wang, Xingrui Wang, Sen Liang, Xin Li, Zhibo Chen