4D-GSW is a kinematic-aware watermarking framework for 4D Gaussian Splatting that embeds robust copyright information while preserving high spatio-temporal consistency. It introduces a Spatio-Temporal Curvature metric to identify dynamic instants and gates watermark gradient injection, and uses a joint HMM-MRF energy minimization model to synchronize watermark phases across temporal trajectories and spatial neighborhoods. An anisotropic gradient routing mechanism ensures watermark embedding remains decoupled from photometric reconstruction fidelity, achieving superior robustness and rendering quality in experiments.
By Sifan Zhou, Hang Zhang, Yuhang Wang, Ming Li
The paper introduces the first systematic robustness benchmark for local invisible image watermarking, evaluating five methods across 55 image transformations that include signal distortions, coordinate misalignments, indirect local edits, and direct watermark edits. Results show that all methods are vulnerable to some transformation, with MaskWM achieving the best payload recovery and localization but at the cost of image quality. The study highlights that robustness varies strongly with transformation type, especially noting that geometric misalignment and generative local edits can completely disrupt payload recovery.
By Kai Yao, Bence Szil\'agyi, Sebesty\'en Kamp, M\'at\'e Po\'or, M\'at\'e Szilveszter, Matyas K. Zsoldos, Marc Juarez
arXiv:2610.23856v1 Announce Type: new
Abstract: Generated videos often lose geometric consistency under camera motion: structures warp under viewpoint change, parallax becomes inconsistent, and smoot...
By Weijie Lyu, Tiancheng Shen, Jason Li, Yujing Wang, Xiaoyuan Wang, Haobo Yuan, Yizhou Zhao, Ming-Hsuan Yang
COVER is a new video watermarking method that targets codec compression as its primary design goal. It embeds the watermark payload in the latent space of a frozen generative video autoencoder and recovers it by re‑encoding the received video into the same latent space. Using a differentiable codec surrogate bank, COVER achieves high bit accuracy across multiple codecs while keeping marked videos visually close to the originals.
By Yuxin Cao, Hao Yang, Ziqi Ding, Jie Hao, Wei Song
ContextAnyone is a context‑aware diffusion framework that treats a reference image as an explicitly preserved appearance anchor rather than a simple conditioning signal. By jointly reconstructing the reference image and generating the target video within a shared diffusion transformer, it provides direct supervision for maintaining identity and fine‑grained appearance throughout denoising. The method introduces asymmetric information flow and Gap‑RoPE positional representations to keep the reference stable while allowing selective access by video tokens, and demonstrates improved identity and appearance consistency on an OpenVid‑HD benchmark.
By Ziyang Mai, Yu-Wing Tai
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:2606. 19676v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success in image and video generation and editing.
By Haengbok Chung
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
CausalAlign is a new framework for diffusion‑based video generation models that tackles the problem of drifting camera ego‑motion over long horizons. It first diagnoses how ego‑motion is represented and changes during denoising, revealing a rise‑and‑fall pattern. Then it strengthens ego‑motion by aligning internal representations to a 3D foundation model in a delta‑token space that captures inter‑frame changes, improving motion accuracy, geometric consistency, and temporal smoothness while preserving visual quality.
By Kai Zhu, Yintianrun Zhang, Shuwei Jiang, Ruolin Zuo, Yixin Chen, Duomin Wang, Miao Liu, Yin Li, Daquan Zhou, Yiwu Zhong
arXiv:2609.25775v1 Announce Type: new
Abstract: Recent advances in generative video models have enabled the synthesis of visually realistic content, posing significant challenges to synthetic video d...
By Huangsen Cao, Hongkang chu, Siyao Yu, Xin Ding, Jianfeng Dong, Yongwei Wang
arXiv:2608.23549v1 Announce Type: new
Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
By Khiem Vuong, Deva Ramanan, Srinivasa Narasimhan
arXiv:2608. 05728v1 Announce Type: cross Abstract: Reference-based event-to-video reconstruction aims to recover target RGB frames from a reference frame and the event stream captured over the reference-to-target interval.
By Feiyu Ji, Xiang Li, Hao Ma, Tianxiang Huang, Qingxin Lu, Mengqi Ji, Lei Han, Xiaokang Yang, Xiaoyun Yuan