arXiv Computer Vision By Xi Ye, Yuzhu Wang, Xiaoyang Liu, Jiayi Wang, Yangyang Xu, Ruyu Wang, Wenlin Chen, Duo Su, Jun Zhu

SymRegFlow: Symmetry-Regularized Flow Matching for Video World Models

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SymRegFlow is a symmetry‑regularized flow‑matching framework that enables multi‑view‑consistent video generation across continuously varying camera poses without requiring ground‑truth novel‑view RGB supervision. The method geometrically warps source views into noisy anchors and uses masked dual‑anchor supervision combined with cross‑anchor denoising‑output consistency to reduce anchor‑specific errors. Experiments on Cosmos‑Drive‑Dreams and nuScenes show that SymRegFlow achieves superior video quality, achieving the lowest FVD and FVMD scores and improving FID and instance preservation compared to existing baselines.

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 Computer Vision.

arXiv Computer Vision
1d ago

PointVGGT: Zero-Shot Multiview RGB-D Point Cloud Registration with Visual Geometry Foundation Priors

The paper introduces PointVGGT, a zero‑shot framework for multiview RGB‑D point cloud registration that replaces the traditional pairwise‑then‑global pipeline. It employs a foundation‑then‑refinement paradigm, using visual geometry foundation models to directly recover metrically consistent global poses and then refining them with voxelized spatial hashing and IRLS‑based bundle adjustment. Experiments on indoor, object‑centric, and outdoor datasets demonstrate superior registration accuracy and computational efficiency without any training.

By Haobo Jiang, Liang Yu, Jianmin Zheng
Hugging Face Trending Papers
Jun 24

MVTrack4Gen: Multi-View Point Tracking as Geometric Supervision for 4D Video Generation

Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelity with respect to the reference video. Existing methods based on explicit 3D representations are limited by the accuracy of off-the-shelf reconstruction modules, which often produce inaccurate geometry for dynamic objects in monocular videos.

Hugging Face Trending Papers
Jul 2

NeoMap: Training-free Novel-View Synthesis from Single Images and Videos

We study the challenging problem of novel view video synthesis from single images or monocular videos. Existing methods, which operate under the assumption that pre-trained video models lack native novel view synthesis capability and enforce view alignment via camera conditioning, task-specific fine-tuning, or stepwise hard denoising guidance, often suffer from artifacts and compromised global scene consistency.