arXiv Computer Vision
2d ago

No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos

NoPo4D is a feed‑forward system that reconstructs dynamic 3D scenes from unposed multi‑view videos by using 4D Gaussian splatting. It introduces a velocity decomposition that separates per‑pixel image‑plane shifts from depth changes, enabling supervision with pseudo optical flow and avoiding the need for accurate camera poses or 3D motion ground truth. The method also employs a bidirectional motion encoder and view‑dependent opacity to improve cross‑view and cross‑timestep alignment, achieving state‑of‑the‑art results on multiple benchmarks while being much faster than per‑scene optimization approaches.

By Matteo Balice, Yanik Kunzi, Chenyangguang Zhang, Matteo Matteucci, Marc Pollefeys, Sunghwan Hong
arXiv Computer Vision
Oct 16

Gen4P: Video Generators are 4D Perceivers

Gen4P is a unified framework that transforms a pretrained video generator into a feed‑forward 4D perceiver, enabling recovery of 4D geometry and motion from monocular video without iterative denoising. It processes video latents in a single encoder pass and uses a lightweight cross‑attention decoder to predict requested 4D attributes, supporting flexible spatial sampling and continuous‑time queries. By integrating a frozen video‑depth LoRA as a geometry prior, Gen4P improves performance on 3D point tracking, video depth, and camera pose estimation, achieving state‑of‑the‑art or competitive results on benchmark tasks and demonstrating robust tracking of complex motion in in‑the‑wild videos.

By Yitong Xia, Zeren Jiang, Chuanxia Zheng
Hugging Face Trending Papers
Oct 15

Gen4P: Video Generators are 4D Perceivers

Gen4P is a unified framework that transforms a pretrained video generator into a feed‑forward 4D perceiver, enabling recovery of 4D geometry and motion from monocular video without iterative denoising. It processes clean video latents in a single encoder pass and uses a lightweight cross‑attention decoder to predict requested 4D attributes, supporting flexible spatial sampling and continuous‑time queries for temporal flexibility. By incorporating a frozen pretrained video‑depth LoRA as a geometry prior, Gen4P improves performance on 3D point tracking, video depth, and camera pose estimation, achieving state‑of‑the‑art or competitive results on these tasks and demonstrating robust tracking of complex motion in in‑the‑wild videos.