4D generation synthesizes dynamic 3D scenes from conditions such as text or images. Existing methods either reconstruct generated RGB videos with a separate 4D model or adapt a particular video generator to predict geometry directly.
arXiv:2610.28257v1 Announce Type: new Abstract: This work presents SplatFlux, a feed-forward latent-to-4DGS framework for both video-based 4D reconstruction and conditional 4D generation. Our key obs...
arXiv:2610.28631v1 Announce Type: new Abstract: Current world models for explicit world simulation typically follow two distinct pathways: (1) video generative models (e.g., Sora), which simulate rea...
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.
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.
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.
arXiv:2607. 01202v1 Announce Type: cross Abstract: We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos.
MATE4D is a framework that transforms a single image into editable dynamic 4D content. It builds a spatio-temporal multi-view image matrix with text-guided background manipulation, then optimizes 3D Gaussian primitives and animates them with a lightweight deformation module. The approach yields scenes with more faithful geometry, smoother temporal behavior, and consistent background edits, outperforming baselines on Objaverse-XL and Diffusion4D.
arXiv:2601.15951v2 Announce Type: replace Abstract: Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle t...
UniDynamics is a diffusion-based framework that generates future 4D dynamic scenes—comprising RGB, depth, and optical flow—from a single event-RGB pair, without needing long histories or control priors. It introduces an Event Latent Enhancement module to align event data into conditioning features and a Perceptual Dynamics Space within a multi-scale U‑Net to decouple and adaptively interact depth and flow, enforcing geometric and motion constraints for coherent predictions. Experiments on VKitti2 and DSEC show state‑of‑the‑art performance, producing high‑quality, temporally coherent, and 4D‑consistent predictions even under high‑speed motion blur.
arXiv:2610.17625v1 Announce Type: cross Abstract: For video generation in dynamic world modeling, observed static and dynamic content should be efficiently reused to provide conditions for repeated g...
The paper introduces 4DGS-Fixer, an iterative refinement framework that uses a video diffusion model to enhance sparse-view 4D Gaussian Splatting for dynamic scene synthesis. It first fuses multi-view depth maps into dense point clouds for better geometric initialization, then applies a pretrained video restoration model to refine rendered sequences, providing pseudo-supervision for further refinement. Experiments on a benchmark dataset show the method outperforms existing baselines, achieving nearly a 2 dB PSNR improvement.