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...
Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and lack the ambient motion that makes environments immersive.
arXiv:2606.20083v4 Announce Type: replace Abstract: Video world models are moving toward preserving an observed world under controllable camera and object motion while allowing its environmental stat...
arXiv:2505. 08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes.
arXiv:2603. 18634v3 Announce Type: replace-cross Abstract: Rapid, large-scale 3D reconstruction from multi-date satellite imagery is vital for environmental monitoring, urban planning, and disaster response, yet remains difficult due to illumination changes, sensor heterogeneity, and the cost of per-scene optimization.
WildRelight is the first in-the-wild dataset designed to evaluate single-image relighting models, featuring high-resolution outdoor scenes captured under strictly aligned, temporally varying natural illuminations paired with high-dynamic-range environment maps. The benchmark demonstrates that state-of-the-art models trained on synthetic data suffer severe domain shifts when applied to real-world imagery. Leveraging the dataset’s temporal structure, the authors introduce a physics-guided inference framework combining Diffusion Posterior Sampling with Temporal Sampling-Aware Test-Time Adaptation, enabling synthetic models to self-supervise and align with real-world statistics on-the-fly.
arXiv:2606. 10620v1 Announce Type: cross Abstract: Image generation models now produce high-quality static images, yet their ability to represent how a visual world changes over time remains poorly understood.
WilLaGS introduces a unified framework that enhances 3D Gaussian Splatting for in-the-wild scenes by learning a continuous global appearance manifold with a β‑VAE and generating dynamic Tri‑Plane features for spatially‑varying local illumination. It also incorporates a self‑supervised perceptual masking mechanism using a Teacher‑Student EMA architecture to suppress transient artifacts and identify inconsistent regions. Experiments on multiple datasets show that WilLaGS achieves state‑of‑the‑art reconstruction quality and novel view synthesis while preserving real‑time rendering efficiency.
The paper introduces 4DGS-WAM, an object‑centric world action model that uses a 4D Gaussian Splatting representation to separate dynamic objects from a static background. By predicting future actions of dynamic actors and their Gaussian splat transformations, the model can reuse previously observed static content for future state generation, reducing redundant background processing. Experiments on the KITTI‑MOT dataset demonstrate the model’s ability to perform short‑horizon prediction and past reconstruction.
arXiv:2608.30184v1 Announce Type: new Abstract: Volumetric video enables immersive free viewpoint rendering of dynamic real world scenes, yet existing methods struggle with long sequences and complex...
arXiv:2609.15032v1 Announce Type: new Abstract: Live free-viewpoint visualization of real humans is critical for immersive communication and interactive digital experiences. Existing methods either r...
arXiv:2609.13262v1 Announce Type: new Abstract: 3D Gaussian Splatting achieves photorealistic reconstruction within training view distribution, yet it degrades on out-of-distribution novel views, exh...