Digital zoom transitions between dual cameras often exhibit conspicuous discontinuities in geometric structure and chromatic consistency, degrading the user experience. While recent dual-camera smooth...
ZoomDiff is a high‑fidelity diffusion model designed to improve dual‑camera smooth zooming by producing photo‑realistic transitions. It strengthens dual‑image conditional guidance during denoising, injects flow‑aligned multi‑scale features from the VAE encoder into the decoder to recover high‑frequency details, and uses flow‑guided temporal consistency supervision to ensure smoother transitions. Experiments on synthetic and real‑world datasets show that ZoomDiff outperforms state‑of‑the‑art methods both quantitatively and qualitatively.
arXiv:2611.07081v1 Announce Type: new Abstract: Multi-perspective-camera systems are widely adopted since they provide broad visual coverage for autonomous driving, motivating the synthesis of a unif...
arXiv:2610.25395v2 Announce Type: replace Abstract: Turning a moving-camera perspective video into a 360{\deg} video means generating content outside the input frame under several constraints: observ...
GS‑Net is a lightweight plug‑and‑play module that expands sparse Structure‑from‑Motion point clouds into dense Gaussian primitives, enabling cross‑sensor view synthesis for autonomous driving. It learns a generalizable initialization for 3D Gaussian Splatting, improving rendering quality for both interpolated and extrapolated camera viewpoints. The authors introduce CARLA‑NVS, a benchmark with 12 uniformly spaced cameras, and show that GS‑Net outperforms standard 3DGS by 2.08 dB PSNR on interpolated views and 1.86 dB on extrapolated views while being 50× faster to initialize.
arXiv:2511.21265v2 Announce Type: replace Abstract: Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) en...
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...
arXiv:2607.14935v2 Announce Type: replace Abstract: Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world application...
SpatialCrafter introduces a two‑stage framework for single‑image world modeling that first generates a global 3D proxy using a Point‑anchored Sparse Structure Flow module, then refines appearance with a Generative Deferred Refiner built on a video diffusion model. The method incorporates Parallel Geometry Injection and Proxy‑Aware Corruption training to integrate the proxy without disrupting the pretrained generative manifold, and it is evaluated on a newly constructed dataset of 115K scenes. Experiments demonstrate that SpatialCrafter outperforms existing approaches, reducing long‑term drift and maintaining consistency under rapid camera motion and extreme viewpoints.
Camera intrinsics are vital for recovering 3D structure from 2D video. However, most 3D algorithms assume fixed intrinsics throughout a video, an assumption that often fails for real-world in-the-wild videos.
arXiv:2510.24904v2 Announce Type: replace Abstract: Although recent video generative models are getting more capable of following external camera controls, imposed by either text descriptions or came...
arXiv:2406.00434v4 Announce Type: replace Abstract: In this paper, we propose MoDGS, a new pipeline to render novel views of dy namic scenes from a casually captured monocular video. Previous monocul...