arXiv AI
Jul 3
NeoMap: Training-free Novel-View Synthesis from Single Images and Videos
arXiv:2607. 01962v1 Announce Type: cross Abstract: We study the challenging problem of novel view video synthesis from single images or monocular videos.
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.
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arXiv Computer Vision
Sep 3
RoGe: Novel View Synthesis via End-to-End Implicit Reconstruction and Generation
RoGe is a new end‑to‑end framework for novel view synthesis that jointly learns an implicit 3D scene representation and a video diffusion model. It eliminates the need for explicit 3D intermediates by querying the implicit scene with camera rays to produce geometric features that condition the diffusion model. Experiments on DL3DV show that RoGe surpasses reconstruction‑based, generation‑based, and hybrid baselines in image quality and temporal consistency, and ablations confirm the benefits of ray‑queried features and joint training.
Hugging Face Trending Papers
Aug 24
FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces artifacts when input views are sparse or target v...
arXiv Computer Vision
Aug 25
FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
arXiv:2608.23549v1 Announce Type: new Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
arXiv Computer Vision
Aug 31
Video Generative Models as Geometry Learner
The paper introduces GeoNeXt, a framework that repurposes pretrained video generative models for geometry estimation by framing it as a next‑frame prediction task. Unlike prior methods that either train separate depth/normal models or fine‑tune image diffusion backbones, GeoNeXt jointly models images and geometric targets, leveraging the structured knowledge of video models for more data‑efficient learning. Experiments show zero‑shot monocular depth and surface normal estimation that outperforms existing generative approaches and rivals discriminative state‑of‑the‑art methods while using far less training data.
arXiv Computer Vision
Sep 21
4DGS-Fixer: Generative Sparse-View 4D Gaussian Splatting with Iterative Refinement Guided by Video Diffusion Priors
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.
arXiv Computer Vision
4d ago
GeoVerse: World-Consistent Novel View Synthesis in Geometric Latent Space
arXiv:2609.35734v2 Announce Type: replace Abstract: Novel view synthesis from sparse images must reconcile faithful reconstruction of observed regions with plausible completion of unseen content, whi...
arXiv Computer Vision
Aug 31
Manifold4D: Denoising on Point Cloud Rendered Manifolds for Video Re-shooting
Manifold4D introduces a new denoising strategy for video re‑shooting that injects a rendered point‑cloud directly into the initial noise manifold, eliminating the need for the render to be an explicit conditioning stream during denoising. This approach allows the network to rely solely on the source video as a visual condition, improving camera‑control accuracy on the DAVIS‑Traj benchmark and Vista4D set, with significant reductions in rotation and translation errors while maintaining video fidelity. User studies confirm enhanced trajectory following and dynamic consistency, especially for large yaw amplitudes and even when the render is corrupted.
Hugging Face Trending Papers
Jun 24
PRISM: Feed-Forward Single-Image 3D Reconstruction via Geometric Warp-Residual Modeling
Reconstructing 3D scenes from a single image is a fundamental challenge in computer vision, with broad applications in virtual reality, robotics, and content creation. Recent methods achieve outstanding performance by leveraging camera-controlled video diffusion models, but rely on iterative diffusion sampling, which greatly limits their practical deployment.
arXiv Machine Learning
Sep 14
AnyView: Synthesizing Any Novel View in Dynamic Scenes
AnyView is a diffusion-based video generation framework designed for dynamic view synthesis, requiring minimal inductive biases or geometric assumptions. It trains a generalist spatiotemporal implicit representation using diverse data sources—monocular, multi-view static, and multi-view dynamic—to produce zero-shot novel videos from arbitrary camera locations and trajectories. The authors evaluate AnyView on standard benchmarks, introduce a new challenging benchmark called AnyViewBench for extreme dynamic view synthesis, and demonstrate that AnyView outperforms baselines in maintaining realistic, plausible, and spatiotemporally consistent videos across diverse real-world scenarios.
arXiv Computer Vision
Sep 1
Dynamic-Robust Photometric-Semantic Reconstruction for Open-Vocabulary 3D Scene Understanding
Dynamic-Robust Photometric-Semantic Reconstruction for Open-Vocabulary 3D Scene Understanding introduces SPAR, a joint semantic‑geometric encoding architecture that isolates transient dynamic noise before latent space aggregation. The method couples motion estimation with multi‑view visual and semantic learning in a dynamic‑region‑aware end‑to‑end training paradigm, enabling the network to resolve motion conflicts and produce temporally stable scene representations. Experiments on the D‑RE10K benchmark show state‑of‑the‑art performance, achieving high PSNR values for novel view synthesis and an 88.5% mIoU for motion mask prediction in a self‑supervised setting.
arXiv Computer Vision
Sep 24
Learn2Splat: Extending the Horizon of Learned 3DGS Optimization
arXiv:2605.15760v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) optimization is most commonly performed using general-purpose first-order optimizers such as Adam or SGD. Although rob...