OmniShowcase introduces a method for generating omnidirectional videos of a specific subject from a handful of multi‑view images. It combines an automated data‑synthesis pipeline that creates training videos with diverse camera trajectories driven by the subject’s geometry, and a geometry‑conditioning technique that supplies the model with surface‑normal maps reconstructed from the reference views. The authors release Showcase‑4K, a large corpus of 3D‑asset‑based videos with 12 camera motions and 88 trajectory configurations, and ShowcaseBench, a held‑out evaluation set with source meshes and camera parameters.
arXiv:2609.14462v1 Announce Type: new Abstract: Interactive video world models must maintain broad scene context under camera motion while producing high-fidelity observations with low latency. Exist...
arXiv:2609.13504v1 Announce Type: new Abstract: Recent developments in feed-forward 3D reconstruction resulted in models which can recover dense scene representations and camera motion solely from an...
arXiv:2610.02160v1 Announce Type: new Abstract: Precise control over camera and object motion is essential for professional video production. Existing methods control objects only coarsely, through i...
Generative Cinematographer (GenCine) lifts a single image into an editable 3D scene scaffold, allowing artists to jointly author camera paths and foreground motion using local 3D motion handles. The system projects these controls into guidance maps that encode handle positions and colors across frames, enabling a pretrained video model to follow camera-relative, piecewise-rigid motion without physics simulation. Training recovers controls from real videos and synthetic ground-truth geometry, and experiments demonstrate consistent camera-relative motion, improved geometric consistency, and strong controllability across diverse real-world scenes.
arXiv:2610.28632v1 Announce Type: cross Abstract: Video generation models can now depict remarkably realistic motion. Yet it is unclear how to use this motion beyond pixels: to animate a specific 3D...
WorldSculpt presents a method for generating compositional 3D representations of cluttered scenes with hundreds of objects by adapting a single-object 3D generative prior to multi-view observations. The approach, built on Pixal3D with a multi-view conditioning pathway, can generalize to highly occluded scenes without scene-level training. The authors also introduce the UE-MeshyScene benchmark and demonstrate that their method outperforms prior approaches across various evaluation settings, including converting existing 3DGS worlds into compositional mesh scenes.
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...
MUGEN is a large‑scale real‑world dataset of over 1,300 hours of 4K panoramic videos with rich semantic and geometric annotations, designed to support interactive 360° world exploration. Wan360 is a camera‑controllable panoramic video generation model built on MUGEN, featuring ERP‑aware components (periodic longitude RoPE, ERP‑aware padding, random roll yaw) and a panoramic Plücker embedding for camera motion. Together, they address gaps in data and model support for immersive, temporally coherent 360° video generation along user‑specified camera trajectories.
arXiv:2607. 00832v1 Announce Type: cross Abstract: A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration.
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:2610.33135v1 Announce Type: new Abstract: Precise camera control is essential for cinematic character video generation, yet existing methods struggle to follow large-angle trajectories with com...
arXiv:2610.08684v1 Announce Type: new Abstract: Modern video models can generate realistic videos from real appearance references and proxy renders that specify scene structure, viewpoint changes, an...