GroundControl introduces a satellite-conditioned multi‑view diffusion model for novel view synthesis that leverages both satellite imagery and street‑level inputs. The approach uses cross‑attention conditioning to preserve pretrained diffusion priors while aligning generated views with the scene layout seen from satellite images, avoiding unrealistic hallucinations. Experiments on outdoor datasets demonstrate superior performance over baselines, particularly when target views have minimal overlap with source views.
By Akshay Krishnan, Matias Turkulainen, Jamie Wynn, Zawar Qureshi, Mohamed Sayed, Guillermo Garcia-Hernando, Sara Vicente, James Hays, Michael Firman, Daniyar Turmukhambetov
arXiv:2609.09890v1 Announce Type: new
Abstract: Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple o...
By Qi Zhang, Yanyifan Wang, Weiyuan Zhang, Hui Huang
Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple outdoor environments (e.g., fields, courtyards). H...
MeSS (Mesh-based Scene Synthesis) is a method that generates high‑quality, style‑consistent outdoor scenes using city mesh models as geometric priors. The pipeline first creates geometrically consistent sparse views with Cascaded Outpainting ControlNets, then densifies intermediate views via AGInpaint, and finally removes visual inconsistencies with GCAlign. Simultaneously, a 3D Gaussian Splatting scene is reconstructed from the mesh, enabling diverse rendering styles after synthesis.
By Xuyang Chen, Zhijun Zhai, Kaixuan Zhou, Zengmao Wang, Jianan He, Dong Wang, Yanfeng Zhang, Mingwei Sun, Xuqin Wang, R\"udiger Westermann, Tao Wu, Konrad Schindler, Liqiu Meng
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.
By Basile Van Hoorick, Dian Chen, Shun Iwase, Pavel Tokmakov, Muhammad Zubair Irshad, Igor Vasiljevic, Swati Gupta, Fangzhou Cheng, Sergey Zakharov, Vitor Campagnolo Guizilini
The paper introduces LEGO, a lifting‑free method for generating egocentric videos from a single exocentric recording. Instead of reconstructing depth and point clouds, it trains a transformer‑based view synthesizer that directly predicts the egocentric view, providing a probabilistic correspondence map that balances structural alignment with texture averaging. This approach outperforms existing explicit pipelines and generalizes across datasets without retraining.
By Suhwan Cho, Yonwoo Choi, Soongjin Kim, Jicheol Park, Taegyu Lim