The paper introduces a method for mirror inpainting that leverages scene geometry to generate realistic reflections. By estimating the geometry of the fixed scene, the approach projects visible content into the mirror region, reducing the need for hallucination. A two‑mask diffusion strategy then refines the mirror area, balancing geometric constraints with learned priors, and the method operates without training on complex real‑world scenes.
By Ofek Basson, Shimon Vainer, Yacov Hel-Or, Ohad Fried
arXiv:2608. 07463v1 Announce Type: cross Abstract: Recent advances in video diffusion models (VDMs) have enabled high-fidelity video synthesis.
By Youjun Zhao, Alex Warren, Gary K. L. Tam, Rynson W. H. Lau
arXiv:2609.23442v1 Announce Type: new
Abstract: Diffusion models generate high-quality images, yet often violate the physical laws governing mirror reflections. Reflections often suffer from geometri...
By Shuheng Ge, Hongwei Ren, Li Zhang, Xiangqian Wu
arXiv:2607.03470v2 Announce Type: replace
Abstract: Synthesizing physically accurate mirror reflections remains a fundamental challenge for modern text-to-image diffusion models, which are increasing...
By Xuan-Bach Mai, Duy-Phuc Nguyen, Quoc-Van Le, Tam V. Nguyen, Thanh-Toan Do, Huu Le, Duong-Van Nguyen, Minh-Triet Tran, Trung-Nghia Le
arXiv:2608. 07559v1 Announce Type: cross Abstract: In 3D reconstruction, mirrors introduce significant challenges by creating distorted and fragmented spaces, resulting in inaccurate and unreliable 3D models.
By Yidan Shen, Yu Wen, Chen Zhang, Xin Fu, Renjie Hu
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.
By Xiaolei Lang, Ze Kang, Zehao Huang, Naiyan Wang
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
The paper introduces CamTrol++, a training‑free method that stabilizes camera‑controlled novel view synthesis from a single image by decomposing large camera motions into small autoregressive steps, thereby limiting per‑step distortion and error accumulation. It also incorporates geometry‑constrained spatial attention, low‑frequency appearance anchoring, and a registration‑free warping pipeline to further enhance stability. Experiments on RealEstate10K and MegaScene demonstrate improved temporal and geometric consistency, better downstream 3D reconstruction quality, and higher generation efficiency, even for long 56‑frame sequences and under depth corruption.
By Prajwal Singh, Arjun Badola, Seema Kumari, Hajime Nagahara, Shanmuganathan Raman
PRISM is a feed‑forward single‑image 3D reconstruction framework that splits the task into a parameter‑free geometric warp and a learned residual correction, eliminating the need for iterative diffusion sampling. It uses a two‑stage training strategy—latent supervised distillation for geometric generalization and perceptual fine‑tuning for appearance quality—to generalize from synthetic data. Experiments on three benchmarks show PRISM matches diffusion‑based methods in quality while cutting inference time to just 36 seconds per scene.
By Zhijie Zheng, Xinhao Xiang, Jiawei Zhang
arXiv:2610.20610v1 Announce Type: new
Abstract: Generating novel views from limited observations requires preserving both the appearance and spatial structure of a scene. Although recent approaches e...
By Jahyeok Koo, Hyeonseo Yu, Seungho Jang, Jaewoo Jung, Jiwon Kang, Seokju Cho, Seungryong Kim
GroundControl introduces a satellite‑conditioned multi‑view diffusion model for novel view synthesis that uses satellite images alongside street‑level inputs. The cross‑attention conditioning preserves pretrained diffusion priors while ensuring realistic spatial layouts, even when target views have little overlap with source views. Experiments on outdoor datasets demonstrate superior performance over both satellite‑only and street‑only baselines without relying on 3D reconstruction.
Spackle is a lightweight residual learning framework designed to improve large-view single-image novel view synthesis (NVS) by mitigating capacity competition in hybrid decoupled systems that combine 3D Gaussian Splatting (3DGS) and diffusion models. It operates in three stages: predicting base 3DGS attributes, automatically identifying poorly reconstructed regions, and learning a residual 3DGS focused on those areas. During inference, Spackle merges the baseline and augmented Gaussians to produce high-fidelity novel views, achieving state‑of‑the‑art performance on large-view-deviation cases.
By Xuanzhi Liu, Yuhe Zhou, Xinyi Wu, Zhenyao Wu, Jinghao Chen, Ruize Han, Song Wang