The paper introduces Ref-GeNVS, a training‑free, reflection‑aware approach for generative novel view synthesis in mirror scenes. It treats a mirror image as two complementary views, estimates the mirror plane and reflected camera poses, and uses a two‑stage generation process with Mirror‑gated attention and Reflection injection to produce reflection‑consistent novel views. The method leverages a multi‑view diffusion backbone without finetuning, outperforming recent generative NVS methods on synthetic and real mirror scenes.
By GeonU Kim, Shin Dong-Yeon, Tae-Hyun Oh
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
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:2607. 00850v1 Announce Type: cross Abstract: Most self-supervised learning (SSL) methods encourage invariance across augmentations, but strict flip invariance can suppress informative left--right correspondences in approximately bilateral data such as medical images and human faces.
By Ruixin Li, Jin Liu, Yuling Shi, Stefano Lodi
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
CrossDepth introduces geometry-constrained attention for multi-view surround depth estimation, addressing cross-image inconsistencies caused by varying camera intrinsics and limited receptive fields. The method conditions features on per-pixel camera-aware ray embeddings and extends pixel context via cross-image attention limited to geometrically plausible regions. Trained self-supervised with photometric consistency, it achieves better depth accuracy and consistency on DDAD and nuScenes compared to existing self-supervised approaches.
By Samer Abualhanud, Max Mehltretter