arXiv:2608. 07463v1 Announce Type: cross Abstract: Recent advances in video diffusion models (VDMs) have enabled high-fidelity video synthesis.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
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.
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...
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...
arXiv:2608. 11562v1 Announce Type: cross Abstract: Videos captured through glass often contain reflections that degrade visual quality and interfere with downstream vision tasks.
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...