arXiv Computer Vision
PhysMirror: Physics-Aware Mirror Object Generation
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arXiv Computer Vision
Sep 22
PhysReflect: Geometry and Perception Guided Diffusion for Physically-Plausible Mirror Reflections
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 Computer Vision
Sep 4
Fill My Mirror: Geometry-Constrained Mirror Inpainting
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 Computer Vision
Sep 1
OptiGeo: Efficient Monocular Geometry for Embodied Perception in Optically Challenging Scenes
arXiv:2608.29881v1 Announce Type: new Abstract: Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective...
arXiv Computer Vision
Sep 4
PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models
PoseDreamer is a new pipeline that uses diffusion models to generate large‑scale synthetic datasets for 3D human mesh estimation, providing 3D mesh annotations that remain aligned with the generated images. The system incorporates controllable image generation, Direct Preference Optimization for control alignment, curriculum‑based hard sample mining, and multi‑stage quality filtering to produce over 500,000 high‑quality samples with a 76% improvement in image‑quality metrics over traditional rendering‑based datasets. Models trained on PoseDreamer match or surpass those trained on real‑world or conventional synthetic data, and combining PoseDreamer with synthetic datasets yields better performance than mixing real and synthetic data alone.
arXiv AI
Sep 7
Reflection-aware Generative Novel View Synthesis
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.
arXiv Machine Learning
Aug 10
MirrorWorld: Taming Video Diffusion Models for Mirror Reflection Generation
arXiv:2608. 07463v1 Announce Type: cross Abstract: Recent advances in video diffusion models (VDMs) have enabled high-fidelity video synthesis.