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Fixing Diffusion Models’ Limited Understanding of Mirrors and Reflections
- Generative AI's rise has spurred interest in developing AI models that understand and replicate physical laws, with a focus on simulating gravity and liquid dynamics.
- Latent diffusion models (LDMs) have become dominant in generative AI, sparking research into their limitations in comprehending and reproducing physical phenomena.
- Issue of accurate reflections in LDMs has gained attention, with research focusing on improving their understanding of mirror reflections.
- Challenges with reflections are prominent in industries like CGI and video gaming, where ray-tracing algorithms are used to simulate realistic reflections and shadows.
- Efforts have been made to improve reflection capabilities in models like Neural Radiance Fields (NeRF) and Gaussian Splatting, with various projects tackling the reflection problem.
- Diffusion models face difficulty in incorporating accurate reflections, requiring extensive and varied training data sets to embed reflection-related rules reliably.
- Recent MirrorVerse project from India aims to enhance diffusion models' reflection capabilities through an improved dataset and training method.
- The MirrorVerse project introduces MirrorFusion 2.0, a diffusion-based generative model focusing on improving photorealism and geometric accuracy in mirror reflections.
- MirrorGen2 dataset, curated for MirrorVerse, enhances mirror reflection training data through diverse 3D objects, scene constructions, and synthetic data generation methods.
- A three-stage curriculum learning process was designed for training MirrorFusion 2.0, incorporating synthetic and real-world data sets to enhance reflection quality and generalization.
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