GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping
GenWarp is a framework for generating novel views from a single image using a semantic-preserving generative model. It combines diffusion techniques with monocular depth estimation, outperforming existing methods in evaluations.
Read original article
The paper "GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping" presents a novel framework for generating new views from a single image, addressing challenges in 3D scene complexity and dataset limitations. The authors, affiliated with Sony AI and Korea University, propose a generative model that utilizes a diffusion approach to perform geometric warping based on monocular depth estimation (MDE). This method improves upon existing techniques by allowing the model to learn where to warp and where to generate, thus preserving semantic details and reducing artifacts. The framework combines cross-view attention with self-attention, enabling the model to focus on occluded or poorly warped areas while reliably warping other regions. The model can generate 3-4 novel views from a single input image, which can then be used in fast 3D scene reconstruction processes. Qualitative and quantitative evaluations indicate that GenWarp outperforms current methods in both in-domain and out-of-domain scenarios, showcasing its effectiveness in generating plausible novel views.
- GenWarp enables novel view generation from a single image using a semantic-preserving approach.
- The model combines diffusion techniques with monocular depth estimation for improved geometric warping.
- It effectively addresses limitations of existing methods by focusing on both reliable and problematic regions during generation.
- The framework allows for quick 3D scene reconstruction from generated views.
- Evaluations show superior performance compared to current state-of-the-art methods.
Related
WildGaussians: 3D Gaussian Splatting in the Wild
A novel method, WildGaussians, enhances 3D scene reconstruction for in-the-wild data by combining DINO features and appearance modeling with 3D Gaussian Splatting. It outperforms NeRFs and 3DGS in handling dynamic scenes.
PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
PhysGen is a novel method for generating realistic videos from a single image using physical simulation and data-driven techniques, developed by researchers from the University of Illinois and Apple.
The Path to StyleGan2 – Implementing the Progressive Growing GAN
The article outlines the implementation of Progressive Growing GAN (PGGAN) for generating high-resolution images, focusing on a two-phase approach and key concepts to enhance training efficiency and image quality.
DifuzCam: Replacing Camera Lens with a Mask and a Diffusion Model
The paper introduces DifuzCam, a lensless camera design using a diffusion model for image reconstruction, enhancing quality and fidelity, with potential applications in various imaging systems.
Splatt3R: Zero-Shot Gaussian Splatting from Uncalibrated Image Pairs
Splatt3R is a feed-forward model for 3D reconstruction and novel view synthesis from stereo images, achieving real-time rendering and strong generalization to uncalibrated images without requiring depth information.
0 commentsRelated
WildGaussians: 3D Gaussian Splatting in the Wild
A novel method, WildGaussians, enhances 3D scene reconstruction for in-the-wild data by combining DINO features and appearance modeling with 3D Gaussian Splatting. It outperforms NeRFs and 3DGS in handling dynamic scenes.
PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
PhysGen is a novel method for generating realistic videos from a single image using physical simulation and data-driven techniques, developed by researchers from the University of Illinois and Apple.
The Path to StyleGan2 – Implementing the Progressive Growing GAN
The article outlines the implementation of Progressive Growing GAN (PGGAN) for generating high-resolution images, focusing on a two-phase approach and key concepts to enhance training efficiency and image quality.
DifuzCam: Replacing Camera Lens with a Mask and a Diffusion Model
The paper introduces DifuzCam, a lensless camera design using a diffusion model for image reconstruction, enhancing quality and fidelity, with potential applications in various imaging systems.
Splatt3R: Zero-Shot Gaussian Splatting from Uncalibrated Image Pairs
Splatt3R is a feed-forward model for 3D reconstruction and novel view synthesis from stereo images, achieving real-time rendering and strong generalization to uncalibrated images without requiring depth information.