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Found 4 papers, 4 papers with code
G-NeRF: Geometry-enhanced Novel View Synthesis from Single-View Images
Novel view synthesis aims to generate new view images of a given view image collection.
HiLo: Detailed and Robust 3D Clothed Human Reconstruction with High-and Low-Frequency Information of Parametric Models
We empirically find that the high-frequency (HF) and low-frequency (LF) information from a parametric model has the potential to enhance geometry details and improve robustness to noise, respectively.
Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit Assignment
In this paper, we propose to evaluate text-to-image generation performance by directly estimating the likelihood of the generated images using a pre-trained likelihood-based text-to-image generative model, i. e., a higher likelihood indicates better perceptual quality and better text-image alignment.
Cross-Ray Neural Radiance Fields for Novel-view Synthesis from Unconstrained Image Collections
To mimic the perception process of humans, in this paper, we propose Cross-Ray NeRF (CR-NeRF) that leverages interactive information across multiple rays to synthesize occlusion-free novel views with the same appearances as the images.
