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Found 11 papers, 1 papers with code
DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation
To provide the content creator with fine-grained control over the lighting during image generation, we augment the text-prompt with detailed lighting information in the form of radiance hints, i. e., visualizations of the scene geometry with a homogeneous canonical material under the target lighting.
Gaussian Splashing: Dynamic Fluid Synthesis with Gaussian Splatting
We demonstrate the feasibility of integrating physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructed using 3DGS.
A Real-time Method for Inserting Virtual Objects into Neural Radiance Fields
We present the first real-time method for inserting a rigid virtual object into a neural radiance field, which produces realistic lighting and shadowing effects, as well as allows interactive manipulation of the object.
Relighting Neural Radiance Fields with Shadow and Highlight Hints
This paper presents a novel neural implicit radiance representation for free viewpoint relighting from a small set of unstructured photographs of an object lit by a moving point light source different from the view position.
Learning Photometric Feature Transform for Free-form Object Scan
We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are fed to a multi-view stereo method to enhance 3D reconstruction.
A Unified Spatial-Angular Structured Light for Single-View Acquisition of Shape and Reflectance
We propose a unified structured light, consisting of an LED array and an LCD mask, for high-quality acquisition of both shape and reflectance from a single view.
Efficient Reflectance Capture with a Deep Gated Mixture-of-Experts
A common, pre-trained latent transform module is also appended to each decoder, to offset the burden of the increased number of decoders.
DiFT: Differentiable Differential Feature Transform for Multi-View Stereo
We present a novel framework to automatically learn to transform the differential cues from a stack of images densely captured with a rotational motion into spatially discriminative and view-invariant per-pixel features at each view.
Learning Implicit Body Representations from Double Diffusion Based Neural Radiance Fields
In this paper, we present a novel double diffusion based neural radiance field, dubbed DD-NeRF, to reconstruct human body geometry and render the human body appearance in novel views from a sparse set of images.
Learning Efficient Photometric Feature Transform for Multi-view Stereo
We present a novel framework to learn to convert the perpixel photometric information at each view into spatially distinctive and view-invariant low-level features, which can be plugged into existing multi-view stereo pipeline for enhanced 3D reconstruction.
Intrinsic Light Field Images
We present a method to automatically decompose a light field into its intrinsic shading and albedo components.
