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Found 27 papers, 9 papers with code
Textured Gaussians for Enhanced 3D Scene Appearance Modeling
As such, each Gaussian can represent a richer set of texture patterns and geometric structures, instead of just a single color and ellipsoid as in naive Gaussian Splatting.
Planar Reflection-Aware Neural Radiance Fields
Neural Radiance Fields (NeRF) have demonstrated exceptional capabilities in reconstructing complex scenes with high fidelity.
Modeling Ambient Scene Dynamics for Free-view Synthesis
We introduce a novel method for dynamic free-view synthesis of an ambient scenes from a monocular capture bringing a immersive quality to the viewing experience.
Taming Latent Diffusion Model for Neural Radiance Field Inpainting
These two problems are further reinforced with the use of pixel-distance losses.
IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images
While numerous 3D reconstruction and novel-view synthesis methods allow for photorealistic rendering of a scene from multi-view images easily captured with consumer cameras, they bake illumination in their representations and fall short of supporting advanced applications like material editing, relighting, and virtual object insertion.
TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion
In contrast, TextureDreamer can transfer highly detailed, intricate textures from real-world environments to arbitrary objects with only a few casually captured images, potentially significantly democratizing texture creation.
LTM: Lightweight Textured Mesh Extraction and Refinement of Large Unbounded Scenes for Efficient Storage and Real-time Rendering
We validate the effectiveness of the proposed method on large unbounded scenes from mip-NeRF 360 Tanks & Temples and Deep Blending datasets achieving at-par rendering quality with 73x reduced triangles and 11x reduction in memory footprint.
SpecNeRF: Gaussian Directional Encoding for Specular Reflections
We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields, which helps decompose appearance into more physically meaningful components.
Single-Image 3D Human Digitization with Shape-Guided Diffusion
We present an approach to generate a 360-degree view of a person with a consistent, high-resolution appearance from a single input image.
VR-NeRF: High-Fidelity Virtualized Walkable Spaces
We present an end-to-end system for the high-fidelity capture, model reconstruction, and real-time rendering of walkable spaces in virtual reality using neural radiance fields.
OmnimatteRF: Robust Omnimatte with 3D Background Modeling
Video matting has broad applications, from adding interesting effects to casually captured movies to assisting video production professionals.
Consistent View Synthesis with Pose-Guided Diffusion Models
In this work, we propose a pose-guided diffusion model to generate a consistent long-term video of novel views from a single image.
Progressively Optimized Local Radiance Fields for Robust View Synthesis
For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner.
HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling
Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques.
Ranked #1 on
Novel View Synthesis
on DONeRF: Evaluation Dataset
Robust Dynamic Radiance Fields
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene.
AMICO: Amodal Instance Composition
Image composition aims to blend multiple objects to form a harmonized image.
Boosting View Synthesis With Residual Transfer
We present a simple but effective technique to boost the rendering quality, which can be easily integrated with most view synthesis methods.
Learning Neural Light Fields With Ray-Space Embedding
Our method supports rendering with a single network evaluation per pixel for small baseline light fields and with only a few evaluations per pixel for light fields with larger baselines.
Learning Neural Light Fields with Ray-Space Embedding Networks
Our method supports rendering with a single network evaluation per pixel for small baseline light field datasets and can also be applied to larger baselines with only a few evaluations per pixel.
TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e. g., NeRF).
Neural 3D Video Synthesis from Multi-view Video
We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation.
Space-time Neural Irradiance Fields for Free-Viewpoint Video
We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video.
Speech2Face: Learning the Face Behind a Voice
How much can we infer about a person's looks from the way they speak?
A Dataset of Flash and Ambient Illumination Pairs from the Crowd
We present a dataset of thousands of ambient and flash illumination pairs to enable studying flash photography and other applications that can benefit from having separate illuminations.
On Learning Associations of Faces and Voices
We computationally model the overlapping information between faces and voices and show that the learned cross-modal representation contains enough information to identify matching faces and voices with performance similar to that of humans.
Learning-based Video Motion Magnification
We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods.
Video Reflection Removal Through Spatio-Temporal Optimization
The novelty of our work is in our optimization formulation as well as the motion initialization strategy.
