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Found 39 papers, 6 papers with code
MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data
We propose scaling up 3D scene reconstruction by training with synthesized data.
EP-CFG: Energy-Preserving Classifier-Free Guidance
Classifier-free guidance (CFG) is widely used in diffusion models but often introduces over-contrast and over-saturation artifacts at higher guidance strengths.
Turbo3D: Ultra-fast Text-to-3D Generation
By shifting the Gaussian reconstructor's inputs from pixel space to latent space, we eliminate the extra image decoding time and halve the transformer sequence length for maximum efficiency.
MaterialPicker: Multi-Modal Material Generation with Diffusion Transformers
High-quality material generation is key for virtual environment authoring and inverse rendering.
RandAR: Decoder-only Autoregressive Visual Generation in Random Orders
We introduce RandAR, a decoder-only visual autoregressive (AR) model capable of generating images in arbitrary token orders.
Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors
We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and video--normal training data.
Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction
Existing feedforward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency.
Generating 3D-Consistent Videos from Unposed Internet Photos
We design a self-supervised method that takes advantage of the consistency of videos and variability of multiview internet photos to train a scalable, 3D-aware video model without any 3D annotations such as camera parameters.
Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splats
Unlike previous feed-forward models that are limited to processing 1~4 input images and can only reconstruct a small portion of a large scene, Long-LRM reconstructs the entire scene in a single feed-forward step.
RelitLRM: Generative Relightable Radiance for Large Reconstruction Models
We propose RelitLRM, a Large Reconstruction Model (LRM) for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations from sparse (4-8) posed images captured under unknown static lighting.
RNG: Relightable Neural Gaussians
We propose Relightable Neural Gaussians (RNG), a novel 3DGS-based framework that enables the relighting of objects with both hard surfaces or soft boundaries, while avoiding assumptions on the shading model.
Neural Product Importance Sampling via Warp Composition
The small conditional head warp is represented by a neural spline flow, while the large unconditional tail is discretized per environment map and its evaluation is instant.
PBIR-NIE: Glossy Object Capture under Non-Distant Lighting
Glossy objects present a significant challenge for 3D reconstruction from multi-view input images under natural lighting.
Perm: A Parametric Representation for Multi-Style 3D Hair Modeling
We present Perm, a learned parametric representation of human 3D hair designed to facilitate various hair-related applications.
Fast and Uncertainty-Aware SVBRDF Recovery from Multi-View Capture using Frequency Domain Analysis
Relightable object acquisition is a key challenge in simplifying digital asset creation.
Neural Gaffer: Relighting Any Object via Diffusion
Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting.
Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling
In contrast to previous methods that use encoding functions with only angular input, we additionally cone-trace spatial features to obtain a spatially varying directional encoding, which addresses the challenging interreflection effects.
RGB$\leftrightarrow$X: Image decomposition and synthesis using material- and lighting-aware diffusion models
Our X$\rightarrow$RGB model explores a middle ground between traditional rendering and generative models: we can specify only certain appearance properties that should be followed, and give freedom to the model to hallucinate a plausible version of the rest.
MeshLRM: Large Reconstruction Model for High-Quality Meshes
This allows for end-to-end mesh reconstruction by fine-tuning a pre-trained NeRF LRM with mesh rendering.
DATENeRF: Depth-Aware Text-based Editing of NeRFs
However, extending these techniques to edit scenes in Neural Radiance Fields (NeRF) is complex, as editing individual 2D frames can result in inconsistencies across multiple views.
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction
We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating the relative camera poses in ~1. 3 seconds on a single A100 GPU.
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model
We propose \textbf{DMV3D}, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion.
Controllable Dynamic Appearance for Neural 3D Portraits
The surface normals prediction is guided using 3DMM normals that act as a coarse prior for the normals of the human head, where direct prediction of normals is hard due to rigid and non-rigid deformations induced by head-pose and facial expression changes.
PSDR-Room: Single Photo to Scene using Differentiable Rendering
A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance.
I$^2$-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs
In this work, we present I$^2$-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields (SDFs).
I2-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs
Further, we propose to decompose the neural radiance field into spatially-varying material of the scene as a neural field through surface-based, differentiable Monte Carlo raytracing and emitter semantic segmentations, which enables physically based and photorealistic scene relighting and editing applications.
PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields
Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis.
Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing
Indoor scenes typically exhibit complex, spatially-varying appearance from global illumination, making inverse rendering a challenging ill-posed problem.
ARF: Artistic Radiance Fields
We present a method for transferring the artistic features of an arbitrary style image to a 3D scene.
Differentiable Rendering of Neural SDFs through Reparameterization
Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images
We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines.
PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting
We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images.
Ranked #5 on
Surface Normals Estimation
on Stanford-ORB
Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering
Reconstructing the shape and appearance of real-world objects using measured 2D images has been a long-standing problem in computer vision.
Inverse Transport Networks
We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination.
Deep Painterly Harmonization
Copying an element from a photo and pasting it into a painting is a challenging task.
Graphics
Deep Photo Style Transfer
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
