Search Results for author:
Found 22 papers, 4 papers with code
MatSwap: Light-aware material transfers in images
As a result, our method seamlessly integrates a desired material into the target location in the photograph while retaining the identity of the scene.
MaterialPicker: Multi-Modal Material Generation with Diffusion Transformers
High-quality material generation is key for virtual environment authoring and inverse rendering.
SAMa: Material-aware 3D Selection and Segmentation
Our approach works on arbitrary 3D representations and outperforms several strong baselines in terms of selection accuracy and multiview consistency.
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.
TexSliders: Diffusion-Based Texture Editing in CLIP Space
Generative models have enabled intuitive image creation and manipulation using natural language.
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.
MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment
We present MatAtlas, a method for consistent text-guided 3D model texturing.
MatSynth: A Modern PBR Materials Dataset
We introduce MatSynth, a dataset of 4, 000+ CC0 ultra-high resolution PBR materials.
ControlMat: A Controlled Generative Approach to Material Capture
Our generative approach further permits exploration of a variety of materials which could correspond to the input image, mitigating the unknown lighting conditions.
The Visual Language of Fabrics
We introduce text2fabric, a novel dataset that links free-text descriptions to various fabric materials.
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.
Materialistic: Selecting Similar Materials in Images
We present a method capable of selecting the regions of a photograph exhibiting the same material as an artist-chosen area.
PhotoMat: A Material Generator Learned from Single Flash Photos
Instead, we train a generator for a neural material representation that is rendered with a learned relighting module to create arbitrarily lit RGB images; these are compared against real photos using a discriminator.
Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training
NeRF acquisition typically requires careful choice of near planes for the different cameras or suffers from background collapse, creating floating artifacts on the edges of the captured scene.
TileGen: Tileable, Controllable Material Generation and Capture
The resulting materials are tileable, can be larger than the target image, and are editable by varying the condition.
Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis
We present a novel U-Attention vision Transformer for universal texture synthesis.
Deep Polarization Imaging for 3D shape and SVBRDF Acquisition
We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues.
Generative Modelling of BRDF Textures from Flash Images
We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance.
Guided Fine-Tuning for Large-Scale Material Transfer
Our solution is extremely simple: we fine-tune a deep appearance-capture network on the provided exemplars, such that it learns to extract similar SVBRDF values from the target image.
Flexible SVBRDF Capture with a Multi-Image Deep Network
Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph.
Graphics I.3
Single-Image SVBRDF Capture with a Rendering-Aware Deep Network
Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures.
Graphics I.3
