Zian Wang

papers

1,072

total citations

papers (12)

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

NEURIPS 2022arXiv

564

citations

Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes

CVPR 2023arXiv

113

citations

Learning Indoor Inverse Rendering With 3D Spatially-Varying Lighting

ICCV 2021arXiv

105

citations

Neural LiDAR Fields for Novel View Synthesis

ICCV 2023arXiv

citations

DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer

NEURIPS 2021arXiv

citations

Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion

ECCV 2022arXiv

citations

Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models

CVPR 2025

citations

Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid

ECCV 2020arXiv

citations

Zian Wang

papers (12)

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes

Learning Indoor Inverse Rendering With 3D Spatially-Varying Lighting

Neural LiDAR Fields for Novel View Synthesis

DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer

Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion

Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models

Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid

Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering

Misalignment-Robust Frequency Distribution Loss for Image Transformation

LuxDiT: Lighting Estimation with Video Diffusion Transformer

Controllable Weather Synthesis and Removal with Video Diffusion Models

papers (12)

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes

Learning Indoor Inverse Rendering With 3D Spatially-Varying Lighting

Neural LiDAR Fields for Novel View Synthesis

DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer

Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion

Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models

Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid

Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering

Misalignment-Robust Frequency Distribution Loss for Image Transformation

LuxDiT: Lighting Estimation with Video Diffusion Transformer

Controllable Weather Synthesis and Removal with Video Diffusion Models