93 citations · 286 across the 44 of their papers we have counts for
22 papers · 1 filter
EP-CFG: Energy-Preserving Classifier-Free Guidance
Kai Zhang, Fujun Luan, Sai Bi +1
Classifier-free guidance (CFG) is widely used in diffusion models but often introduces over-contrast and over-saturation artifacts at higher guidance strengths. We present EP-CFG (…
Turbo3D: Ultra-fast Text-to-3D Generation
Hanzhe Hu, Tianwei Yin, Fujun Luan +6
We present Turbo3D, an ultra-fast text-to-3D system capable of generating high-quality Gaussian splatting assets in under one second. Turbo3D employs a rapid 4-step, 4-view diffusi…
MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data
Hanwen Jiang, Zexiang Xu, Desai Xie +11
We propose scaling up 3D scene reconstruction by training with synthesized data. At the core of our work is MegaSynth, a procedurally generated 3D dataset comprising 700K scenes -…
MaterialPicker: Multi-Modal DiT-Based Material Generation
Xiaohe Ma, Valentin Deschaintre, Miloš Hašan +4
High-quality material generation is key for virtual environment authoring and inverse rendering. We propose MaterialPicker, a multi-modal material generator leveraging a Diffusion…
RandAR: Decoder-only Autoregressive Visual Generation in Random Orders
Ziqi Pang, Tianyuan Zhang, Fujun Luan +5
We introduce RandAR, a decoder-only visual autoregressive (AR) model capable of generating images in arbitrary token orders. Unlike previous decoder-only AR models that rely on a p…
Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors
Zhengfei Kuang, Tianyuan Zhang, Kai Zhang +7
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 vid…
Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction
Yuanhao Cai, He Zhang, Kai Zhang +12
Existing feedforward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt…
KFC-W: Generating 3D-Consistent Videos from Unposed Internet Photos
Gene Chou, Kai Zhang, Sai Bi +5
We address the problem of generating videos from unposed internet photos. A handful of input images serve as keyframes, and our model interpolates between them to simulate a path m…
RelitLRM: Generative Relightable Radiance for Large Reconstruction Models
Tianyuan Zhang, Zhengfei Kuang, Haian Jin +9
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) pos…
Neural Product Importance Sampling via Warp Composition
Joey Litalien, Miloš Hašan, Fujun Luan +2
Achieving high efficiency in modern photorealistic rendering hinges on using Monte Carlo sampling distributions that closely approximate the illumination integral estimated for eve…
LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias
Haian Jin, Hanwen Jiang, Hao Tan +6
We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce two a…
Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splats
Chen Ziwen, Hao Tan, Kai Zhang +5
We propose Long-LRM, a feed-forward 3D Gaussian reconstruction model for instant, high-resolution, 360° wide-coverage, scene-level reconstruction. Specifically, it takes in 32 inpu…
RNG: Relightable Neural Gaussians
Jiahui Fan, Fujun Luan, Jian Yang +2
3D Gaussian Splatting (3DGS) has shown impressive results for the novel view synthesis task, where lighting is assumed to be fixed. However, creating relightable 3D assets, especia…
PBIR-NIE: Glossy Object Capture under Non-Distant Lighting
Guangyan Cai, Fujun Luan, Miloš Hašan +5
Glossy objects present a significant challenge for 3D reconstruction from multi-view input images under natural lighting. In this paper, we introduce PBIR-NIE, an inverse rendering…
Perm: A Parametric Representation for Multi-Style 3D Hair Modeling
Chengan He, Xin Sun, Zhixin Shu +8
We present Perm, a learned parametric representation of human 3D hair designed to facilitate various hair-related applications. Unlike previous work that jointly models the global…
Uncertainty for SVBRDF Acquisition using Frequency Analysis
Ruben Wiersma, Julien Philip, Miloš Hašan +4
This paper aims to quantify uncertainty for SVBRDF acquisition in multi-view captures. Under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the o…
Neural Gaffer: Relighting Any Object via Diffusion
Haian Jin, Yuan Li, Fujun Luan +6
Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting. Many prior methods either support only…
Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling
Liwen Wu, Sai Bi, Zexiang Xu +5
Novel-view synthesis of specular objects like shiny metals or glossy paints remains a significant challenge. Not only the glossy appearance but also global illumination effects, in…
RGBX: Image decomposition and synthesis using material- and lighting-aware diffusion models
Zheng Zeng, Valentin Deschaintre, Iliyan Georgiev +5
The three areas of realistic forward rendering, per-pixel inverse rendering, and generative image synthesis may seem like separate and unrelated sub-fields of graphics and vision.…
Rendering Participating Media Using Path Graphs
Becky Hu, Xi Deng, Fujun Luan +2
Rendering volumetric scattering media, including clouds, fog, smoke, and other complex materials, is crucial for realism in computer graphics. Traditional path tracing, while unbia…
MeshLRM: Large Reconstruction Model for High-Quality Meshes
Xinyue Wei, Kai Zhang, Sai Bi +6
We propose MeshLRM, a novel LRM-based approach that can reconstruct a high-quality mesh from merely four input images in less than one second. Different from previous large reconst…
DATENeRF: Depth-Aware Text-based Editing of NeRFs
Sara Rojas, Julien Philip, Kai Zhang +4
Recent advancements in diffusion models have shown remarkable proficiency in editing 2D images based on text prompts. However, extending these techniques to edit scenes in Neural R…