11 citations · 32 across the 15 of their papers we have counts for
15 papers
BlenderGym: Benchmarking Foundational Model Systems for Graphics Editing
Yunqi Gu, Ian Huang, Jihyeon Je +2
3D graphics editing is crucial in applications like movie production and game design, yet it remains a time-consuming process that demands highly specialized domain expertise. Auto…
Neural Control Variates with Automatic Integration
Zilu Li, Guandao Yang, Qingqing Zhao +4
This paper presents a method to leverage arbitrary neural network architecture for control variates. Control variates are crucial in reducing the variance of Monte Carlo integratio…
Robust Symmetry Detection via Riemannian Langevin Dynamics
Jihyeon Je, Jiayi Liu, Guandao Yang +5
Symmetries are ubiquitous across all kinds of objects, whether in nature or in man-made creations. While these symmetries may seem intuitive to the human eye, detecting them with a…
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
Sara Sabour, Lily Goli, George Kopanas +6
3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However…
Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusion
Boyang Deng, Richard Tucker, Zhengqi Li +3
We present a method for generating Streetscapes-long sequences of views through an on-the-fly synthesized city-scale scene. Our generation is conditioned by language input (e.g., c…
InfoGaussian: Structure-Aware Dynamic Gaussians through Lightweight Information Shaping
Yunchao Zhang, Guandao Yang, Leonidas Guibas +1
3D Gaussians, as a low-level scene representation, typically involve thousands to millions of Gaussians. This makes it difficult to control the scene in ways that reflect the under…
Probing the 3D Awareness of Visual Foundation Models
Mohamed El Banani, Amit Raj, Kevis-Kokitsi Maninis +7
Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their train…
PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations
Yang Zheng, Qingqing Zhao, Guandao Yang +8
Modeling and rendering photorealistic avatars is of crucial importance in many applications. Existing methods that build a 3D avatar from visual observations, however, struggle to…
BlenderAlchemy: Editing 3D Graphics with Vision-Language Models
Ian Huang, Guandao Yang, Leonidas Guibas
Graphics design is important for various applications, including movie production and game design. To create a high-quality scene, designers usually need to spend hours in software…
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
Francis Engelmann, Ayca Takmaz, Jonas Schult +25
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023. The goal of this wo…
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
Boyuan Chen, Zhuo Xu, Sean Kirmani +6
Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While Vision Language Models (VLM) have demons…
GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation
Tong Wu, Guandao Yang, Zhibing Li +5
Despite recent advances in text-to-3D generative methods, there is a notable absence of reliable evaluation metrics. Existing metrics usually focus on a single criterion each, such…
4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Sherwin Bahmani, Ivan Skorokhodov, Victor Rong +7
Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes. However, current text-to-4D methods face a t…
Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models
Wen-Hsuan Chu, Adam W. Harley, Pavel Tokmakov +3
Object tracking is central to robot perception and scene understanding. Tracking-by-detection has long been a dominant paradigm for object tracking of specific object categories. R…
Support-Set Context Matters for Bongard Problems
Nikhil Raghuraman, Adam W. Harley, Leonidas Guibas
Current machine learning methods struggle to solve Bongard problems, which are a type of IQ test that requires deriving an abstract "concept" from a set of positive and negative "s…