51 citations · 117 across the 16 of their papers we have counts for
23 papers
Fluid-Gen-Zero: Grounding Pretrained Video Generators in Physics without Training
Hong Huang, Yuqiu Liu, Chenyu You +3
We present Fluid-Gen-Zero, a training-free framework for physics-aware fluid-object interaction video generation that decouples physical reasoning from appearance synthesis. Our ke…
Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs
Yuqun Wu, Yao Xiao, Chuhang Zou +2
Recent works augment Vision-Language Models with geometry features from pretrained 3D models, expecting that the geometric signal will boost spatial reasoning. However, we find tha…
SAM-V: Geometry-Aware Segment Anything for Multi-View Instance Segmentation
Jiangshan Gong, Yuqun Wu, Qiqian Fu +4
Consistent multi-view object segmentation is critical for 3D perception and robotics, yet remains challenging under severe viewpoint and occlusion changes. Existing methods typical…
AssetGen: Deployable 3D Asset Generation at Interactive Speed
Dilin Wang, Xiaoyu Xiang, Kihyuk Sohn +14
While 3D generation is progressing rapidly, recent work has often focused on obtaining high-resolution assets, leaving user experience and deployability as afterthoughts. We presen…
Fast SAM 3D Body: Accelerating SAM 3D Body for Real-Time Full-Body Human Mesh Recovery
Timing Yang, Sicheng He, Hongyi Jing +4
SAM 3D Body (3DB) achieves state-of-the-art accuracy in monocular 3D human mesh recovery, yet its inference latency of several seconds per image precludes real-time application. We…
SceneDiff: A Benchmark and Method for Multiview Object Change Detection
Yuqun Wu, Chih-hao Lin, Henry Che +4
We investigate the problem of identifying objects that have been added, removed, or moved between a pair of captures (images or videos) of the same scene at different times. Accura…
WorldGen: From Text to Traversable and Interactive 3D Worlds
Dilin Wang, Hyunyoung Jung, Tom Monnier +22
We introduce WorldGen, a system that enables the automatic creation of large-scale, interactive 3D worlds directly from text prompts. Our approach transforms natural language descr…
Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time
Jingxuan Xu, Hong Huang, Chuhang Zou +3
We propose a neural physics system for real-time, interactive fluid simulations. Traditional physics-based methods, while accurate, are computationally intensive and suffer from la…
Plenoptic PNG: Real-Time Neural Radiance Fields in 150 KB
Jae Yong Lee, Yuqun Wu, Chuhang Zou +2
The goal of this paper is to encode a 3D scene into an extremely compact representation from 2D images and to enable its transmittance, decoding and rendering in real-time across v…
MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance
Yuqun Wu, Jae Yong Lee, Chuhang Zou +2
The latest regularized Neural Radiance Field (NeRF) approaches produce poor geometry and view extrapolation for large scale sparse view scenes, such as ETH3D. Density-based approac…
QFF: Quantized Fourier Features for Neural Field Representations
Jae Yong Lee, Yuqun Wu, Chuhang Zou +2
Multilayer perceptrons (MLPs) learn high frequencies slowly. Recent approaches encode features in spatial bins to improve speed of learning details, but at the cost of larger model…
Deep PatchMatch MVS with Learned Patch Coplanarity, Geometric Consistency and Adaptive Pixel Sampling
Jae Yong Lee, Chuhang Zou, Derek Hoiem
Recent work in multi-view stereo (MVS) combines learnable photometric scores and regularization with PatchMatch-based optimization to achieve robust pixelwise estimates of depth, n…
Recurrent Transformer Variational Autoencoders for Multi-Action Motion Synthesis
Rania Briq, Chuhang Zou, Leonid Pishchulin +2
We consider the problem of synthesizing multi-action human motion sequences of arbitrary lengths. Existing approaches have mastered motion sequence generation in single action scen…
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility
Jae Yong Lee, Joseph DeGol, Chuhang Zou +1
Recent learning-based multi-view stereo (MVS) methods show excellent performance with dense cameras and small depth ranges. However, non-learning based approaches still outperform…
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention
Donghyun Kim, Tian Lan, Chuhang Zou +5
Prior work in multi-task learning has mainly focused on predictions on a single image. In this work, we present a new approach for multi-task learning from videos via efficient int…
Improving Style Transfer with Calibrated Metrics
Mao-Chuang Yeh, Shuai Tang, Anand Bhattad +2
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image. We seek to understand how to improve style transfer. To d…
Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods
Chuhang Zou, Jheng-Wei Su, Chi-Han Peng +5
Recent approaches for predicting layouts from 360 panoramas produce excellent results. These approaches build on a common framework consisting of three steps: a pre-processing step…
Counterfactual Depth from a Single RGB Image
Theerasit Issaranon, Chuhang Zou, David Forsyth
We describe a method that predicts, from a single RGB image, a depth map that describes the scene when a masked object is removed - we call this "counterfactual depth" that models…
Silhouette Guided Point Cloud Reconstruction beyond Occlusion
Chuhang Zou, Derek Hoiem
One major challenge in 3D reconstruction is to infer the complete shape geometry from partial foreground occlusions. In this paper, we propose a method to reconstruct the complete…
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
Chuhang Zou, Alex Colburn, Qi Shan +1
We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e.g. L-shape r…
Complete 3D Scene Parsing from an RGBD Image
Chuhang Zou, Ruiqi Guo, Zhizhong Li +1
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our…
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
Chuhang Zou, Ersin Yumer, Jimei Yang +2
The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sen…
Predicting Complete 3D Models of Indoor Scenes
Ruiqi Guo, Chuhang Zou, Derek Hoiem
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our…