1 citations · 3 across the 12 of their papers we have counts for
16 papers
Complementing DINO Features with Image Structure for Part Discovery
Samyak Rawlekar, Nikhil Chowdary Paleti, Amey Gupta +1
Self-supervised Vision Transformers (ViTs) like DINO encode rich semantics, enabling training-free segmentation of objects from the background and discovery of their parts via feat…
S2Tok: Streaming 3D Gaussian Reconstruction with Persistent Spatial Tokens
Fang Li, Jiraphon Yenphraphai, Quentin Herau +5
Streaming 3D reconstruction requires more than a sequence of geometric predictions: it requires a persistent scene state that can incorporate new evidence and remain renderable as…
World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible
Hao Zhang, Mohamed El Banani, Jen-Hao Cheng +6
Image-to-3D methods often trade off faithfulness and completeness: depth estimators are anchored to input pixels but stop at the visible surface, while image-to-3D models generate…
RigMo: Unifying Rig and Motion Learning for Generative Animation
Hao Zhang, Jiahao Luo, Bohui Wan +7
Despite significant progress in 4D generation, rig and motion, the core structural and dynamic components of animation are typically modeled as separate problems. Existing pipeline…
RGB-Only Supervised Camera Parameter Optimization in Dynamic Scenes
Fang Li, Hao Zhang, Narendra Ahuja
Although COLMAP has long remained the predominant method for camera parameter optimization in static scenes, it is constrained by its lengthy runtime and reliance on ground truth (…
Stable Part Diffusion 4D: Multi-View RGB and Kinematic Parts Video Generation
Hao Zhang, Chun-Han Yao, Simon Donné +2
We present Stable Part Diffusion 4D (SP4D), a framework for generating paired RGB and kinematic part videos from monocular inputs. Unlike conventional part segmentation methods tha…
PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling
Hao Zhang, Haolan Xu, Chun Feng +2
Skinning and rigging are fundamental components in animation, articulated object reconstruction, motion transfer, and 4D generation. Existing approaches predominantly rely on Linea…
Measuring the (Un)Faithfulness of Concept-Based Explanations
Shubham Kumar, Narendra Ahuja
Deep vision models perform input-output computations that are hard to interpret. Concept-based explanation methods (CBEMs) increase interpretability by re-expressing parts of the m…
Efficiently Disentangling CLIP for Multi-Object Perception
Samyak Rawlekar, Yujun Cai, Yiwei Wang +2
Vision-language models like CLIP excel at recognizing the single, prominent object in a scene. However, they struggle in complex scenes containing multiple objects. We identify a f…
R2I-rPPG: A Robust Region of Interest Selection Method for Remote Photoplethysmography to Extract Heart Rate
Sandeep Nagar, Mark Hasegawa-Johnson, David G. Beiser +1
The COVID-19 pandemic has underscored the need for low-cost, scalable approaches to measuring contactless vital signs, either during initial triage at a healthcare facility or virt…
Self-Calibrating 4D Novel View Synthesis from Monocular Videos Using Gaussian Splatting
Fang Li, Hao Zhang, Narendra Ahuja
Gaussian Splatting (GS) has significantly elevated scene reconstruction efficiency and novel view synthesis (NVS) accuracy compared to Neural Radiance Fields (NeRF), particularly f…
S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video
Hao Zhang, Fang Li, Samyak Rawlekar +1
Reconstructing dynamic articulated objects from a singular monocular video is challenging, requiring joint estimation of shape, motion, and camera parameters from limited views. Cu…
MagicPose4D: Crafting Articulated Models with Appearance and Motion Control
Hao Zhang, Di Chang, Fang Li +2
With the success of 2D and 3D visual generative models, there is growing interest in generating 4D content. Existing methods primarily rely on text prompts to produce 4D content, b…
Learning Implicit Representation for Reconstructing Articulated Objects
Hao Zhang, Fang Li, Samyak Rawlekar +1
3D Reconstruction of moving articulated objects without additional information about object structure is a challenging problem. Current methods overcome such challenges by employin…
CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen
Hao Zhang, Fang Li, Lu Qi +2
Addressing Out-Of-Distribution (OOD) Segmentation and Zero-Shot Semantic Segmentation (ZS3) is challenging, necessitating segmenting unseen classes. Existing strategies adapt the c…
Open-NeRF: Towards Open Vocabulary NeRF Decomposition
Hao Zhang, Fang Li, Narendra Ahuja
In this paper, we address the challenge of decomposing Neural Radiance Fields (NeRF) into objects from an open vocabulary, a critical task for object manipulation in 3D reconstruct…