112 citations · 304 across the 149 of their papers we have counts for
22 papers · 2 filters
Autoregressive Flow Matching for Motion Prediction
Johnathan Xie, Stefan Stojanov, Cristobal Eyzaguirre +2
Motion prediction has been studied in different contexts with models trained on narrow distributions and applied to downstream tasks in human motion prediction and robotics. Simult…
VLIC: Vision-Language Models As Perceptual Judges for Human-Aligned Image Compression
Kyle Sargent, Ruiqi Gao, Philipp Henzler +5
Evaluations of image compression performance which include human preferences have generally found that naive distortion functions such as MSE are insufficiently aligned to human pe…
WonderZoom: Multi-Scale 3D World Generation
Jin Cao, Hong-Xing Yu, Jiajun Wu
We present WonderZoom, a novel approach to generating 3D scenes with contents across multiple spatial scales from a single image. Existing 3D world generation models remain limited…
A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding
Christina Liu, Alan Q. Wang, Joy Hsu +2
Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools. Broadly, these frameworks lev…
ART: Articulated Reconstruction Transformer
Zizhang Li, Cheng Zhang, Zhengqin Li +7
We introduce ART, Articulated Reconstruction Transformer -- a category-agnostic, feed-forward model that reconstructs complete 3D articulated objects from only sparse, multi-state…
Seeing the Wind from a Falling Leaf
Zhiyuan Gao, Jiageng Mao, Hong-Xing Yu +5
A longstanding goal in computer vision is to model motions from videos, while the representations behind motions, i.e. the invisible physical interactions that cause objects to def…
Web-Scale Collection of Video Data for 4D Animal Reconstruction
Brian Nlong Zhao, Jiajun Wu, Shangzhe Wu
Computer vision for animals holds great promise for wildlife research but often depends on large-scale data, while existing collection methods rely on controlled capture setups. Re…
Coupled Diffusion Sampling for Training-Free Multi-View Image Editing
Hadi Alzayer, Yunzhi Zhang, Chen Geng +2
We present an inference-time diffusion sampling method to perform multi-view consistent image editing using pre-trained 2D image editing models. These models can independently prod…
Ctrl-VI: Controllable Video Synthesis via Variational Inference
Haoyi Duan, Yunzhi Zhang, Yilun Du +1
Many video workflows benefit from a mixture of user controls with varying granularity, from exact 4D object trajectories and camera paths to coarse text prompts, while existing vid…
From Programs to Poses: Factored Real-World Scene Generation via Learned Program Libraries
Joy Hsu, Emily Jin, Jiajun Wu +1
Real-world scenes, such as those in ScanNet, are difficult to capture, with highly limited data available. Generating realistic scenes with varied object poses remains an open and…
Weakly-Supervised Learning of Dense Functional Correspondences
Stefan Stojanov, Linan Zhao, Yunzhi Zhang +2
Establishing dense correspondences across image pairs is essential for tasks such as shape reconstruction and robot manipulation. In the challenging setting of matching across diff…
Explain Before You Answer: A Survey on Compositional Visual Reasoning
Fucai Ke, Joy Hsu, Zhixi Cai +10
Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground inte…
LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit
Chengtao Lv, Bilang Zhang, Yang Yong +7
Large Vision-Language Models (VLMs) exhibit impressive multi-modal capabilities but suffer from prohibitive computational and memory demands, due to their long visual token sequenc…
Taming generative video models for zero-shot optical flow extraction
Seungwoo Kim, Khai Loong Aw, Klemen Kotar +8
Extracting optical flow from videos remains a core computer vision problem. Motivated by the recent success of large general-purpose models, we ask whether frozen self-supervised v…
Product of Experts for Visual Generation
Yunzhi Zhang, Carson Murtuza-Lanier, Zizhang Li +2
Modern neural models capture rich priors and have complementary knowledge over shared data domains, e.g., images and videos. Integrating diverse knowledge from multiple sources --…
Category-Agnostic Neural Object Rigging
Guangzhao He, Chen Geng, Shangzhe Wu +1
The motion of deformable 4D objects lies in a low-dimensional manifold. To better capture the low dimensionality and enable better controllability, traditional methods have devised…
Understanding Complexity in VideoQA via Visual Program Generation
Cristobal Eyzaguirre, Igor Vasiljevic, Achal Dave +5
We propose a data-driven approach to analyzing query complexity in Video Question Answering (VideoQA). Previous efforts in benchmark design have relied on human expertise to design…
X-Capture: An Open-Source Portable Device for Multi-Sensory Learning
Samuel Clarke, Suzannah Wistreich, Yanjie Ze +1
Understanding objects through multiple sensory modalities is fundamental to human perception, enabling cross-sensory integration and richer comprehension. For AI and robotic system…
T*: Re-thinking Temporal Search for Long-Form Video Understanding
Jinhui Ye, Zihan Wang, Haosen Sun +9
Efficiently understanding long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms for long-form video understanding…
Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals
Stefan Stojanov, David Wendt, Seungwoo Kim +4
Estimating motion in videos is an essential computer vision problem with many downstream applications, including controllable video generation and robotics. Current solutions are p…
FluidNexus: 3D Fluid Reconstruction and Prediction from a Single Video
Yue Gao, Hong-Xing Yu, Bo Zhu +1
We study reconstructing and predicting 3D fluid appearance and velocity from a single video. Current methods require multi-view videos for fluid reconstruction. We present FluidNex…
Predicate Hierarchies Improve Few-Shot State Classification
Emily Jin, Joy Hsu, Jiajun Wu
State classification of objects and their relations is core to many long-horizon tasks, particularly in robot planning and manipulation. However, the combinatorial explosion of pos…