311 citations · 979 across the 36 of their papers we have counts for
36 papers
CreativeVR: Diffusion-Prior-Guided Approach for Structure and Motion Restoration in Generative and Real Videos
Tejas Panambur, Ishan Rajendrakumar Dave, Chongjian Ge +2
Modern text-to-video (T2V) diffusion models can synthesize visually compelling clips, yet they remain brittle at fine-scale structure: even state-of-the-art generators often produc…
SpotEdit: Evaluating Visually-Guided Image Editing Methods
Sara Ghazanfari, Wei-An Lin, Haitong Tian +1
Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content genera…
Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
Min Bai, Shenlong Wang, Kelvin Wong +2
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV). Our re…
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Ze Yang, Shenlong Wang, Sivabalan Manivasagam +5
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.…
Diverse Complexity Measures for Dataset Curation in Self-driving
Abbas Sadat, Sean Segal, Sergio Casas +4
Modern self-driving autonomy systems heavily rely on deep learning. As a consequence, their performance is influenced significantly by the quality and richness of the training data…
Deep Structured Reactive Planning
Jerry Liu, Wenyuan Zeng, Raquel Urtasun +1
An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the…
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
James Tu, Huichen Li, Xinchen Yan +6
Modern self-driving perception systems have been shown to improve upon processing complementary inputs such as LiDAR with images. In isolation, 2D images have been found to be extr…
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Yun Chen, Frieda Rong, Shivam Duggal +6
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current works in image simulation either fail to be photor…
Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs
Sean Segal, Eric Kee, Wenjie Luo +3
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling effici…
Hierarchical Verification for Adversarial Robustness
Cong Han Lim, Raquel Urtasun, Ersin Yumer
We introduce a new framework for the exact point-wise robustness verification problem that exploits the layer-wise geometric structure of deep feed-forward networks with r…
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
Kibok Lee, Zhuoyuan Chen, Xinchen Yan +2
We introduce ShapeAdv, a novel framework to study shape-aware adversarial perturbations that reflect the underlying shape variations (e.g., geometric deformations and structural di…
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Yanran Guan, Han Liu, Kun Liu +8
We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D…
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
Abbas Sadat, Mengye Ren, Andrei Pokrovsky +3
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules. This is usually achieved by two modules: beh…
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
Mengtian Li, Ersin Yumer, Deva Ramanan
In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and mod…
Photo-Sketching: Inferring Contour Drawings from Images
Mengtian Li, Zhe Lin, Radomir Mech +2
Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision. On one hand, they are the 2D elements that convey 3D shapes, on the ot…
UPSNet: A Unified Panoptic Segmentation Network
Yuwen Xiong, Renjie Liao, Hengshuang Zhao +4
In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network,…
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics
Xinchen Yan, Akash Rastogi, Ruben Villegas +5
Long-term human motion can be represented as a series of motion modes---motion sequences that capture short-term temporal dynamics---with transitions between them. We leverage this…
Learning Blind Video Temporal Consistency
Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang +3
Applying image processing algorithms independently to each frame of a video often leads to undesired inconsistent results over time. Developing temporally consistent video-based ex…
iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos
Aron Monszpart, Paul Guerrero, Duygu Ceylan +2
A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video. While the problem remains a subj…
Self-supervised Multi-view Person Association and Its Applications
Minh Vo, Ersin Yumer, Kalyan Sunkavalli +3
Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and…
PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image
Chen Liu, Jimei Yang, Duygu Ceylan +2
This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image d…
BodyNet: Volumetric Inference of 3D Human Body Shapes
Gül Varol, Duygu Ceylan, Bryan Russell +4
Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging du…
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing
Chen-Hsuan Lin, Ersin Yumer, Oliver Wang +2
We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image. To achieve this, we p…
Self-supervised Learning of Motion Capture
Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer +1
Current state-of-the-art solutions for motion capture from a single camera are optimization driven: they optimize the parameters of a 3D human model so that its re-projection match…