Results 11 to 20 of about 641,442 (338)
LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding [PDF]
Computer Vision and Pattern Recognition, 2023 Humans excel at acquiring knowledge through observation. For example, we can learn to use new tools by watching demonstrations. This skill is fundamental for intelligent systems to interact with the world.
Gen Li +3 more
semanticscholar +1 more source
Affordance Diffusion: Synthesizing Hand-Object Interactions [PDF]
Computer Vision and Pattern Recognition, 2023 Recent successes in image synthesis are powered by large-scale diffusion models. However, most methods are currently limited to either text- or image-conditioned generation for synthesizing an entire image, texture transfer or inserting objects into a ...
Yufei Ye +7 more
semanticscholar +1 more source
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose [PDF]
IEEE International Conference on Computer Vision, 2023 How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction.
Juntao Jian +4 more
semanticscholar +1 more source
One-Shot Open Affordance Learning with Foundation Models [PDF]
Computer Vision and Pattern Recognition, 2023 We introduce One-shot Open Affordance Learning (OOAL), where a model is trained with just one example per base object category, but is expected to identify novel objects and affordances. While vision-language models excel at recognizing novel objects and
Gen Li +3 more
semanticscholar +1 more source
Grounding 3D Object Affordance from 2D Interactions in Images [PDF]
IEEE International Conference on Computer Vision, 2023 Grounding 3D object affordance seeks to locate objects’ "action possibilities" regions in the 3D space, which serves as a link between perception and operation for embodied agents.
Yuhang Yang +5 more
semanticscholar +1 more source
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects [PDF]
Neural Information Processing Systems, 2023 Articulated object manipulation is a fundamental yet challenging task in robotics. Due to significant geometric and semantic variations across object categories, previous manipulation models struggle to generalize to novel categories.
Chuanruo Ning +4 more
semanticscholar +1 more source
Learning Affordance Grounding from Exocentric Images [PDF]
Computer Vision and Pattern Recognition, 2022 Affordance grounding, a task to ground (i.e., localize) action possibility region in objects, which faces the challenge of establishing an explicit link with object parts due to the diversity of interactive affordance.
Hongcheng Luo +4 more
semanticscholar +1 more source
Open-Vocabulary Affordance Detection in 3D Point Clouds [PDF]
IEEE/RJS International Conference on Intelligent RObots and Systems, 2023 Affordance detection is a challenging problem with a wide variety of robotic applications. Traditional affordance detection methods are limited to a predefined set of affordance labels, hence potentially restricting the adaptability of intelligent robots
Toan Ngyen +6 more
semanticscholar +1 more source
Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations [PDF]
Robotics: Science and Systems, 2021 Grasp detection in clutter requires the robot to reason about the 3D scene from incomplete and noisy perception. In this work, we draw insight that 3D reconstruction and grasp learning are two intimately connected tasks, both of which require a fine ...
Zhenyu Jiang +4 more
semanticscholar +1 more source
Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation [PDF]
IEEE International Conference on Computer Vision, 2023 Understanding and manipulating deformable objects (e.g., ropes and fabrics) is an essential yet challenging task with broad applications. Difficulties come from complex states and dynamics, diverse configurations and high-dimensional action space of ...
Ruihai Wu, Chuanruo Ning, Hao Dong
semanticscholar +1 more source