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Found 12 papers, 7 papers with code
Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance
Despite significant advancements in text-to-motion synthesis, generating language-guided human motion within 3D environments poses substantial challenges.
AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents
Traditional approaches in physics-based motion generation, centered around imitation learning and reward shaping, often struggle to adapt to new scenarios.
Scaling Up Dynamic Human-Scene Interaction Modeling
Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling, we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method.
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding
In comparison to recent advancements in the 2D domain, grounding language in 3D scenes faces several significant challenges: (i) the inherent complexity of 3D scenes due to the diverse object configurations, their rich attributes, and intricate relationships; (ii) the scarcity of paired 3D vision-language data to support grounded learning; and (iii) the absence of a unified learning framework to distill knowledge from grounded 3D data.
Feedback RoI Features Improve Aerial Object Detection
Neuroscience studies have shown that the human visual system utilizes high-level feedback information to guide lower-level perception, enabling adaptation to signals of different characteristics.
Grasp Multiple Objects with One Hand
The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation.
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution.
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning.
Full-Body Articulated Human-Object Interaction
By learning the geometrical relationships in HOI, we devise the very first model that leverage human pose estimation to tackle the estimation of articulated object poses and shapes during whole-body interactions.
HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes
Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes remains challenging due to the mediocre characteristics of the existing datasets on Human-Scene Interaction (HSI); they only have limited scale/quality and lack semantics.
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology.
GenDexGrasp: Generalizable Dexterous Grasping
By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands.
