arXiv AI
Jul 2
Interact3D: Compositional 3D Generation of Interactive Objects
arXiv:2603. 16085v2 Announce Type: replace-cross Abstract: Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets.
Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints
The paper presents a method for reconstructing object geometry under occlusion by combining generative shape priors with contact-based constraints. Generative models provide plausible guesses for unseen parts, while contact information from videos or physical interactions supplies sparse boundary constraints. The authors integrate these cues through a contact-guided 3D generation framework, demonstrating improved reconstruction on synthetic and real-world datasets compared to baseline approaches.
Related stories
arXiv AI
Jun 2
Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling
arXiv:2602. 08058v3 Announce Type: replace-cross Abstract: In the presence of occlusions and measurement noise, geometrically accurate scene reconstructions -- which fit the sensor data -- can still be physically incorrect.
arXiv Computer Vision
6d ago
Reconstructing Humans and Objects in Interaction using Large Reconstruction Models
The paper introduces MILO, a framework that uses Large Reconstruction Models (LRMs) to reconstruct detailed 3D human‑object interactions from a single image. By treating the LRM mesh as a geometric scaffold, MILO segments it into human and object parts, fits a parametric body model to the human component, and optionally aligns an object template to the object component. The approach achieves higher reconstruction accuracy than existing baselines across multiple benchmarks and interaction scenarios.
arXiv Computer Vision
Aug 25
SpatialDiff: 3D-Aware Object Movement via Implicit Spatial Modeling
arXiv:2608.21776v1 Announce Type: new Abstract: Recent advances in image editing allow impressive manipulation of objects, existing methods still struggle to handle spatial movement in complex scenes...
arXiv AI
Jun 8
MatterDoor: Sampling Zero-shot Spatio-semantic Priors using Generative Models
arXiv:2510. 11014v2 Announce Type: replace-cross Abstract: Autonomous robots often view rooms only partially, through a doorway, where the walls and scene structure hide the geometry and task-relevant semantics needed for safe navigation and goal-directed action.
Hugging Face Trending Papers
Jul 29
ContactFlow: A video action conditioning that transfers across embodiments
World models offer a promising route toward robot planning by enabling agents to imagine and verify the consequences of actions before execution. However, current video-based world models often struggle to capture the physical constraints that govern manipulation, particularly contact.
Hugging Face Trending Papers
Jul 29
StructureGS: Structure-aware Gaussian Splatting for Articulated Object Reconstruction
Reconstructing articulated objects with multiple movable parts is essential for understanding object structure and enabling physical interaction. However, this reconstruction task poses significant challenges due to the entanglement of geometry, appearance, and motion parameters during optimization.
arXiv AI
Jun 24
NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation
arXiv:2606. 24450v1 Announce Type: cross Abstract: Perceiving physical contact is fundamental to dexterous manipulation.
arXiv Machine Learning
Aug 12
Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)
arXiv:2509. 06191v2 Announce Type: replace-cross Abstract: Recent 3D generative models, which are capable of generating full object shapes from just a few images, now open up new opportunities in robotics.
arXiv AI
Jun 9
EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets
arXiv:2606. 08057v1 Announce Type: cross Abstract: Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or require pre-scanned object assets.
arXiv Computer Vision
3d ago
GraspHOI: Full-Body 3D Human-Object Reconstruction with Finger-Level Grasps from a Single In-the-Wild Image
arXiv:2608.28386v1 Announce Type: new Abstract: Existing monocular full-body 3D human-object interaction (HOI) methods do not combine explicit finger-level grasp optimization with category-agnostic o...
arXiv AI
Jun 30
Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning
arXiv:2606. 28813v1 Announce Type: cross Abstract: Human videos are a scalable source of supervision for robot manipulation, as they are abundant and naturally capture rich object interactions.