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.
By Agniv Chatterjee, Georgios Pavlakos
Reconstructing dynamic and interactive 3D scenes from real-world observations remains a fundamental challenge in computer vision and robotics. While recent advances in 3D Gaussian Splatting have enabled high-fidelity static reconstruction, extending it to interactive environments with articulated robots and manipulable objects remains difficult due to complex contact interactions and abrupt pose changes.
Grasp in Gaussians (GraG) is a fast, robust method for reconstructing dynamic 3D hand‑object interactions from a single monocular video. It leverages pretrained hand and object priors and represents the scene with a compact Sum‑of‑Gaussians (SoG) model, enabling efficient tracking while preserving geometric fidelity. Experiments show GraG achieves temporally coherent reconstructions on long sequences 4.4×–38.9× faster than prior work.
By Ayce Idil Aytekin, Xu Chen, Zhengyang Shen, Thabo Beeler, Helge Rhodin, Rishabh Dabral, Christian Theobalt
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.
By Minghan Zhu, Zhiyi Wang, Qihang Sun, Maani Ghaffari, Michael Posa
The key challenge in articulated 3D object generation from a single image is accurately predicting the underlying kinematic structure. Existing methods either infer kinematic parameters directly from a static image that lacks dynamic part-level kinematic relationships, or estimate parameters from visual dynamics generated from a single image, which is prone to accumulated errors of two steps.
arXiv:2606. 01950v1 Announce Type: cross Abstract: World models enable intelligent agents to predict the consequences of their actions on the environment.
By Jens U. Kreber, Lukas Mack, Joerg Stueckler
arXiv:2606. 28656v1 Announce Type: cross Abstract: Deformable 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for rendering dynamic scenes in a wide range of 3D applications.
By Ruitao Chen, Mozhang Guo, Jinge Li
arXiv:2606. 02000v1 Announce Type: cross Abstract: Diffusion models have shown remarkable success in video generation.
By Jingyun Liang, Min Wei, Shikai Li, Yizeng Han, Hangjie Yuan, Lei Sun, Weihua Chen, Fan Wang
arXiv:2605.29661v3 Announce Type: replace
Abstract: Monocular 3D shape recovery is fundamental to geometric understanding, yet achieving robust generalization across arbitrary viewpoints and unseen o...
By Yiyao Ma, Kai Chen, Zhongxiang Zhou, Zhuheng Song, Dongsheng Xie, Zelong Tan, Rong Xiong, Qi Dou
Driven by the availability of large-scale datasets, Human Pose Estimation (HPE) plays a critical role in numerous downstream tasks. However, mainstream benchmarks exhibit severe representation bias, predominantly featuring able-bodied individuals.
Reconstructing humans and their surrounding environments in a globally consistent 4D space is essential for comprehensive perception. However, prior works typically assume single-view inputs or decouple humans, scenes, and cameras, making them unable to recover coherent geometry, stable motion, and physically aligned trajectories.
arXiv:2605. 18010v2 Announce Type: replace Abstract: Acquisition and creation of 3D assets have been largely view- or appearance-driven.
By Mingrui Zhao, Sai Raj Kishore Perla, Kai Wang, Sauradip Nag, Duc Anh Nguyen, Jiayi Peng, Ruiqi Wang, Angel X. Chang, Manolis Savva, Ali Mahdavi-Amiri, Hao Zhang