Reconstructing 3D humans from images captured at multiple perspectives typically requires pre-calibration, like using checkerboards or MVS algorithms, which limits scalability and applicability in diverse real-world scenarios. In this work, we present AHAP (Reconstructing Arbitrary Humans from Arbitrary Perspectives), a feed-forward framework for reconstructing arbitrary humans from arbitrary camera perspectives without requiring camera calibration. Our core lies in the effective fusion of multi-view geometry to assist human association, reconstruction and localization. Specifically, we use a Cross-View Identity Association module through learnable person queries and soft assignment, supervised by contrastive learning to resolve cross-view human identity association. A Human Head fuses cross-view features and scene context for SMPL prediction, guided by cross-view reprojection losses to enforce body pose consistency. Additionally, multi-view geometry eliminates the depth ambiguity inherent in monocular methods, providing more precise 3D human localization through multi-view triangulation. Experiments on EgoHumans and EgoExo4D demonstrate that AHAP achieves competitive performance on both world-space human reconstruction and camera pose estimation, while being 180$\times$ faster than optimization-based approaches.
Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction
University of Bologna（博洛尼亚大学）；Peking University（北京大学）；The Hong Kong Polytechnic University（香港理工大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Huawei（华为）；University of Trento（特伦托大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Monash University（莫纳什大学）；Google DeepMind（谷歌DeepMind）；University of California, Merced（加州大学默塞德分校）
Perspective-aware fusion of incomplete depth maps and surface normals for accurate 3D reconstruction
Austrian Institute of Technology（奥地利技术研究所）；Center for Vision, Automation and Control（视觉、自动化与控制中心）；Competence unit High-Performance Vision System（高性能视觉系统能力单元）
MTFusion: Reconstructing Any 3D Object from Single Image Using Multi-word Textual Inversion
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University（四川大学合成视觉基础科学国家重点实验室）；College of Computer Science, Sichuan University（四川大学计算机学院）
You Need a Transition Plane: Bridging Continuous Panoramic 3D Reconstruction with Perspective Gaussian Splatting
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Visual Intelligence +X International Cooperation Joint Laboratory of MOE（教育部视觉智能+X国际合作联合实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；Max Planck ETH Center for Learning Systems（马克斯·普朗克-ETH学习系统中心）；Microsoft（微软）；ETH Zürich（苏黎世联邦理工学院）；KU Leuven（鲁汶大学）
NeuroMamba: Multi-Perspective Feature Interaction with Visual Mamba for Neuron Segmentation
State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences（脑认知与脑启发智能技术国家重点实验室，自动化研究所）
Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Nanjing University of Science and Technology（南京理工大学）；Huazhong University of Science and Technology（华中科技大学）；Chinese Academy of Sciences（中国科学院）；Northwestern Polytechnical University（西北工业大学）
