← Back to all news
arXiv Computer Vision By Yuanwang Yang, Buzhen Huang, Zongxuan Ren, Jing Huang, Kun Li

Feed-Forward Multi-view Multi-person Reconstruction with Contrastive Human-Aware 3D Representation

Read the original on arXiv Computer Vision →

The paper introduces a top‑down approach for multi‑person 3D reconstruction from multiple views, using a unified, instance‑centric human‑aware 3D space. Observations from different cameras are lifted into this shared space where geometry, appearance, and semantic cues are jointly encoded, and a spatial contrastive learning strategy aligns features of the same person across views while separating different individuals. The method then regresses SMPL parameters from 3D tokens in a feed‑forward manner, achieving robust, accurate, and efficient reconstruction even under severe occlusions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Aug 28

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.

By Agniv Chatterjee, Georgios Pavlakos
computer-visionroboticsbenchmarks
More like this →