arXiv Computer Vision
DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation
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HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstruction
arXiv:2603.12789v3 Announce Type: replace Abstract: Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independ...
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PAOLI: Pose-free Articulated Object Learning from Sparse-view Images
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Multiview Multi-Person Human Mesh Recovery Under Large Scenes with Occlusions
Human mesh recovery (HMR) aims to recover 3D human meshes from images. Most existing HMR benchmarks and methods focus on either multi-person reconstruction from a single view or single-person reconstruction from multiple views, where the number of subjects and the scene scale are relatively limited.
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Accurate and Efficient Object Pose Estimation via the Aggregation of Diffusion Features
The paper introduces a new approach to 3D object pose estimation that leverages diffusion model features, such as those from Stable Diffusion, to improve performance on unseen objects. Three aggregation architectures—vanilla, nonlinear, and context‑aware—are proposed, with a confidence‑adaptive aggregation network that selectively uses discriminative features to balance speed and accuracy. The method achieves state‑of‑the‑art accuracy on unseen object benchmarks (97.7% vs. 93.5% on Unseen LM) and real‑time performance (68 FPS) while reducing computational cost.
Hugging Face Trending Papers
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TROPHIES: Temporal Reconstruction of Places, Humans, and Cameras from Multi-view Videos
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.