AHAP: Reconstructing Arbitrary Humans from Arbitrary Perspectives with Geometric Priors

Multi-view human reconstruction has been extensively studied under simplified settings, yet robust and efficient multi-person reconstruction in unconstrained environments remains challenging. Existing bottom-up methods often rely on accurate camera calibration and explicit cross-view matching, and therefore struggle with severe occlusions and ambiguities. We propose a new top-down paradigm that maintains a unified, instance-centric human-aware 3D space, enabling simultaneous camera calibration, cross-view association, and human reconstruction via cross-modal contrastive learning. Observations from multiple views are lifted and fused into this shared 3D space, where geometric structure, visual appearance, and human-centric semantic cues are jointly encoded at the instance level. We further introduce a spatial contrastive learning strategy that aligns 3D features corresponding to the same human instance across different views and modalities while separating different instances. This enables correspondence reasoning, semantic aggregation, and instance discrimination to be performed natively in 3D, improving cross-view consistency and robustness under severe occlusions. Finally, structured human body models are recovered in a feed-forward manner by regressing SMPL parameters from instance-level 3D human tokens. Extensive experiments demonstrate robust, accurate, and efficient multi-view human reconstruction in challenging real-world scenarios.

Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independently trained human and scene priors often produces misalignment in scale and depth. We observe that the two priors have complementary strengths. The scene prior provides consistent depth but approximate scale, while the human prior provides fixed body scale but less reliable depth. Motivated by this observation, we present HARMONI, a feed-forward framework that reconstructs cameras, scene, and humans with identities from monocular or multi-view video without test-time optimization. We introduce bidirectional anchoring, in which scene depth guides human placement, while human keypoints calibrate the scene scale to match the human body. While most existing approaches target monocular inputs and multi-view methods rely on optimization or re-identification, our approach naturally extends to multiple views. Building on bidirectional anchoring, we introduce a multi-view fusion module that merges per-view estimates, while reducing the influence of unreliable views and visually similar individuals. Experiments show that our framework outperforms previous human-scene methods in global motion and multi-view pose estimation by up to 28% and 64%, while running 28 times faster than optimization-based approaches. Project page: https://nstar1125.github.io/harmoni.

Recovering 3D human pose from multi-view imagery typically relies on precise camera calibration, which is often unavailable in real-world scenarios, thereby severely limiting the applicability of existing methods. To overcome this challenge, we propose an unconstrained framework that synergizes deep neural networks, algebraic priors, and temporal dynamics for uncalibrated multi-view human pose estimation. First, we introduce the Triangulation with Transformer Regressor (TTR), which reformulates classical triangulation into a data-driven token fusion process to bypass the dependency on explicit camera parameters. Second, to explicitly embed the inherent algebraic relations of the multi-view variety into the learning process, we propose the Gröbner basis Corrector (GC). This pioneering loss formulation enforces constraints derived from the multi-view variety to ensure the neural predictions strictly adhere to the laws of projective geometry. Finally, we devise the Temporal Equivariant Rectifier (TER), which exploits the equivariance property of human motion to impose temporal coherence and structural consistency, effectively mitigating scale ambiguity in uncalibrated settings. Extensive evaluations on standard benchmarks demonstrate that our framework establishes a new state-of-the-art for uncalibrated multi-view human pose estimation. Notably, our approach significantly closes the performance gap between calibration-free methods and fully calibrated oracles.