Reconstructing metrically accurate humans and their surrounding scenes from a single image is crucial for virtual reality, robotics, and comprehensive 3D scene understanding. However, existing methods struggle with depth ambiguity, occlusions, and physically inconsistent contacts. To address these challenges, we introduce PhySIC, a unified framework for physically plausible Human–Scene Interaction and Contact reconstruction. PhySIC recovers metrically consistent SMPL-X human meshes, dense scene surfaces, and vertex-level contact maps within a shared coordinate frame, all from a single RGB image. Starting from coarse monocular depth and parametric body estimates, PhySIC performs occlusion-aware inpainting, fuses visible depth with unscaled geometry for a robust initial metric scene scaffold, and synthesizes missing support surfaces like floors. A confidence-weighted optimization subsequently refines body pose, camera parameters, and global scale by jointly enforcing depth alignment, contact priors, interpenetration avoidance, and 2D reprojection consistency. Explicit occlusion masking safeguards invisible body regions against implausible configurations. PhySIC is highly efficient, requiring only 9 seconds for a joint human-scene optimization and less than 27 seconds for end-to-end reconstruction process. Moreover, the framework naturally handles multiple humans, enabling reconstruction of diverse human scene interactions. Empirically, PhySIC substantially outperforms single-image baselines, reducing mean per-vertex scene error from 641 mm to 227 mm, halving the pose-aligned mean per-joint position error (PA-MPJPE) to 42 mm, and improving contact F1-score from 0.09 to 0.51. Qualitative results demonstrate that PhySIC yields realistic foot-floor interactions, natural seating postures, and plausible reconstructions of heavily occluded furniture. By converting a single image into a physically plausible 3D human-scene pair, PhySIC advances accessible and scalable 3D scene understanding. Our implementation is publicly available at https://yuxuan-xue.com/physic.

Generating realistic and controllable 3D human avatars is a long-standing challenge. The difficulty increases when covering a broad range of attributes such as ethnicity, age, clothing styles, and detailed body shapes. Capturing and annotating large-scale human datasets for training generative models is prohibitively expensive and limited in both scale and diversity. The central question we address in this paper is: Can we distill existing foundation models to generate theoretically unbounded richly annotated 3D human data? We introduce InfiniHuman, a novel framework to distill these models synergistically, to generate richly annotated human data with minimal cost and theoretically unlimited scalability. Specifically, we propose InfiniHumanData, a fully automatic pipeline that leverages vision-language and image generation models to create a large-scale multi-modal dataset. Remarkably, users cannot distinguish our automatically generated identities from scan renderings. InfiniHumanData contains 111K identities and covers unprecedented diversity in ethnicity, age, clothing styles, and more. Each identity is annotated with multi-granularity text descriptions, multi-view RGB images, detailed clothing images, and SMPL body shape parameters. Based on this, we learn InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body shape, and clothing assets. InfiniHumanGen enables fast, realistic, and precisely controllable avatar generation. Extensive experiments demonstrate that InfiniHuman significantly surpasses existing state-of-the-art methods in terms of visual quality, generation speed, and controllability. Importantly, our approach democratizes high-quality avatar generation with fine-grained control at infinite scale through a practical and affordable solution. To facilitate future research, we will publicly release our automatic data generation pipeline and the comprehensive dataset InfiniHumanData, and the generative models InfiniHumanGen. The code and data of InfiniHuman is publicly available at https://yuxuan-xue.com/infini-human.