PhotoHOI: Synthesizing 3D Hand-Object Interactions from a Single RGB Photograph

• URL: http://arxiv.org/abs/2608.01905v2
• Date: Thu, 06 Aug 2026 13:24:49 GMT
• Title: PhotoHOI: Synthesizing 3D Hand-Object Interactions from a Single RGB Photograph
• Abstract summary: PhotoHOI synthesizing 3D hand-object interaction sequences from a single RGB photograph and an open-vocabulary language instruction.<n>PhotoHOI first uses a vision-language model to parse the input image and instruction into a structured task specification.<n>It then recovers a compact task-relevant 3D scene and plans a smooth collision-aware object trajectory.
• Score: 45.09993780203758
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hand-object interaction (HOI) is a fundamental human behavior with broad applications in AR/VR, digital humans, and embodied interaction. Existing methods typically require predefined object geometry, object trajectories, or task-specific conditions, limiting their use with natural real-world inputs. To address this, we study a more practical problem of synthesizing 3D hand-object interaction sequences from a single RGB photograph and an open-vocabulary language instruction, and introduce PhotoHOI. PhotoHOI first uses a vision-language model to parse the input image and instruction into a structured task specification, including the interaction object, target region, and spatial relation. It then recovers a compact task-relevant 3D scene and plans a smooth collision-aware object trajectory based on the recovered object states, support relations, and surrounding scene geometry. To synthesize hand motion that generalizes to real-world photographs and unseen objects, it learns transferable task-conditioned contact and contact-conditioned grasp priors from large-scale affordance and HOI data. The grasp is further refined in a learned latent space, constraining the optimization to a plausible hand-pose manifold. Experiments on GRAB and H2O demonstrate improved contact quality and reduced penetration over representative baselines. Results on real-world photographs further demonstrate higher task success and scene consistency, together with generalization to unseen objects and open-vocabulary instructions.

Related papers

• GIRAF: Towards Generalizable Human Interactions with Articulated Objects [50.52795413484619]
  We present a text-conditioned diffusion model that addresses the challenge of Synthesizing realistic full-body human interactions with articulated objects.<n>Our method demonstrated strong generalization to unseen object configurations, surpassing current state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-07-08T19:30:44Z)
• Generalized Hand-Object Pose Estimation with Occlusion Awareness [87.10073091219066]
  Generalized 3D hand-object pose estimation from a single RGB image remains challenging due to the large variations in object appearances and interaction patterns.<n>We propose GenHOI, a framework for generalized hand-object pose estimation with occlusion awareness.
  arXiv  Detail & Related papers  (2026-03-19T15:19:23Z)
• InteractAnything: Zero-shot Human Object Interaction Synthesis via LLM Feedback and Object Affordance Parsing [36.29681929804816]
  We propose a novel zero-shot 3D HOI generation framework without training on specific datasets.<n>We use a pre-trained 2D image diffusion model to parse unseen objects and extract contact points.<n>We then introduce a detailed optimization to generate fine-grained, precise, and natural interaction, enforcing realistic 3D contact between the 3D object and the involved body parts.
  arXiv  Detail & Related papers  (2025-05-30T07:53:55Z)
• SIGHT: Synthesizing Image-Text Conditioned and Geometry-Guided 3D Hand-Object Trajectories [124.24041272390954]
  Modeling hand-object interaction priors holds significant potential to advance robotic and embodied AI systems.<n>We introduce SIGHT, a novel task focused on generating realistic and physically plausible 3D hand-object interaction trajectories from a single image.<n>We propose SIGHT-Fusion, a novel diffusion-based image-text conditioned generative model that tackles this task by retrieving the most similar 3D object mesh from a database.
  arXiv  Detail & Related papers  (2025-03-28T20:53:20Z)
• HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video [70.11702620562889]
  HOLD -- the first category-agnostic method that reconstructs an articulated hand and object jointly from a monocular interaction video. We develop a compositional articulated implicit model that can disentangled 3D hand and object from 2D images. Our method does not rely on 3D hand-object annotations while outperforming fully-supervised baselines in both in-the-lab and challenging in-the-wild settings.
  arXiv  Detail & Related papers  (2023-11-30T10:50:35Z)
• Full-Body Articulated Human-Object Interaction [61.01135739641217]
  CHAIRS is a large-scale motion-captured f-AHOI dataset consisting of 16.2 hours of versatile interactions. CHAIRS provides 3D meshes of both humans and articulated objects during the entire interactive process. By learning the geometrical relationships in HOI, we devise the very first model that leverage human pose estimation.
  arXiv  Detail & Related papers  (2022-12-20T19:50:54Z)
• Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors [42.17542596399014]
  We present a method for inferring diverse 3D models of human-object interactions from images. Our method extracts high-level commonsense knowledge from large language models. We quantitatively evaluate the inferred 3D models on a large human-object interaction dataset.
  arXiv  Detail & Related papers  (2022-09-06T13:32:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.