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ARXIV:2604.05436 · 3D RECONSTRUCTION · SUBMITTED 08 APR · 03:22 UTC · FRESHNESS UNKNOWN
ARXIV:2604.054363D RECONSTRUCTIONSUBMITTED 08 APR · 03:22 UTCFRESHNESS UNKNOWNGwanghyun Kim · Junghun James Kim · Suh Yoon Jeon · Jason Park · Se Young Chun · arXiv
A holistic method for reconstructing textured 3D human models from a single image, explicitly modeling group-level context and interaction priors to resolve occlusions and generate physically plausible, high-fidelity reconstructions of…
Opportunity summary
Pain A holistic method for reconstructing textured 3D human models from a single image, explicitly modeling group-level context and interaction priors to resolve occlusions and generate physically plausible, high-fidelity reconstructions of interacting people.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A holistic method for reconstructing textured 3D human models from a single image, explicitly modeling group-level context and interaction priors to resolve occlusions and generate physically plausible, high-fidelity reconstructions of interacting people. However, existing…
Reconstructing textured 3D human models from a single image is fundamental for AR/VR and digital human applications. However, existing methods mostly focus on single individuals and thus fail in multi-human scenes, where naive composition…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments show that HUG3D significantly outperforms both single-human and existing multi-human methods, producing physically plausible, high-fidelity 3D reconstructions of interacting people from a…
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A holistic method for reconstructing textured 3D human models from a single image, explicitly modeling group-level context and interaction priors to resolve occlusions and generate physically plausible, high-fidelity reconstructions of…
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10.48550/arXiv.2604.05436A holistic method for reconstructing textured 3D human models from a single image, explicitly modeling group-level context and interaction priors to resolve occlusions and generate physically plausible, high-fidelity reconstructions of interacting people.
Abstract
Reconstructing textured 3D human models from a single image is fundamental for AR/VR and digital human applications. However, existing methods mostly focus on single individuals and thus fail in multi-human scenes, where naive composition of individual reconstructions often leads to artifacts such as unrealistic overlaps, missing geometry in occluded regions, and distorted interactions. These limitations highlight the need for approaches that incorporate group-level context and interaction priors. We introduce a holistic method that explicitly models both group- and instance-level information. To mitigate perspective-induced geometric distortions, we first transform the input into a canonical orthographic space. Our primary component, Human Group-Instance Multi-View Diffusion (HUG-MVD), then generates complete multi-view normals and images by jointly modeling individuals and group context to resolve occlusions and proximity. Subsequently, the Human Group-Instance Geometric Reconstruction (HUG-GR) module optimizes the geometry by leveraging explicit, physics-based interaction priors to enforce physical plausibility and accurately model inter-human contact. Finally, the multi-view images are fused into a high-fidelity texture. Together, these components form our complete framework, HUG3D. Extensive experiments show that HUG3D significantly outperforms both single-human and existing multi-human methods, producing physically plausible, high-fidelity 3D reconstructions of interacting people from a single image. Project page: https://jongheean11.github.io/HUG3D_project
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Dimensions overall score 7.0
PROBLEM
A holistic method for reconstructing textured 3D human models from a single image, explicitly modeling group-level context and interaction priors to resolve occlusions and generate physically plausible, high-fidelity reconstructions of interacting people. However, existing metho...
METHOD
Reconstructing textured 3D human models from a single image is fundamental for AR/VR and digital human applications. However, existing methods mostly focus on single individuals and thus fail in multi-human scenes, where naive composition of individual reconstructions often lead...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments show that HUG3D significantly outperforms both single-human and existing multi-human methods, producing physically plausible, high-fidelity 3D reconstructions of interacting people f...
WHY NOW
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A holistic method for reconstructing textured 3D human models from a single image, explicitly modeling group-level context and interaction priors to resolve occlusions and generate physically plausible, high-fidelity reconstructions of interacting people. However, existing methods mostly focus on single individuals and thus fail in multi-human scenes, where naive composition of individual reconstructions often leads to artifacts such as unrealistic overlaps, missing geometry in occluded regions, and distorted interactions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reconstructing textured 3D human models from a single image is fundamental for AR/VR and digital human applications. However, existing methods mostly focus on single individuals and thus fail in multi-human scenes, where naive composition of individual reconstructions often leads to artifacts such as unrealistic overlaps, missing geometry in occluded regions, and distorted interactions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments show that HUG3D significantly outperforms both single-human and existing multi-human methods, producing physically plausible, high-fidelity 3D reconstructions of interacting people from a single image. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A holistic method for reconstructing textured 3D human models from a single image, explicitly modeling group-level context and interaction priors to resolve occlusions and generate physically plausible, high-fidelity reconstructions of interacting people.
Segment
3D Reconstruction
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Commercial read
7.0/10 public viability
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Technical feasibility
partial
Current read
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Run minimal reproduction from the Build Passport prototype path.
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Defensibility
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Current read
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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People
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People
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