SPL4SH: Designing a Systematic Pipeline for 4D Synthetic Humans in XR Content Creation

Abstract

XR content creation increasingly combines reconstruction, generative modeling, animation synthesis, neural rendering, and real-time engine deployment. However, creating deployable synthetic 4D humans remains fragmented because human assets must preserve body structure, appearance, motion, deformation, and scene-level plausibility across time. This paper proposes SPL4SH, a Systematic Pipeline for 4D Synthetic Humans, to investigate how contemporary AI-assisted techniques can be organized into an XR-oriented production workflow. SPL4SH is built from a stage-based taxonomy covering modeling, rigging, animation, rendering, and interaction. This taxonomy supports a comparative suitability assessment of recent techniques and guides a modular proof-of-concept implementation integrating SMPLify for parametric body modeling and rigging, Kimodo for controllable motion generation, SMPLitex for SMPL-compatible texture generation, Rokoko-based retargeting, Blender-based asset integration, and Unreal Engine deployment. The evaluation combines individual module tests, full-pipeline integration, and scene-level deployment in a meeting-room environment involving human-object, human-human, and human-scene arrangements. Results show that modular 4D human generation is technically feasible: SMPL-X geometry supports rigged deformation, generated motions can be retargeted to animated characters, texture maps improve mannequin-like bodies, and final assets can be placed in real-time XR scenes. However, persistent barriers remain, including format mismatch, manual skeleton alignment, retargeting fragility, texture discontinuities, hallucinated visual regions, motion interpenetration, stiff transitions, and scene-level validation. SPL4SH contributes a grounded framework for selecting, combining, and evaluating independent generative components as XR-ready synthetic human assets.