PersonaCraft: Personalized and Controllable Full-Body Multi-Human Scene Generation Using Occlusion-Aware 3D-Conditioned Diffusion
Gwanghyun Kim, Suh Yoon Jeon, Seunggyu Lee, Se Young Chun

TL;DR
PersonaCraft is a novel framework that combines diffusion models with 3D human modeling to generate personalized, multi-human scenes with robust occlusion handling and full-body control.
Contribution
It introduces a 3D-aware pose conditioning method, occlusion-focused training, and a flexible full-body personalization approach, advancing beyond prior facial-only or 2D pose-based methods.
Findings
Outperforms existing methods in image quality and personalization accuracy
Demonstrates robust occlusion handling in complex scenes
Enables flexible full-body customization
Abstract
We present PersonaCraft, a framework for controllable and occlusion-robust full-body personalized image synthesis of multiple individuals in complex scenes. Current methods struggle with occlusion-heavy scenarios and complete body personalization, as 2D pose conditioning lacks 3D geometry, often leading to ambiguous occlusions and anatomical distortions, and many approaches focus solely on facial identity. In contrast, our PersonaCraft integrates diffusion models with 3D human modeling, employing SMPLx-ControlNet, to utilize 3D geometry like depth and normal maps for robust 3D-aware pose conditioning and enhanced anatomical coherence. To handle fine-grained occlusions, we propose Occlusion Boundary Enhancer Network that exploits depth edge signals with occlusion-focused training, and Occlusion-Aware Classifier-Free Guidance strategy that selectively reinforces conditioning in occluded…
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Taxonomy
TopicsPersona Design and Applications
MethodsDiffusion
