SkeleGuide: 基于显式骨骼推理的上下文感知人体图像合成
SkeleGuide: Explicit Skeleton Reasoning for Context-Aware Human-in-Place Image Synthesis
AI总结：
SkeleGuide通过显式骨骼推理提升上下文感知的人体图像合成质量，提供高保真且结构合理的生成结果。
摘要：
生成逼真且结构合理的真人图像以融入现有场景仍是对当前生成模型构成重大挑战，这些模型往往会产生如扭曲的四肢和不自然姿势等伪影。我们将这种系统性失败归因于无法对人类骨骼结构进行显式推理。为解决这一问题，我们引入了SkeleGuide，这是一种基于显式骨骼推理的新框架。通过联合训练其推理和渲染阶段，SkeleGuide学会生成一个内部姿态，作为强大的结构先验，引导合成朝着高结构完整性方向发展。为了实现细粒度用户控制，我们引入了PoseInverter模块，该模块将此内部潜在姿态解码为显式且可编辑的格式。大量实验表明，SkeleGuide在生成高保真且上下文感知的人体图像方面显著优于专用和通用模型。我们的工作提供了有力证据，表明显式建模骨骼结构是实现稳健且合理的人体图像合成的关键步骤。
英文摘要：
Generating realistic and structurally plausible human images into existing scenes remains a significant challenge for current generative models, which often produce artifacts like distorted limbs and unnatural poses. We attribute this systemic failure to an inability to perform explicit reasoning over human skeletal structure. To address this, we introduce SkeleGuide, a novel framework built upon explicit skeletal reasoning. Through joint training of its reasoning and rendering stages, SkeleGuide learns to produce an internal pose that acts as a strong structural prior, guiding the synthesis towards high structural integrity. For fine-grained user control, we introduce PoseInverter, a module that decodes this internal latent pose into an explicit and editable format. Extensive experiments demonstrate that SkeleGuide significantly outperforms both specialized and general-purpose models in generating high-fidelity, contextually-aware human images. Our work provides compelling evidence that explicitly modeling skeletal structure is a fundamental step towards robust and plausible human image synthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
