一张照片，一个3D「你」：计算所等提出HumanLift，实现高保真数字人重建

Core Insights - The article discusses the development of a new technology called HumanLift, which enables the reconstruction of high-quality, realistic 3D digital humans from a single reference image, addressing challenges in 3D consistency and detail accuracy [2][4][25]. Part 1: Background - Traditional methods for single-image digital human reconstruction are categorized into explicit and implicit approaches, each with limitations in handling complex clothing and achieving realistic textures [8]. - Recent advancements in generative models and neural implicit rendering have improved the connection between 2D images and 3D space, yet challenges remain in high-fidelity 3D human modeling due to data scarcity and complexity in human poses and clothing [8][9]. Part 2: Algorithm Principles - HumanLift aims to create a 3D digital representation that captures realistic appearance and fine details from a single image, utilizing a two-stage process [11]. - The first stage generates realistic multi-view images from a single photo using a 3D-aware multi-view human generation method, incorporating a backbone network based on a video generation model [13][14]. - The second stage reconstructs the 3D representation using the generated multi-view images, optimizing parameters based on Gaussian mesh representation [15][17]. Part 3: Effectiveness Demonstration - HumanLift demonstrates its capability by generating multi-view RGB and normal images from real-world photographs, achieving photo-realistic results and maintaining spatial consistency [20]. - Ablation studies confirm the importance of facial enhancement and SMPL-X pose optimization in improving detail quality and rendering accuracy [21][22][23]. Part 4: Conclusion - The development of HumanLift represents a significant advancement in single-image full-body digital human reconstruction, overcoming traditional limitations and providing a user-friendly solution for high-quality 3D modeling [25].