The field of 3D modeling and reconstruction is witnessing significant advancements, with a focus on developing innovative methods for learning human perspective, reconstructing articulated objects, and editing 3D models. Researchers are exploring new approaches to capture the nuances of human-drawn perspectives, enabling more realistic and engaging computer graphics applications. Furthermore, there is a growing interest in reconstructing complex scenes involving multiple objects and intricate interactions, with a emphasis on efficiency and accuracy.

Noteworthy papers in this area include:

• A method for learning human perspective from single sketches, which overcomes the lack of suitable large-scale data by learning from a single artist sketch and a best matching analytical camera view.
• A cross-category approach for reconstructing multiple man-made articulated objects from a single RGBD image, which effectively handles instances with diverse part structures and various part counts.
• A framework for compositional 3D scene/object reconstruction from a single image, which achieves a significant speedup over existing methods while setting new benchmarks in performance.
• A disentangled reconstruction pipeline for realistic decal blending, which simulates stickers attached to the reconstructed surface and allows for instant editing and real-time rendering.