AI-Assisted Multiview Cartoon Character Modeling: Physics-Consistent Inverse Rendering for Dynamic-Backlight Light-Field 3D Display

Three-dimensional display is fundamentally a problem of reconstructing view-dependent optical rays rather than presenting planar images. Multilayer light field display provides an effective panel-based route for 3D scene reconstruction, but it remains constrained by two coupled bottlenecks: additional attenuation layers can improve angular reconstruction while reducing transmitted light, and higher spatio-angular density increases the cost of inverse synthesis. This study proposes a dynamic-backlight-enabled multilayer framework in which a pixel-addressable emission plane participates in light-field synthesis, thereby replacing one purely passive illumination stage with a controllable optical degree of freedom. The emitted rays of conventional- and dynamic-backlight configurations are represented by a unified non-negative tensor model, and layer synthesis is formulated as a weighted non-negative tensor factorization (NTF) problem. A physics-consistent convolutional neural network is further used to predict display-layer patterns through the same optical forward model and is extended from 5 × 5 to 10 × 10 viewpoint inputs. The quantitative evidence reported in this paper comprises NTF runtime and test-set PSNR, whereas the prototype photographs provide qualitative verification of viewpoint reconstruction. Because the conventional and dynamic prototypes use different LCD and OLED panels that were not luminance- or color-matched, their apparent brightness and contrast differences are not interpreted as calibrated measurements of architectural gain. Within this evidence boundary, the results support proof-of-concept feasibility of jointly designing the emission plane, attenuation layers, and inverse solver, rather than complete validation of a practical electronically addressable multilayer display.