VIDEORNG: RELIGHTABLE NEURAL 3D ASSET EXTRACTION FROM GENERATIVE VIDEO MODELS

Generating realistic 3D assets remains constrained by classical representations: surface meshes and compact physically based materials struggle to express objects whose appearance arises from fine geometry or complex light transport, while existing Gaussian-splatting generators typically bake fixed illumination into appearance. We introduce VideoRNG, a two-stage method that generates complete, relightable 3D assets from a text prompt or a single image without prescribing a mesh or analytic material model. A video generator fine-tuned from a pretrained video diffusion backbone first produces a structured RGBA turntable sequence under a known pose, camera, and illumination protocol. A feed-forward large reconstruction model initialized from a pretrained video diffusion backbone then maps the video-VAE latent frames of selected turntable frames directly to millions of anisotropic Gaussians with learned neural appearance features; all of its parameters are fine-tuned for reconstruction. A shared directional decoder and stochastic Gaussian ray tracer turn this representation, which we call ray-traced Relightable Neural Gaussians (rtRNG), into radiance under novel viewpoints and illumination. To preserve high-frequency appearance while learning geometry, we introduce depth-anchored appearance training, and we make million-Gaussian reconstruction practical through foreground token selection and cost-aware distributed sampling. VideoRNG generates complex assets such as fur, feathers, and spider webs that can be interactively rendered, relit, and integrated into scenes. Our anonymous results page: https://videorng.github.io/