Generative Rendering and Beyond

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious manual process, which can be automated by emerging text-to-video diffusion models (SORA). Despite great promise, video diffusion models are difficult to control, hindering users from applying their own creativity rather than amplifying it. In this talk, we present a novel approach called Generative Rendering that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. Our approach takes an animated, low-fidelity rendered mesh as input and injects the ground truth correspondence information obtained from the dynamic mesh into various stages of a pre-trained text-to-image generation model to output high-quality and temporally consistent frames. Going beyond, we will discuss the various challenges and goals towards achieving controllability in video diffusion models, and conclude with a preview of our ongoing consensus video generation efforts.