Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models

• URL: http://arxiv.org/abs/2312.01409v1
• Date: Sun, 3 Dec 2023 14:17:11 GMT
• Title: Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models
• Authors: Shengqu Cai and Duygu Ceylan and Matheus Gadelha and Chun-Hao Paul Huang and Tuanfeng Yang Wang and Gordon Wetzstein
• Abstract summary: We present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path.
• Score: 40.71940056121056
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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. Despite great promise, video diffusion models are difficult to control, hindering a user to apply their own creativity rather than amplifying it. To address this challenge, we present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. For this purpose, 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. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path.

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