Geometry-as-context: Modulating Explicit 3D in Scene-consistent Video Generation to Geometry Context
JiaKui Hu, Jialun Liu, Liying Yang, Xinliang Zhang, Kaiwen Li, Shuang Zeng, Yuanwei Li, Haibin Huang, Chi Zhang, Yanye Lu

TL;DR
This paper introduces a novel 'geometry-as-context' framework for scene-consistent video generation that iteratively estimates geometry and generates views, improving scene consistency and camera control over previous methods.
Contribution
It proposes an autoregressive model with a camera gated attention module that integrates geometry estimation and view synthesis, reducing errors from separate models and non-differentiable processes.
Findings
Outperforms previous methods in scene consistency.
Effectively maintains camera control during video generation.
Demonstrates robustness on various camera trajectories.
Abstract
Scene-consistent video generation aims to create videos that explore 3D scenes based on a camera trajectory. Previous methods rely on video generation models with external memory for consistency, or iterative 3D reconstruction and inpainting, which accumulate errors during inference due to incorrect intermediary outputs, non-differentiable processes, and separate models. To overcome these limitations, we introduce ``geometry-as-context". It iteratively completes the following steps using an autoregressive camera-controlled video generation model: (1) estimates the geometry of the current view necessary for 3D reconstruction, and (2) simulates and restores novel view images rendered by the 3D scene. Under this multi-task framework, we develop the camera gated attention module to enhance the model's capability to effectively leverage camera poses. During the training phase, text contexts…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · 3D Shape Modeling and Analysis · Advanced Vision and Imaging
