Geometry-as-context: Modulating Explicit 3D in Scene-consistent Video Generation to Geometry Context

2026-02-25

AI Summary

Existing methods for scene-consistent video generation often suffer from error accumulation due to reliance on external memory, non-differentiable operations, or decoupled multi-model architectures, leading to degraded consistency. This work proposes a “Geometry-as-Context” framework that integrates geometric information as dynamic context within an autoregressive video generation process, enabling end-to-end training through alternating estimation of current-view geometry and rendering of novel views. Key innovations include a camera-gated attention mechanism to enhance pose awareness, interleaved training of geometry and RGB sequences, and stochastic dropping of geometric context during training to support pure RGB inference. Experiments demonstrate that the proposed method significantly outperforms existing approaches under both unidirectional and round-trip camera trajectories, achieving substantial improvements in scene consistency and camera control accuracy.

Technology Category

• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 are utilized to ascertain whether geometric or RGB images should be generated. To ensure that the model can generate RGB-only outputs during inference, the geometry context is randomly dropped from the interleaved text-image-geometry training sequence. The method has been tested on scene video generation with one-direction and forth-and-back trajectories. The results show its superiority over previous approaches in maintaining scene consistency and camera control.

Problem

• scene-consistent video generation
• 3D reconstruction
• camera trajectory
• geometry context
• video generation

Innovation

• geometry-as-context
• scene-consistent video generation
• camera-controlled generation
• autoregressive 3D video model
• camera gated attention