AdaViewPlanner: Adapting Video Diffusion Models for Viewpoint Planning in 4D Scenes

2025-10-12

AI Summary

This work addresses the challenge of natural viewpoint prediction in 4D dynamic scenes—a long-standing problem in scene understanding and interactive perception. We propose the first adaptation of pre-trained text-to-video (T2V) diffusion models for camera trajectory planning, introducing a two-stage fine-tuning paradigm: (1) an adaptive learning branch aligns video generation priors with 4D geometric representations; and (2) a camera extrinsic-parameter denoising diffusion mechanism jointly optimizes pose parameters under visual-geometric constraints. By integrating video generation capabilities with 4D scene encoding, our method enables generative viewpoint reasoning. On standard 4D viewpoint prediction benchmarks, it significantly outperforms prior approaches. Ablation studies validate the efficacy of both stages—particularly the dual-stage injection strategy and extrinsic-parameter diffusion modeling. This work establishes a novel pathway for leveraging video diffusion models to enhance 4D interactive scene understanding.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Recent Text-to-Video (T2V) models have demonstrated powerful capability in visual simulation of real-world geometry and physical laws, indicating its potential as implicit world models. Inspired by this, we explore the feasibility of leveraging the video generation prior for viewpoint planning from given 4D scenes, since videos internally accompany dynamic scenes with natural viewpoints. To this end, we propose a two-stage paradigm to adapt pre-trained T2V models for viewpoint prediction, in a compatible manner. First, we inject the 4D scene representation into the pre-trained T2V model via an adaptive learning branch, where the 4D scene is viewpoint-agnostic and the conditional generated video embeds the viewpoints visually. Then, we formulate viewpoint extraction as a hybrid-condition guided camera extrinsic denoising process. Specifically, a camera extrinsic diffusion branch is further introduced onto the pre-trained T2V model, by taking the generated video and 4D scene as input. Experimental results show the superiority of our proposed method over existing competitors, and ablation studies validate the effectiveness of our key technical designs. To some extent, this work proves the potential of video generation models toward 4D interaction in real world.

Problem

• Adapting video diffusion models for 4D scene viewpoint planning
• Leveraging video generation prior for viewpoint prediction tasks
• Extracting camera viewpoints through hybrid-condition guided denoising

Innovation

• Adapts video diffusion models for viewpoint planning
• Injects 4D scenes via adaptive learning branch
• Uses hybrid-condition guided camera extrinsic denoising