TwiFF (Think With Future Frames): A Large-Scale Dataset for Dynamic Visual Reasoning

2026-02-11

AI Summary

Existing visual chain-of-thought methods struggle with temporal reasoning in dynamic scenes. To address this challenge, this work proposes the TwiFF framework and introduces TwiFF-2.7M, the first large-scale temporally aligned dataset for dynamic visual chain-of-thought reasoning. TwiFF integrates pretrained video generation and image understanding capabilities, enabling a synergistic vision-language temporal reasoning mechanism by iteratively generating future action frames while simultaneously performing textual reasoning. Experimental results demonstrate that TwiFF significantly outperforms both existing visual and purely text-based chain-of-thought approaches on dynamic visual question answering tasks, thereby validating its effectiveness and state-of-the-art performance.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• 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

Visual Chain-of-Thought (VCoT) has emerged as a promising paradigm for enhancing multimodal reasoning by integrating visual perception into intermediate reasoning steps. However, existing VCoT approaches are largely confined to static scenarios and struggle to capture the temporal dynamics essential for tasks such as instruction, prediction, and camera motion. To bridge this gap, we propose TwiFF-2.7M, the first large-scale, temporally grounded VCoT dataset derived from $2.7$ million video clips, explicitly designed for dynamic visual question and answer. Accompanying this, we introduce TwiFF-Bench, a high-quality evaluation benchmark of $1,078$ samples that assesses both the plausibility of reasoning trajectories and the correctness of final answers in open-ended dynamic settings. Building on these foundations, we propose the TwiFF model, a unified modal that synergistically leverages pre-trained video generation and image comprehension capabilities to produce temporally coherent visual reasoning cues-iteratively generating future action frames and textual reasoning. Extensive experiments demonstrate that TwiFF significantly outperforms existing VCoT methods and Textual Chain-of-Thought baselines on dynamic reasoning tasks, which fully validates the effectiveness for visual question answering in dynamic scenarios. Our code and data is available at https://github.com/LiuJunhua02/TwiFF.

Problem

• Visual Chain-of-Thought
• dynamic visual reasoning
• temporal dynamics
• video question answering
• multimodal reasoning

Innovation

• Dynamic Visual Reasoning
• Visual Chain-of-Thought
• Temporal Grounding
• Video Generation
• Multimodal Reasoning