Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis

• URL: http://arxiv.org/abs/2506.23263v1
• Date: Sun, 29 Jun 2025 14:37:48 GMT
• Title: Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis
• Authors: Lei-lei Li, Jianwu Fang, Junbin Xiao, Shanmin Pang, Hongkai Yu, Chen Lv, Jianru Xue, Tat-Seng Chua,
• Abstract summary: Egocentricly comprehending the causes and effects of car accidents is crucial for the safety of self-driving cars.<n>This work argues that precisely identifying the accident participants and capturing their related behaviors are of critical importance.<n>We propose a novel diffusion model, Causal-VidSyn, for synthesizing egocentric traffic accident videos.
• Score: 78.14763828578904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Egocentricly comprehending the causes and effects of car accidents is crucial for the safety of self-driving cars, and synthesizing causal-entity reflected accident videos can facilitate the capability test to respond to unaffordable accidents in reality. However, incorporating causal relations as seen in real-world videos into synthetic videos remains challenging. This work argues that precisely identifying the accident participants and capturing their related behaviors are of critical importance. In this regard, we propose a novel diffusion model, Causal-VidSyn, for synthesizing egocentric traffic accident videos. To enable causal entity grounding in video diffusion, Causal-VidSyn leverages the cause descriptions and driver fixations to identify the accident participants and behaviors, facilitated by accident reason answering and gaze-conditioned selection modules. To support Causal-VidSyn, we further construct Drive-Gaze, the largest driver gaze dataset (with 1.54M frames of fixations) in driving accident scenarios. Extensive experiments show that Causal-VidSyn surpasses state-of-the-art video diffusion models in terms of frame quality and causal sensitivity in various tasks, including accident video editing, normal-to-accident video diffusion, and text-to-video generation.

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